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William Meeker Q Statistical Methods for - maxdoors.ru
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William P Gardiner Statistical Analysis Methods for Chemists

William Meeker Q. Statistical Methods for Reliability Data


Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Statistical Methods for Reliability Data was among those chosen. Bringing statistical methods for reliability testing in line with the computer age This volume presents state-of-the-art, computer-based statistical methods for reliability data analysis and test planning for industrial products. Statistical Methods for Reliability Data updates and improves established techniques as it demonstrates how to apply the new graphical, numerical, or simulation-based methods to a broad range of models encountered in reliability data analysis. It includes methods for planning reliability studies and analyzing degradation data, simulation methods used to complement large-sample asymptotic theory, general likelihood-based methods of handling arbitrarily censored data and truncated data, and more. In this book, engineers and statisticians in industry and academia will find: A wealth of information and procedures developed to give products a competitive edge Simple examples of data analysis computed with the S-PLUS system-for which a suite of functions and commands is available over the Internet End-of-chapter, real-data exercise sets Hundreds of computer graphics illustrating data, results of analyses, and technical concepts An essential resource for practitioners involved in product reliability and design decisions, Statistical Methods for Reliability Data is also an excellent textbook for on-the-job training courses, and for university courses on applied reliability data analysis at the graduate level. An Instructor's Manual presenting detailed solutions to all the problems in the book is available upon requestfrom the Wiley editorial department.

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Группа авторов Statistical Models and Methods for Reliability and Survival Analysis


Statistical Models and Methods for Reliability and Survival Analysis brings together contributions by specialists in statistical theory as they discuss their applications providing up-to-date developments in methods used in survival analysis, statistical goodness of fit, stochastic processes for system reliability, amongst others. Many of these are related to the work of Professor M. Nikulin in statistics over the past 30 years. The authors gather together various contributions with a broad array of techniques and results, divided into three parts – Statistical Models and Methods, Statistical Models and Methods in Survival Analysis, and Reliability and Maintenance. The book is intended for researchers interested in statistical methodology and models useful in survival analysis, system reliability and statistical testing for censored and non-censored data.

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Rand R. Wilcox Understanding and Applying Basic Statistical Methods Using R


Features a straightforward and concise resource for introductory statistical concepts, methods, and techniques using R Understanding and Applying Basic Statistical Methods Using R uniquely bridges the gap between advances in the statistical literature and methods routinely used by non-statisticians. Providing a conceptual basis for understanding the relative merits and applications of these methods, the book features modern insights and advances relevant to basic techniques in terms of dealing with non-normality, outliers, heteroscedasticity (unequal variances), and curvature. Featuring a guide to R, the book uses R programming to explore introductory statistical concepts and standard methods for dealing with known problems associated with classic techniques. Thoroughly class-room tested, the book includes sections that focus on either R programming or computational details to help the reader become acquainted with basic concepts and principles essential in terms of understanding and applying the many methods currently available. Covering relevant material from a wide range of disciplines, Understanding and Applying Basic Statistical Methods Using R also includes: Numerous illustrations and exercises that use data to demonstrate the practical importance of multiple perspectives Discussions on common mistakes such as eliminating outliers and applying standard methods based on means using the remaining data Detailed coverage on R programming with descriptions on how to apply both classic and more modern methods using R A companion website with the data and solutions to all of the exercises Understanding and Applying Basic Statistical Methods Using R is an ideal textbook for an undergraduate and graduate-level statistics courses in the science and/or social science departments. The book can also serve as a reference for professional statisticians and other practitioners looking to better understand modern statistical methods as well as R programming. Rand R. Wilcox, PhD, is Professor in the Department of Psychology at the University of Southern California, Fellow of the Association for Psychological Science, and an associate editor for four statistics journals. He is also a member of the International Statistical Institute. The author of more than 320 articles published in a variety of statistical journals, he is also the author eleven other books on statistics. Dr. Wilcox is creator of WRS (Wilcox’ Robust Statistics), which is an R package for performing robust statistical methods. His main research interest includes statistical methods, particularly robust methods for comparing groups and studying associations.

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Reinhard Viertl Statistical Methods for Fuzzy Data


Statistical data are not always precise numbers, or vectors, or categories. Real data are frequently what is called fuzzy. Examples where this fuzziness is obvious are quality of life data, environmental, biological, medical, sociological and economics data. Also the results of measurements can be best described by using fuzzy numbers and fuzzy vectors respectively. Statistical analysis methods have to be adapted for the analysis of fuzzy data. In this book, the foundations of the description of fuzzy data are explained, including methods on how to obtain the characterizing function of fuzzy measurement results. Furthermore, statistical methods are then generalized to the analysis of fuzzy data and fuzzy a-priori information. Key Features: Provides basic methods for the mathematical description of fuzzy data, as well as statistical methods that can be used to analyze fuzzy data. Describes methods of increasing importance with applications in areas such as environmental statistics and social science. Complements the theory with exercises and solutions and is illustrated throughout with diagrams and examples. Explores areas such quantitative description of data uncertainty and mathematical description of fuzzy data. This work is aimed at statisticians working with fuzzy logic, engineering statisticians, finance researchers, and environmental statisticians. It is written for readers who are familiar with elementary stochastic models and basic statistical methods.

