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Books on the topic 'Statistical hypotheses testing'

Author: Grafiati

Published: 4 June 2021

Last updated: 29 January 2023

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1

Lehmann, E. L. Testing statistical hypotheses. 2nd ed. New York: Springer, 1986.

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2

Lehmann, E. L. Testing statistical hypotheses. 2nd ed. New York: Springer, 1997.

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3

Lehmann, E. L., and Joseph P. Romano. Testing Statistical Hypotheses. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-70578-7.

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4

Lehmann, E. L. Testing Statistical Hypotheses. New York, NY: Springer New York, 1986. http://dx.doi.org/10.1007/978-1-4757-1923-9.

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5

1960-, Romano Joseph P., ed. Testing statistical hypotheses. 3rd ed. New York: Springer, 2005.

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6

Testing statistical hypotheses. 2nd ed. Pacific Grove, Calif: Wadsworth & Brooks/Cole Advanced Books & Software, 1991.

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7

Testing statistical hypotheses. 2nd ed. New York: Wiley, 1986.

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8

Testing statistical hypotheses. 2nd ed. New York: Springer, 1997.

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9

Testing statistical hypotheses of equivalence. Boca Raton, FL: Chapman & Hall/CRC, 2003.

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10

Testing statistical hypotheses of equivalence and noninferiority. 2nd ed. Boca Raton: CRC Press, 2010.

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11

Isadore, Newman, and Kelly Francis J. 1926-, eds. Testing research hypotheses with the general linear model. Carbondale: Southern Illinois University Press, 1996.

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12

Permutation, parametric and bootstrap tests of hypotheses. 3rd ed. New York: Springer, 2005.

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13

Levchenko, Boris. Criteria for testing hypotheses about uniformity. Application manual. ru: INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/986695.

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The guide discusses the use of statistical criteria focused on the testing of hypotheses about uniformity of laws, which belong to the analyzed sample, of the homogeneous medium (about equality of the mathematical expectations), about the homogeneity of variance (equality of variances of compared samples). The disadvantages and advantages of various criteria are indicated, and the application of criteria in conditions of violation of standard assumptions is considered. Tables containing percentage points and statistical distribution models necessary for the correct application of the criteria are provided. This publication describes a broader set of criteria. Constructed models of marginal distributions of statistics for some sample criteria of law uniformity are proposed. Following the recommendations will ensure the correctness and validity of statistical conclusions when analyzing data. It is intended for specialists who are more or less faced with the issues of statistical data analysis, processing the results of experiments, and using statistical methods to analyze various aspects and trends of the surrounding reality. It will be useful for engineers, researchers, specialists in various fields (doctors, biologists, sociologists, economists, etc.), University teachers, graduate students and students.

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14

Permutation tests: A practical guide to resampling methods for testing hypotheses. New York: Springer-Verlag, 1994.

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15

Good, Phillip I. Permutation tests: A practical guide to resampling methods for testing hypotheses. 3rd ed. New York: Springer, 2005.

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16

Bening, Vladimir E. Asymptotic theory of testing statistical hypotheses: Efficient statistics, optimality, power loss, and deficiency. Utrecht: VSP, 2000.

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17

Noreen, Eric W. Computer-intensive methods for testing hypotheses: An introduction. New York: Wiley, 1989.

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18

Bierens, Herman J. Asymptotic theory of integrated conditional moment tests. St. Andrews: St. Salvator's College, 1995.

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19

Lemeshko, Boris, and Irina Veretel'nikova. Criteria for testing hypotheses about randomness and the absence of a trend. Application Guide. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1587437.

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The monograph discusses the application of statistical criteria aimed at testing hypotheses about the absence of a trend in the analyzed samples. The rejection of such a hypothesis gives grounds to consider the analyzed data as samples of independent equally distributed random variables. We consider a set of special criteria aimed at testing such hypotheses, as well as a set of criteria for the uniformity of laws, the uniformity of averages and the uniformity of variances, which can also be used for these purposes. The disadvantages and advantages of various criteria are emphasized, the application of criteria in conditions of violation of standard assumptions is considered. Estimates of the power of the criteria are given, which allows you to navigate when choosing the most preferred criteria. Following the recommendations will ensure the correctness and increase the validity of statistical conclusions when analyzing data. It is intended for specialists who are interested in the application of statistical methods for the analysis of various aspects and trends of the surrounding reality and who are in contact with the processing of experimental results, the need for data analysis in their activities. It will be useful for engineers, researchers, specialists of various profiles (doctors, biologists, sociologists, economists, etc.) who face the need for statistical analysis of experimental results in their activities. It will also be useful for university teachers, graduate students and students.

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20

Khuri, André I. Statistical tests in mixed linear models. New York: Wiley, 1998.

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21

Paliy, Irina. Probability theory and mathematical statistics. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1065828.

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The tutorial is an introductory course in probability theory and mathematical statistics. Elements of combinatorics, basic concepts and theorems of probability theory, discrete random variables, continuous random variables, some limit theorems, one-dimensional and two-dimensional samples, point and interval estimation of parameters of the general population, testing of statistical hypotheses, elements of queuing theory are considered. The presentation of the theoretical material is accompanied by a large number of detailed examples of problem solving. For students of technical and economic fields of study and specialties, studying under the bachelor's and specialty programs.

