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Mathematical Nonparametric Statistics


Mathematical Nonparametric Statistics
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Mathematical Nonparametric Statistics


Mathematical Nonparametric Statistics
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Author : Edward B. Manoukian
language : en
Publisher: Taylor & Francis
Release Date : 2022-04-18

Mathematical Nonparametric Statistics written by Edward B. Manoukian and has been published by Taylor & Francis this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-18 with Mathematics categories.


First published in 1986. Primarily a reference text, Mathematical Nonparametric Statistics provides mathematicians and students with a systematic mathematical analysis and the fine points of nonparametrical statistical procedures and models used in practice. Divided into five sections and beginning with an extensive chapter on the fundamentals of mathematical statistical methods, its coverage of such topics as the Jackknife method, the Kolmogorov-Smirnov statistic, Box's method and the ch-squared test of fit is rigorous. Written for audiences with differing backgounds in mathematics, the book is of special use to those in the management sciences, industrial engineering, psychology and economics, as well as mathematics.



Mathematical Nonparametric Statistics


Mathematical Nonparametric Statistics
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Author : Manoukian
language : en
Publisher: CRC Press
Release Date : 1986-11-10

Mathematical Nonparametric Statistics written by Manoukian and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986-11-10 with Mathematics categories.


First published in 1986. Primarily a reference text, Mathematical Nonparametric Statistics provides mathematicians and students with a systematic mathematical analysis and the fine points of nonparametrical statistical procedures and models used in practice. Divided into five sections and beginning with an extensive chapter on the fundamentals of mathematical statistical methods, its coverage of such topics as the Jackknife method, the Kolmogorov-Smirnov statistic, Box's method and the ch-squared test of fit is rigorous. Written for audiences with differing backgounds in mathematics, the book is of special use to those in the management sciences, industrial engineering, psychology and economics, as well as mathematics.



Theory Of Nonparametric Tests


Theory Of Nonparametric Tests
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Author : Thorsten Dickhaus
language : en
Publisher: Springer
Release Date : 2018-03-27

Theory Of Nonparametric Tests written by Thorsten Dickhaus and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-03-27 with Mathematics categories.


This textbook provides a self-contained presentation of the main concepts and methods of nonparametric statistical testing, with a particular focus on the theoretical foundations of goodness-of-fit tests, rank tests, resampling tests, and projection tests. The substitution principle is employed as a unified approach to the nonparametric test problems discussed. In addition to mathematical theory, it also includes numerous examples and computer implementations. The book is intended for advanced undergraduate, graduate, and postdoc students as well as young researchers. Readers should be familiar with the basic concepts of mathematical statistics typically covered in introductory statistics courses.



Mathematical Statistics


Mathematical Statistics
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Author : Keith Knight
language : en
Publisher: CRC Press
Release Date : 1999-11-24

Mathematical Statistics written by Keith Knight and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 1999-11-24 with Mathematics categories.


Traditional texts in mathematical statistics can seem - to some readers-heavily weighted with optimality theory of the various flavors developed in the 1940s and50s, and not particularly relevant to statistical practice. Mathematical Statistics stands apart from these treatments. While mathematically rigorous, its focus is on providing a set of useful tools that allow students to understand the theoretical underpinnings of statistical methodology. The author concentrates on inferential procedures within the framework of parametric models, but - acknowledging that models are often incorrectly specified - he also views estimation from a non-parametric perspective. Overall, Mathematical Statistics places greater emphasis on frequentist methodology than on Bayesian, but claims no particular superiority for that approach. It does emphasize, however, the utility of statistical and mathematical software packages, and includes several sections addressing computational issues. The result reaches beyond "nice" mathematics to provide a balanced, practical text that brings life and relevance to a subject so often perceived as irrelevant and dry.



All Of Nonparametric Statistics


All Of Nonparametric Statistics
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Author : Larry Wasserman
language : en
Publisher: Springer Science & Business Media
Release Date : 2006-09-10

All Of Nonparametric Statistics written by Larry Wasserman and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2006-09-10 with Mathematics categories.