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Ingvar Eidhammer Computational and Statistical Methods for Protein Quantification by Mass Spectrometry


The definitive introduction to data analysis in quantitative proteomics This book provides all the necessary knowledge about mass spectrometry based proteomics methods and computational and statistical approaches to pursue the planning, design and analysis of quantitative proteomics experiments. The author’s carefully constructed approach allows readers to easily make the transition into the field of quantitative proteomics. Through detailed descriptions of wet-lab methods, computational approaches and statistical tools, this book covers the full scope of a quantitative experiment, allowing readers to acquire new knowledge as well as acting as a useful reference work for more advanced readers. Computational and Statistical Methods for Protein Quantification by Mass Spectrometry: Introduces the use of mass spectrometry in protein quantification and how the bioinformatics challenges in this field can be solved using statistical methods and various software programs. Is illustrated by a large number of figures and examples as well as numerous exercises. Provides both clear and rigorous descriptions of methods and approaches. Is thoroughly indexed and cross-referenced, combining the strengths of a text book with the utility of a reference work. Features detailed discussions of both wet-lab approaches and statistical and computational methods. With clear and thorough descriptions of the various methods and approaches, this book is accessible to biologists, informaticians, and statisticians alike and is aimed at readers across the academic spectrum, from advanced undergraduate students to post doctorates entering the field.

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Machin David Regression Methods for Medical Research


Regression Methods for Medical Research provides medical researchers with the skills they need to critically read and interpret research using more advanced statistical methods. The statistical requirements of interpreting and publishing in medical journals, together with rapid changes in science and technology, increasingly demands an understanding of more complex and sophisticated analytic procedures. The text explains the application of statistical models to a wide variety of practical medical investigative studies and clinical trials. Regression methods are used to appropriately answer the key design questions posed and in so doing take due account of any effects of potentially influencing co-variables. It begins with a revision of basic statistical concepts, followed by a gentle introduction to the principles of statistical modelling. The various methods of modelling are covered in a non-technical manner so that the principles can be more easily applied in everyday practice. A chapter contrasting regression modelling with a regression tree approach is included. The emphasis is on the understanding and the application of concepts and methods. Data drawn from published studies are used to exemplify statistical concepts throughout. Regression Methods for Medical Research is especially designed for clinicians, public health and environmental health professionals, para-medical research professionals, scientists, laboratory-based researchers and students.

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Jochen Voss An Introduction to Statistical Computing. A Simulation-based Approach


A comprehensive introduction to sampling-based methods in statistical computing The use of computers in mathematics and statistics has opened up a wide range of techniques for studying otherwise intractable problems. Sampling-based simulation techniques are now an invaluable tool for exploring statistical models. This book gives a comprehensive introduction to the exciting area of sampling-based methods. An Introduction to Statistical Computing introduces the classical topics of random number generation and Monte Carlo methods. It also includes some advanced methods such as the reversible jump Markov chain Monte Carlo algorithm and modern methods such as approximate Bayesian computation and multilevel Monte Carlo techniques An Introduction to Statistical Computing: Fully covers the traditional topics of statistical computing. Discusses both practical aspects and the theoretical background. Includes a chapter about continuous-time models. Illustrates all methods using examples and exercises. Provides answers to the exercises (using the statistical computing environment R); the corresponding source code is available online. Includes an introduction to programming in R. This book is mostly self-contained; the only prerequisites are basic knowledge of probability up to the law of large numbers. Careful presentation and examples make this book accessible to a wide range of students and suitable for self-study or as the basis of a taught course

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A. Gouveia Oliveira Biostatistics Decoded


Biostatistics Decoded  covered a large number of statistical methods that are mainly applied to clinical and epidemiological research, as well as a comprehensive discussion of study designs for observational research and clinical trials, two important concerns for the clinical researcher. In this second edition, new material is included covering statistical methods and study designs that are used to analyse research. Following the same methodology used in the first edition, the chapters are presented in two levels of detail, one for the reader who wishes only to understand the rationale behind each statistical method, and one for the reader who wishes to understand the computations Key features include: Extensive coverage of the design and analysis of experiments for basic science research Experimental designs are presented together with the statistical methods The rationale of all forms of ANOVA is explained with simple mathematics A comprehensive presentation of statistical tests for multiple comparisons Calculations for all statistical methods are illustrated with examples and explained step-by-step. This book presents biostatistical concepts and methods in a way that is accessible to anyone, regardless of his or her knowledge of mathematics. The topics selected for this book cover will meet the needs of clinical professionals to readers in basic science research.