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22

Shi, Ning-Zhong. Statistical hypothesis testing: Theory and methods. Singapore: World Scientific Publishing, 2008.

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23

Jian, Tao, ed. Statistical hypothesis testing: Theory and methods. Singapore: World Scientific Publishing, 2008.

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24

Introduction to robust estimation and hypothesis testing. 2nd ed. Amsterdam: Elsevier/Academic Press, 2005.

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25

Introduction to robust estimation and hypothesis testing. 3rd ed. Amsterdam: Academic Press, 2012.

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26

Introduction to robust estimation and hypothesis testing. San Diego, CA: Academic Press, 1997.

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27

Wilcox, Rand R. Introduction to robust estimation and hypothesis testing. 3rd ed. Amsterdam: Academic Press, 2012.

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28

Wilcox, Rand R. Introduction to robust estimation and hypothesis testing. 3rd ed. Amsterdam: Academic Press, 2012.

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29

Mohr, Lawerence B. Understanding significance testing. Newbury Park, Calif: Sage Publications, 1990.

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30

Taeger, Dirk, and Sonja Kuhnt. Statistical Hypothesis Testing with SAS and R. Chichester, UK: John Wiley & Sons, Ltd, 2014. http://dx.doi.org/10.1002/9781118762585.

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31

Ward, Roy. Statistics for quality assurance: Hypothesis testing. Sheffield: Sheffield City Polytechnic, 1985.

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32

Tiku, Moti Lal. Robust estimation and hypothesis testing. New Delhi: New Age International (P) Ltd., Publishers, 2004.

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33

Koch, Karl-Rudolf. Parameter estimation and hypothesis testing in linear models. Berlin: Springer-Verlag, 1988.

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34

Lemeshko, Boris, Aleksandr Popov, and Vadim Seleznev. Criteria for checking the deviation of the distribution from the normal law. Application Guide. ru: INFRA-M Academic Publishing LLC., 2022. http://dx.doi.org/10.12737/1896110.

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The monograph discusses the application of statistical criteria aimed at testing the hypothesis that the analyzed data belongs to the normal law of probability distribution. Special criteria, nonparametric criteria of agreement and criteria of type χ2 are considered and compared. The disadvantages and advantages of various criteria are indicated. Tables containing percentage points and statistical distribution models necessary for the correct application of criteria are given. In comparison with the first edition, the set of considered special criteria of normality has been significantly expanded. The entire set of criteria is ranked by power relative to a number of closely competing hypotheses, which facilitates the selection of the most preferred criteria. It is shown that in applications, the properties of criteria can change significantly due to the presence of rounding errors and this must be taken into account when forming statistical conclusions. Following the recommendations when analyzing data will ensure the correctness of statistical conclusions and increase their validity. It is designed for specialists who, in one way or another, encounter in their activities issues of statistical data analysis, processing of experimental results, the use of statistical methods to analyze various aspects and trends of the surrounding reality. It will be useful for engineers, researchers, specialists of various profiles (physicians, biologists, sociologists, economists, etc.), university teachers, graduate students and students.

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35

Testing problems with linear or angular inequality constraints. New York: Springer-Verlag, 1990.

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36

Testing Statistical Hypotheses. Taylor & Francis Group, 1991.

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37

Testing Statistical Hypotheses. New York, NY: Springer New York, 2005. http://dx.doi.org/10.1007/0-387-27605-x.

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38

Wellek, Stefan. Testing Statistical Hypotheses of Equivalence. Chapman & Hall/CRC, 2002.

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39

Wellek, Stefan. Testing Statistical Hypotheses of Equivalence. Taylor & Francis Group, 2003.

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40

Testing Statistical Hypotheses: Volume I. Springer International Publishing AG, 2022.

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41

Wellek, Stefan. Testing Statistical Hypotheses of Equivalence. Chapman and Hall/CRC, 2002. http://dx.doi.org/10.1201/9781420035964.

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42

Wellek, Stefan. Testing Statistical Hypotheses of Equivalence. Taylor & Francis Group, 2002.

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43

Lehmann, E. L., and Joseph P. Romano. Testing Statistical Hypotheses: Volume II. Springer International Publishing AG, 2022.

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44

Testing Statistical Hypotheses (Springer Texts in Statistics). Springer, 2006.

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45

Kachiashvili, Karlos J., and Kartlos Kachiashvili. Constrained Bayesian Methods of Hypotheses Testing: A New Philosophy of Hypotheses Testing in Parallel and Sequential Experiments. Nova Science Publishers, Incorporated, 2018.

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46

Wellek, Stefan. Testing Statistical Hypotheses of Equivalence and Noninferiority. Taylor & Francis Group, 2010.

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47

Lehmann, E. L., and Joseph P. Romano. Testing Statistical Hypotheses: Volumes I and II. Springer International Publishing AG, 2022.

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48

Wellek, Stefan. Testing Statistical Hypotheses of Equivalence and Noninferiority. Taylor & Francis Group, 2010.

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49

Wellek, Stefan. Testing Statistical Hypotheses of Equivalence and Noninferiority. Chapman and Hall/CRC, 2010. http://dx.doi.org/10.1201/ebk1439808184.

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50

Wellek, Stefan. Testing Statistical Hypotheses of Equivalence and Noninferiority. Taylor & Francis Group, 2010.

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