There are many books on various aspects of nonparametric inference such as density estimation, nonparametric regression, bootstrapping, and wavelets methods. But it is hard to ?nd all these topics covered in one place. The goal of this text is to provide readers with a single book where they can ?nd a brief account of many of the modern topics in nonparametric inference. The book is aimed at master’s-level or Ph. D. -level statistics and computer science students. It is also suitable for researchersin statistics, machine lea- ing and data mining who want to get up to speed quickly on modern n- parametric methods. My goal is to quickly acquaint the reader with the basic concepts in many areas rather than tackling any one topic in great detail. In the interest of covering a wide range of topics, while keeping the book short, I have opted to omit most proofs. Bibliographic remarks point the reader to references that contain further details. Of course, I have had to choose topics to include andto omit,the title notwithstanding. For the mostpart,I decided to omit topics that are too big to cover in one chapter. For example, I do not cover classi?cation or nonparametric Bayesian inference. The book developed from my lecture notes for a half-semester (20 hours) course populated mainly by master’s-level students. For Ph. D.



Introduction To Nonparametric Statistics


Introduction To Nonparametric Statistics
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Author : JOHN E. KOLASSA
language : en
Publisher:
Release Date : 2022

Introduction To Nonparametric Statistics written by JOHN E. KOLASSA and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.




Nonparametric Statistics


Nonparametric Statistics
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Author : Gregory W. Corder
language : en
Publisher: John Wiley & Sons
Release Date : 2014-04-14

Nonparametric Statistics written by Gregory W. Corder and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-04-14 with Mathematics categories.


“...a very useful resource for courses in nonparametric statistics in which the emphasis is on applications rather than on theory. It also deserves a place in libraries of all institutions where introductory statistics courses are taught." –CHOICE This Second Edition presents a practical and understandable approach that enhances and expands the statistical toolset for readers. This book includes: New coverage of the sign test and the Kolmogorov-Smirnov two-sample test in an effort to offer a logical and natural progression to statistical power SPSS® (Version 21) software and updated screen captures to demonstrate how to perform and recognize the steps in the various procedures Data sets and odd-numbered solutions provided in an appendix, and tables of critical values Supplementary material to aid in reader comprehension, which includes: narrated videos and screen animations with step-by-step instructions on how to follow the tests using SPSS; online decision trees to help users determine the needed type of statistical test; and additional solutions not found within the book.



Nonparametric Statistical Methods For Complete And Censored Data


Nonparametric Statistical Methods For Complete And Censored Data
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Author : M.M. Desu
language : en
Publisher: CRC Press
Release Date : 2003-09-29

Nonparametric Statistical Methods For Complete And Censored Data written by M.M. Desu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-09-29 with Mathematics categories.


Balancing the "cookbook" approach of some texts with the more mathematical approach of others, Nonparametric Statistical Methods for Complete and Censored Data introduces commonly used non-parametric methods for complete data and extends those methods to right censored data analysis. Whenever possible, the authors derive their methodology from the general theory of statistical inference and introduce the concepts intuitively for students with minimal backgrounds. Derivations and mathematical details are relegated to appendices at the end of each chapter, which allows students to easily proceed through each chapter without becoming bogged down in a lot of mathematics. In addition to the nonparametric methods for analyzing complete and censored data, the book covers optimal linear rank statistics, clinical equivalence, analysis of block designs, and precedence tests. To make the material more accessible and practical, the authors use SAS programs to illustrate the various methods included. Exercises in each chapter, SAS code, and a clear, accessible presentation make this an outstanding text for a one-semester senior or graduate-level course in nonparametric statistics for students in a variety of disciplines, from statistics and biostatistics to business, psychology, and the social scientists. Prerequisites: Students will need a solid background in calculus and a two-semester course in mathematical statistics.



All Of Statistics


All Of Statistics
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Author : Larry Wasserman
language : en
Publisher: Springer Science & Business Media
Release Date : 2013-12-11

All Of Statistics written by Larry Wasserman and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-12-11 with Mathematics categories.


Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.



Mathematical Statistics


Mathematical Statistics
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Author : A A Borokov
language : en
Publisher: Routledge
Release Date : 2019-01-22

Mathematical Statistics written by A A Borokov and has been published by Routledge this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-22 with Mathematics categories.


A wide-ranging, extensive overview of modern mathematical statistics, this work reflects the current state of the field while being succinct and easy to grasp. The mathematical presentation is coherent and rigorous throughout. The author presents classical results and methods that form the basis of modern statistics, and examines the foundations o