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Ilya Prigogine Monte Carlo Methods in Chemical Physics


In Monte Carlo Methods in Chemical Physics: An Introduction to the Monte Carlo Method for Particle Simulations J. Ilja Siepmann Random Number Generators for Parallel Applications Ashok Srinivasan, David M. Ceperley and Michael Mascagni Between Classical and Quantum Monte Carlo Methods: «Variational» QMC Dario Bressanini and Peter J. Reynolds Monte Carlo Eigenvalue Methods in Quantum Mechanics and Statistical Mechanics M. P. Nightingale and C.J. Umrigar Adaptive Path-Integral Monte Carlo Methods for Accurate Computation of Molecular Thermodynamic Properties Robert Q. Topper Monte Carlo Sampling for Classical Trajectory Simulations Gilles H. Peslherbe Haobin Wang and William L. Hase Monte Carlo Approaches to the Protein Folding Problem Jeffrey Skolnick and Andrzej Kolinski Entropy Sampling Monte Carlo for Polypeptides and Proteins Harold A. Scheraga and Minh-Hong Hao Macrostate Dissection of Thermodynamic Monte Carlo Integrals Bruce W. Church, Alex Ulitsky, and David Shalloway Simulated Annealing-Optimal Histogram Methods David M. Ferguson and David G. Garrett Monte Carlo Methods for Polymeric Systems Juan J. de Pablo and Fernando A. Escobedo Thermodynamic-Scaling Methods in Monte Carlo and Their Application to Phase Equilibria John Valleau Semigrand Canonical Monte Carlo Simulation: Integration Along Coexistence Lines David A. Kofke Monte Carlo Methods for Simulating Phase Equilibria of Complex Fluids J. Ilja Siepmann Reactive Canonical Monte Carlo J. Karl Johnson New Monte Carlo Algorithms for Classical Spin Systems G. T. Barkema and M.E.J. Newman

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William Q Meeker | Department of Statistics

Recent Publications: • Meeker, W.Q., G.J Hahn, and L.A. Escobar, (2017), Statistical Intervals: A Guide for Practitioners and Researchers, Second Edition. John ...

Homepage for William Q. Meeker - Iowa State University

William Q. Meeker Professor of Statistics Distinguished Professor of Liberal Arts and Sciences Office information: Department of Statistics 2109 Snedecor Hall Iowa State University Ames, Iowa 50011-1210. Email: wqmeeker@iastate.edu . Phone number (with voice mail): (515)-294-5336 Secretary's (Denise Riker's) phone number: (515)-294-1076 Fax number: (515)-294-4040. Education: B.S., 1972 ...

William Q. Meeker | JMP

William Q. Meeker Professor of Statistics and Distinguished Professor, Iowa State University . Bill Meeker, PhD is a Professor of Statistics and Distinguished Professor of Liberal Arts and Sciences at Iowa State University. He is a Fellow of the American Statistical Association (ASA) and the American Society for Quality (ASQ) and a past Editor of Technometrics. He is co-author of the books ...

‪William Q. Meeker‬ - ‪Google Scholar‬

William Q. Meeker. Distinguished Professor of Statistics at Iowa State University. Verified email at iastate.edu - Homepage. Engineering statistics reliability statistical computing. Articles Cited by Co-authors. Title. Sort. Sort by citations Sort by year Sort by title. Cited by. Cited by. Year; Statistical methods for reliability data . WQ Meeker, LA Escobar. John Wiley & Sons, 2014. 4276 ...

William Q. Meeker - IEEE Xplore Author Details

William Q. Meeker is a Professor of Statistics and Distinguished Professor of Liberal Arts and Sciences at Iowa State University. He is co-author of the books Statistical Methods for Reliability Data with Luis Escobar (1998), Statistical Intervals: A Guide for Practitioners with Gerald Hahn (1991), nine book chapters, and of numerous publications in the engineering and statistical literature ...

William Q. Meeker: free download. Ebooks library. On-line ...

William Q. Meeker, Gerald J. Hahn, Luis A. Escobar. Year: 2017. Language: english. File: PDF, 4.78 MB × Create a new ZAlert. ZAlerts allow you to be notified by email about the availability of new books according to your search query. A search query can be a title of the book, a name of the author, ISBN or anything else. Read more about ZAlerts. Author / ISBN / Topic / Any search query ...

SelectedWorks - William Q Meeker - Bepress

William Q. Meeker, Dennis Roach and Seth S. Kessler There is much interest in the potential to use Structural Health Monitoring (SHM) technology to augment traditional Nondestructive Evaluation (NDE) ...

