The Conditional Probability Integral Transformation And Applications To Obtain Composite Chi Square Goodness Of Fit Tests


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Goodness-of-Fit-Techniques


Goodness-of-Fit-Techniques

Author: RalphB. D'Agostino

language: en

Publisher: Routledge

Release Date: 2017-10-19


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Conveniently grouping methods by techniques, such as chi-squared and empirical distributionfunction , and also collecting methods of testing for specific famous distributions, this usefulreference is the fust comprehensive.review of the extensive literature on the subject. It surveysthe leading methods of testing fit . .. provides tables to make the tests available . .. assessesthe comparative merits of different test procedures . .. and supplies numerical examples to aidin understanding these techniques.Goodness-of-Fit Techniques shows how to apply the techniques . .. emphasizes testing for thethree major distributions, normal, exponential, and uniform . .. discusses the handling of censoreddata .. . and contains over 650 bibliographic citations that cover the field.Illustrated with tables and drawings, this volume is an ideal reference for mathematical andapplied statisticians, and biostatisticians; professionals in applied science fields, including psychologists,biometricians , physicians, and quality control and reliability engineers; advancedundergraduate- and graduate-level courses on goodness-of-fit techniques; and professional seminarsand symposia on applied statistics, quality control, and reliability.

Handbook Of Applied Econometrics And Statistical Inference


Handbook Of Applied Econometrics And Statistical Inference

Author: Aman Ullah

language: en

Publisher: CRC Press

Release Date: 2002-01-29


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Summarizing developments and techniques in the field, this reference covers sample surveys, nonparametric analysis, hypothesis testing, time series analysis, Bayesian inference, and distribution theory for applications in statistics, economics, medicine, biology, engineering, sociology, psychology, and information technology. It supplies a geometric proof of an extended Gauss-Markov theorem, approaches for the design and implementation of sample surveys, advances in the theory of Neyman's smooth test, and methods for pre-test and biased estimation. It includes discussions ofsample size requirements for estimation in SUR models, innovative developments in nonparametric models, and more.

General Technical Report RM.


General Technical Report RM.

Author:

language: en

Publisher:

Release Date: 1981


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