Titlebook: Asymptotic Statistical Inference; A Basic Course Using Shailaja Deshmukh,Madhuri Kulkarni Textbook 2021 The Editor(s) (if applicable) and T

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Textbook 2021s for contingency tables. The book also discusses a score test and Wald’s test, their relationship with the likelihood ratio test and Karl Pearson’s chi-square test. An important finding is that, while testing any hypothesis about the parameters of a multinomial distribution, a score test statistic

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and computational exercises based on R, and MCQs to clarify.The book presents the fundamental concepts from asymptotic statistical inference theory, elaborating on some basic large sample optimality properties of estimators and some test procedures. The most desirable property of consistency of an

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Shailaja Deshmukh,Madhuri KulkarniPresents fundamental concepts from asymptotic statistical inference theory, illustrated by R software.Contains numerous examples, conceptual and computational exercises based on R, and MCQs to clarify

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https://doi.org/10.1007/978-1-4302-0377-3 distribution with some illustrations as it is basic to all statistical inference procedures and data analysis. An estimator . is defined as a Borel measurable function from the sample space to the parameter space. In the present book, the focus is on the discussion of large sample optimality proper

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Neil Daswani,Christoph Kern,Anita Kesavan to . using various modes of convergence. The most frequently investigated large sample property of an estimator is weak consistency. Weak consistency of an estimator is defined in terms of convergence in probability. We examine how close the estimator is to the true parameter value in terms of prob