William C. Meeker: Kostenloses Herunterladen ...

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William Meeker

2109 Snedecor 2438 Osborn Dr. Ames, IA 50011-1090 wqmeeker@iastate.edu. Phone: 515-294-5336

William Q. Meeker - dblp.uni-trier.de

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Meeker, William Q. • Center for Nondestructive Evaluation ...

William Q. Meeker. Distinguished Professor, Statistics. Academic department profile. Education. B.S. Industrial Management (1972, Clarkson) M.S. Operations Research ...

Meeker, William Q. | onAcademic

Meeker, William Q. Hong, Yili Reliability field data such as that obtained from warranty claims and maintenance records have been used traditionally for such purposes as generating predictions for warranty costs and optimizing the cost of system operation and maintenance. In the current (and future) generation of many products, the nature of field reliability data is changing dramatically. In ...

Statistical methods for reliability data | William Q ...

William Q. Meeker, Luis A. Escobar. Amstat News asked three review editors to rate their top five favorite books in the September 2003 issue. Statistical Methods for Reliability Data was among those chosen.Bringing statistical methods for reliability testing in line with the computer age This volume presents state-of-the-art, computer-based statistical methods for reliability data analysis and ...

William Q. Meeker - Iowa State University

William Q. Meeker. William Q. Meeker. Distinguished Professor Statistics College of Liberal Arts & Sciences. Office: 2109 Snedecor Hall Phone: 515 294-5336 E-mail: wqmeeker@iastate.edu. RESEARCH INTERESTS. William Q. Meeker’s research interests include reliability data analysis, warranty analysis, reliability test planning, accelerated testing, nondestructive evaluation, and statistical ...

Statistical Methods for Reliability Data | William Q ...

William Q. Meeker, Luis A. Escobar. Explains computer-based statistical methods for reliability data analysis and test planning for industrial products. Demonstrates how to apply the latest graphical, numerical, and simulation-based methods to a broad range of models found in reliability data analysis, and covers areas such as analyzing degradation data, simulation methods used to complement ...

William Q ,Jr Meeker - Home

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"Exploring Reliability" mit Dr. Bill Meeker | JMP

William Q. Meeker ist Professor für Statistik und Distinguished Professor an der Iowa State University. In diesem Video spricht er über Wahrscheinlichkeitsdiagramme und Lebensdauerverteilung, mehrfahce Ausfallursachen (Fehlerarten), Lebensdauermodellierung mit erklärenden Variablen und beschleunigte Lebensdauertests.

wqmeeker (William Q Meeker) · GitHub

William Q Meeker wqmeeker. Follow. Block or report user Block or report wqmeeker. Block user. Prevent this user from interacting with your repositories and sending you notifications. Learn more about blocking users. Block user Report abuse. Contact GitHub support about this user’s behavior. Learn more about reporting abuse. Report abuse. 2 followers · 0 following · 0. Iowa State University ...

William Meeker - Historical records and family trees ...

Create a free family tree for yourself or for William Meeker and we’ll search for valuable new information for you. Get started West Virginia Death Index & Certificates, 1853-1964. William W. Meeker 1873 1908 William W. Meeker, 1873 - 1908. William W. Meeker was born on month day 1873, at birth place, to William Meeker and Mary Shelters. William married Devonia Vortis. William passed away on ...

William Q. Meeker (Author of Statistical Methods for ...

William Q. Meeker is the author of Statistical Methods for Reliability Data (3.67 avg rating, 9 ratings, 0 reviews, published 1998), Statistical Interval...

William Q Meeker, (515) 233-2046, Ames — Public Records ...

William Q Meeker Phone Numbers (515) 233-2046. Landline phone by Qwest Corp, two persons associated (515) 232-1323. Landline phone by Qwest Corp, three persons associated; Persons Associated with Address 5697 Arrasmith Trail. Roselma Hansen. Details (319) 330-3495 ...

William Q. Meeker

Follow William Q. Meeker and explore their bibliography from Amazon.com's William Q. Meeker Author Page.

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Amazon.com: Statistical Methods for Reliability Data ...

William Q. Meeker. 4.0 out of 5 stars 8. Hardcover. $103.05 Next page. More items to explore. Page 1 of 1 Start over Page 1 of 1 . Previous page. An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics) Gareth James. 4.7 out of 5 stars 1,001. Hardcover #1 Best Seller in Mathematical Physics. $45.90 R for Data Science: Import, Tidy, Transform, Visualize ...

Statistical Intervals (eBook, PDF) von William Q. Meeker ...

Presents a detailed exposition of statistical intervals and emphasizes applications in industry. The discussion differentiates at an elementary level among different kinds of statistical intervals and gives instruction with numerous examples and simple math on how to construct such intervals from sample data.

William Meeker Profiles | Facebook

View the profiles of people named William Meeker. Join Facebook to connect with William Meeker and others you may know. Facebook gives people the power...

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Author: William Q. Meeker ; Keyword: arrhenius relationship ; Search or Start over. Show search tips » Search tips. Exact phrase search: Use quotes, e.g. "integral equations" Wildcard search: Use asterisk, e.g. topo* Subject search: Truncate MSC codes with wildcard, e.g. 14A15 or 14A* Author search: Sequence does not matter; use of first name or initial varies by journal, e.g. harris john or ...

William Q. Meeker | Semantic Scholar

Semantic Scholar profile for William Q. Meeker, with 43 highly influential citations and 16 scientific research papers.

Statistical Methods for Reliability Data Wiley Series in ...

Statistical Methods for Reliability Data (Wiley Series in Probability and Statistics) | Meeker, William Q., Escobar, Luis A. | ISBN: 9780471143284 | Kostenloser Versand für alle Bücher mit Versand und Verkauf duch Amazon.

William Meeker | Center for Civilians in Conflict

William Meeker currently serves as CIVIC’s Africa Director where he oversees the organization’s country programs in sub-Saharan Africa. Prior to joining CIVIC, he served as Deputy Director with the U.S. Department of State’s Bureau of Conflict and Stabilization Operations where he oversaw analysis, planning, programs, and learning for conflict prevention, mitigation and response efforts ...

William Q. Meeker - Dialnet

William Q. Meeker, Georgios Sarakakis, Athanasios Gerokostopoulos Quality control and applied statistics , ISSN 0033-5207, Vol. 59, Nº. 4, 2014 , págs. 349-352 Field-failure predictions based on failure-time data with dynamic covariate information

Amazon.com: Statistical Intervals: A Guide for ...

William Q. Meeker, PhD, is Professor of Statistics and Distinguished Professor of Liberal Arts and Sciences at Iowa State University. He is a Fellow of the American Statistical Association and an elected member of the International Statistics Institute. Among his many awards and honors are the Youdan Prize and two Wilcoxon Prizes as well as two awards for outstanding teaching at Iowa State. He ...

Statistical Intervals: A Guide for Practitioners and ...

William Q. Meeker is Professor of Statistics and Distinguished Professor of Liberal Arts and Sciences at Iowa State University. He is co-author of Statistical Methods for Reliability Data (Wiley, 1998) and of numerous publications in the engineering and statistical literature and has won many awards for his research. Gerald J. Hahn served for 46 years as applied statistician and manager of an ...

William Meeker | Descendants of Founders of New Jersey

The progenitor of the New Jersey branch of the Meeker family was William Meeker, who came from Leamington, Warwickshire, England about 1635 to the Massachusetts Bay, and thence removed to New Haven colony, of which he was one of the founders. While residing there he married Sarah Preston, a native of Yorkshire, England.

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470 Independence Station Rd, Independence, KY is the residential address for William. Residents of 41051 pay approximately $1,070 a month for a 2-bedroom unit. We know that Barbara J Meeker, Barbara W Meeker, and five other persons also lived at this address, perhaps within a different time frame. (859) 371-1741 is the

Statistical Intervals (eBook, PDF) von Gerald J. Hahn ...

William Q. Meeker is Professor of Statistics and Distinguished Professor of Liberal Arts and Sciences at Iowa State University. He is co-author of Statistical Methods for Reliability Data (Wiley, 1998) and of numerous publications in the engineering and statistical literature and has won many awards for his research. Gerald J. Hahn served for 46 years as applied statistician and manager of an ...

Search Result

Author: William Q. Meeker ; Author: Duan, Yuanyuan ; Search or Start over. Show search tips » Search tips. Exact phrase search: Use quotes, e.g. "integral equations" Wildcard search: Use asterisk, e.g. topo* Subject search: Truncate MSC codes with wildcard, e.g. 14A15 or 14A* Author search: Sequence does not matter; use of first name or initial varies by journal, e.g. harris john or t arens ...

William MEEKER | Distinguished Professor | Iowa State ...

William MEEKER, Distinguished Professor | Cited by 10,527 | of Iowa State University, IA (ISU) | Read 312 publications | Contact William MEEKER

William Meeker in New York (NY) | 6 records found | Whitepages

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Bill Meeker - Historical records and family trees - MyHeritage

Henry William Meeker was born on month day 1941, at birth place, Pennsylvania, to Henry William Meeker. Henry passed away on month day 2008, at age 67 at death place, Pennsylvania. 4 of 5 records View all. 1940 United States Federal Census. Bill Meeker 1928 Oklahoma Bill Meeker, born Circa 1928. Bill Meeker was born circa 1928, at birth place, Oklahoma, to Carl C Meeker and Sarah Meeker. Bill ...

William R Meeker Profiles | Facebook

View the profiles of people named William R Meeker. Join Facebook to connect with William R Meeker and others you may know. Facebook gives people the...

Advances in Degradation Modeling | SpringerLink

This volume—dedicated to William Q. Meeker on the occasion of his sixtieth birthday—is a collection of invited chapters covering recent advances in accelerated life testing and degradation models. The book covers a wide range of applications to areas such as reliability, quality control, the health sciences, economics, and finance. Specific topics covered include: * Accelerated testing and ...

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Statistical Methods for Reliability Data von: William Q. Meeker, Luis A. Escobar Verlag:

Luis A. Escobar and William Q. Meeker

luis@lsu.edu. William Q. Meeker is Distinguished Professor, Department of Statistics, Iowa State University, Ames, Iowa 50011, USA e-mail: wqmeeker@iastate.edu. This is an electronic reprint of ...

CiteSeerX — Search Results — Combining Exact and ...

CiteSeerX - Scientific articles matching the query: Combining Exact and Metaheuristic Techniques for Learning Extended Finite-State Machines from Test Scenarios and Temporal Properties.

Chronological Bibliography of Science Fiction History ...

The following bibliography of science fiction criticism does not claim to be exhaustive. It does, however, gather together a large number of critical materials on sf that the edit

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Hengqing Tong Developing Econometrics


Statistical Theories and Methods with Applications to Economics and Business highlights recent advances in statistical theory and methods that benefit econometric practice. It deals with exploratory data analysis, a prerequisite to statistical modelling and part of data mining. It provides recently developed computational tools useful for data mining, analysing the reasons to do data mining and the best techniques to use in a given situation. Provides a detailed description of computer algorithms. Provides recently developed computational tools useful for data mining Highlights recent advances in statistical theory and methods that benefit econometric practice. Features examples with real life data. Accompanying software featuring DASC (Data Analysis and Statistical Computing). Essential reading for practitioners in any area of econometrics; business analysts involved in economics and management; and Graduate students and researchers in economics and statistics.

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Nigel Lewis DaCosta Operational Risk with Excel and VBA. Applied Statistical Methods for Risk Management, + Website


A valuable reference for understanding operational risk Operational Risk with Excel and VBA is a practical guide that only discusses statistical methods that have been shown to work in an operational risk management context. It brings together a wide variety of statistical methods and models that have proven their worth, and contains a concise treatment of the topic. This book provides readers with clear explanations, relevant information, and comprehensive examples of statistical methods for operational risk management in the real world. Nigel Da Costa Lewis (Stamford, CT) is president and CEO of StatMetrics, a quantitative research boutique. He received his PhD from Cambridge University.

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Xiao-Hua Zhou Statistical Methods in Diagnostic Medicine


An important role of diagnostic medicine research is to estimate and compare the accuracies of diagnostic tests. This book provides a comprehensive account of statistical methods for design and analysis of diagnostic studies, including sample size calculations, estimation of the accuracy of a diagnostic test, comparison of accuracies of competing diagnostic tests, and regression analysis of diagnostic accuracy data. Discussing recently developed methods for correction of verification bias and imperfect reference bias, methods for analysis of clustered diagnostic accuracy data, and meta-analysis methods, Statistical Methods in Diagnostic Medicine explains: * Common measures of diagnostic accuracy and designs for diagnostic accuracy studies * Methods of estimation and hypothesis testing of the accuracy of diagnostic tests * Meta-analysis * Advanced analytic techniques-including methods for comparing correlated ROC curves in multi-reader studies, correcting verification bias, and correcting when an imperfect gold standard is used Thoroughly detailed with numerous applications and end-of-chapter problems as well as a related FTP site providing FORTRAN program listings, data sets, and instructional hints, Statistical Methods in Diagnostic Medicine is a valuable addition to the literature of the field, serving as a much-needed guide for both clinicians and advanced students.

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Galit Shmueli Statistical Methods in e-Commerce Research


This groundbreaking book introduces the application of statistical methodologies to e-Commerce data With the expanding presence of technology in today's economic market, the use of the Internet for buying, selling, and investing is growing more popular and public in nature. Statistical Methods in e-Commerce Research is the first book of its kind to focus on the statistical models and methods that are essential in order to analyze information from electronic-commerce (e-Commerce) transactions, identify the challenges that arise with new e-Commerce data structures, and discover new knowledge about consumer activity. This collection gathers over thirty researchers and practitioners from the fields of statistics, computer science, information systems, and marketing to discuss the growing use of statistical methods in e-Commerce research. From privacy protection to economic impact, the book first identifies the many obstacles that are encountered while collecting, cleaning, exploring, and analyzing e-Commerce data. Solutions to these problems are then suggested using established and newly developed statistical and data mining methods. Finally, a look into the future of this evolving area of study is provided through an in-depth discussion of the emerging methods for conducting e-Commerce research. Statistical Methods in e-Commerce Research successfully bridges the gap between statistics and e-Commerce, introducing a statistical approach to solving challenges that arise in the context of online transactions, while also introducing a wide range of e-Commerce applications and problems where novel statistical methodology is warranted. It is an ideal text for courses on e-Commerce at the upper-undergraduate and graduate levels and also serves as a valuable reference for researchers and analysts across a wide array of subject areas, including economics, marketing, and information systems who would like to gain a deeper understanding of the use of statistics in their work.

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Группа авторов Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists


The first all-inclusive introduction to modern statistical research methods in the natural resource sciences The use of Bayesian statistical analysis has become increasingly important to natural resource scientists as a practical tool for solving various research problems. However, many important contemporary methods of applied statistics, such as generalized linear modeling, mixed-effects modeling, and Bayesian statistical analysis and inference, remain relatively unknown among researchers and practitioners in this field. Through its inclusive, hands-on treatment of real-world examples, Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists successfully introduces the key concepts of statistical analysis and inference with an accessible, easy-to-follow approach. The book provides case studies illustrating common problems that exist in the natural resource sciences and presents the statistical knowledge and tools needed for a modern treatment of these issues. Subsequent chapter coverage features: An introduction to the fundamental concepts of Bayesian statistical analysis, including its historical background, conjugate solutions, Bayesian hypothesis testing and decision-making, and Markov Chain Monte Carlo solutions The relevant advantages of using Bayesian statistical analysis, rather than the traditional frequentist approach, to address research problems Two alternative strategies—the a posteriori model selection strategy and the a priori parsimonious model selection strategy using AIC and DIC—to model selection and inference The ideas of generalized linear modeling (GLM), focusing on the most popular GLM of logistic regression An introduction to mixed-effects modeling in S-Plus® and R for analyzing natural resource data sets with varying error structures and dependencies Each statistical concept is accompanied by an illustration of its frequentist application in S-Plus® or R as well as its Bayesian application in WinBUGS. Brief introductions to these software packages are also provided to help the reader fully understand the concepts of the statistical methods that are presented throughout the book. Assuming only a minimal background in introductory statistics, Contemporary Bayesian and Frequentist Statistical Research Methods for Natural Resource Scientists is an ideal text for natural resource students studying statistical research methods at the upper-undergraduate or graduate level and also serves as a valuable problem-solving guide for natural resource scientists across a broad range of disciplines, including biology, wildlife management, forestry management, fisheries management, and the environmental sciences.

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Группа авторов Statistical Models and Methods for Lifetime Data


Praise for the First Edition «An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . .» -Choice «This is an important book, which will appeal to statisticians working on survival analysis problems.» -Biometrics «A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook.» -Statistics in Medicine The statistical analysis of lifetime or response time data is a key tool in engineering, medicine, and many other scientific and technological areas. This book provides a unified treatment of the models and statistical methods used to analyze lifetime data. Equally useful as a reference for individuals interested in the analysis of lifetime data and as a text for advanced students, Statistical Models and Methods for Lifetime Data, Second Edition provides broad coverage of the area without concentrating on any single field of application. Extensive illustrations and examples drawn from engineering and the biomedical sciences provide readers with a clear understanding of key concepts. New and expanded coverage in this edition includes: * Observation schemes for lifetime data * Multiple failure modes * Counting process-martingale tools * Both special lifetime data and general optimization software * Mixture models * Treatment of interval-censored and truncated data * Multivariate lifetimes and event history models * Resampling and simulation methodology

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Bovas Abraham Statistical Methods for Forecasting


The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. «This book, it must be said, lives up to the words on its advertising cover: 'Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice.' It does just that!» -Journal of the Royal Statistical Society «A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series analysis by PhD students; or to support a concentration in quantitative methods for MBA students; or as a work in applied statistics for advanced undergraduates.» -Choice Statistical Methods for Forecasting is a comprehensive, readable treatment of statistical methods and models used to produce short-term forecasts. The interconnections between the forecasting models and methods are thoroughly explained, and the gap between theory and practice is successfully bridged. Special topics are discussed, such as transfer function modeling; Kalman filtering; state space models; Bayesian forecasting; and methods for forecast evaluation, comparison, and control. The book provides time series, autocorrelation, and partial autocorrelation plots, as well as examples and exercises using real data. Statistical Methods for Forecasting serves as an outstanding textbook for advanced undergraduate and graduate courses in statistics, business, engineering, and the social sciences, as well as a working reference for professionals in business, industry, and government.

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Subhash Aryal Statistical Methods for Groundwater Monitoring


A new edition of the most comprehensive overview of statistical methods for environmental monitoring applications Thoroughly updated to provide current research findings, Statistical Methods for Groundwater Monitoring, Second Edition continues to provide a comprehensive overview and accessible treatment of the statistical methods that are useful in the analysis of environmental data. This new edition expands focus on statistical comparison to regulatory standards that are a vital part of assessment, compliance, and corrective action monitoring in the environmental sciences. The book explores quantitative concepts useful for surface water monitoring as well as soil and air monitoring applications while also maintaining a focus on the analysis of groundwater monitoring data in order to detect environmental impacts from a variety of sources, such as industrial activity and waste disposal. The authors introduce the statistical properties of alternative approaches, such as false positive and false negative rates, that are associated with each test and the factors related to these error rates. The Second Edition also features: An introduction to Intra-laboratory Calibration Curves and random-effects regression models for non-constant measurement variability Coverage of statistical prediction limits for a gamma-distributed random variable, with a focus on estimation and testing of parameters in environmental monitoring applications A unified treatment of censored data with the computation of statistical prediction, tolerance, and control limits Expanded coverage of statistical issues related to laboratory practice, such as detection and quantitation limits An updated chapter on regulatory issues that outlines common mistakes to avoid in groundwater monitoring applications as well as an introduction to the newest regulations for both hazardous and municipal solid waste facilities Each chapter provides a general overview of a problem, followed by statistical derivation of the solution and a relevant example complete with computational details that allow readers to perform routine application of the statistical results. Relevant issues are highlighted throughout, and recommendations are also provided for specific problems based on characteristics such as number of monitoring wells, number of constituents, distributional form of measurements, and detection frequency. Statistical Methods for Groundwater Monitoring, Second Edition is an excellent supplement to courses on environmental statistics at the upper-undergraduate and graduate levels. It is also a valuable resource for researchers and practitioners in the fields of biostatistics, engineering, and the environmental sciences who work with statistical methods in their everyday work.

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Anand Joglekar M. Industrial Statistics. Practical Methods and Guidance for Improved Performance


HELPS YOU FULLY LEVERAGE STATISTICAL METHODS TO IMPROVE INDUSTRIAL PERFORMANCE Industrial Statistics guides you through ten practical statistical methods that have broad applications in many different industries for enhancing research, product design, process design, validation, manufacturing, and continuous improvement. As you progress through the book, you'll discover some valuable methods that are currently underutilized in industry as well as other methods that are often not used correctly. With twenty-five years of teaching and consulting experience, author Anand Joglekar has helped a diverse group of companies reduce costs, accelerate product development, and improve operations through the effective implementation of statistical methods. Based on his experience working with both clients and students, Dr. Joglekar focuses on real-world problem-solving. For each statistical method, the book: Presents the most important underlying concepts clearly and succinctly Minimizes mathematical details that can be delegated to a computer Illustrates applications with numerous practical examples Offers a «Questions to Ask» section at the end of each chapter to assist you with implementation The last chapter consists of 100 practical questions followed by their answers. If you're already familiar with statistical methods, you may want to take the test first to determine which methods to focus on. By helping readers fully leverage statistical methods to improve industrial performance, this book becomes an ideal reference and self-study guide for scientists, engineers, managers and other technical professionals across a wide range of industries. In addition, its clear explanations and examples make it highly suited as a textbook for undergraduate and graduate courses in statistics.

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Группа авторов Biostatistical Methods


Comprehensive coverage of classical and modern methods of biostatistics Biostatistical Methods focuses on the assessment of risks and relative risks on the basis of clinical investigations. It develops basic concepts and derives biostatistical methods through both the application of classical mathematical statistical tools and more modern likelihood-based theories. The first half of the book presents methods for the analysis of single and multiple 2x2 tables for cross-sectional, prospective, and retrospective (case-control) sampling, with and without matching using fixed and two-stage random effects models. The text then moves on to present a more modern likelihood- or model-based approach, which includes unconditional and conditional logistic regression; the analysis of count data and the Poisson regression model; and the analysis of event time data, including the proportional hazards and multiplicative intensity models. The book contains a technical appendix that presents the core mathematical statistical theory used for the development of classical and modern statistical methods. Biostatistical Methods: The Assessment of Relative Risks: * Presents modern biostatistical methods that are generalizations of the classical methods discussed * Emphasizes derivations, not just cookbook methods * Provides copious reference citations for further reading * Includes extensive problem sets * Employs case studies to illustrate application of methods * Illustrates all methods using the Statistical Analysis System(r) (SAS) Supplemented with numerous graphs, charts, and tables as well as a Web site for larger data sets and exercises, Biostatistical Methods: The Assessment of Relative Risks is an excellent guide for graduate-level students in biostatistics and an invaluable reference for biostatisticians, applied statisticians, and epidemiologists.

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