NITIS MUKHOPADHYAY – PROBABILITY AND STATISTICAL INFERENCE

The textbooks at this level are very competitively priced. The textbook shows the theory of probability and statistical inference in the style of a tutorials, using worked examples, exercises, numerous figures and tables, and computer simulations to develop and illustrate concepts.

Beginning with an introduction to the basic ideas and techniques in probability theory and progressing to more rigorous topics. The waiting time between failures for exponential distributions is studied. Concepts of convergence in probability and distribution have been developed. The sample variance is highlighted by the central limit theorem. Sampling and the Cornish-Fisher expansions are introduced. focuses on the basics of sufficiency, information, completeness, and ancillarity. The location, scale, and location-scale families of distributions are explained. The covers include moment inequality estimators, maximum likelihood inequality estimators, and the Cramér-Rao. Discussing uniformly minimum variance unbiased estimators. The theory of most powerful (MP) and uniformly most powerful (UMP) tests of hypotheses is the focus. The likelihood ratio tests include the mean, variance, and correlation coefficients. The methods are summarized. The monotone likelihood ratio is described. handles stabilizing transformations. Historical context for statistics and statistical discoveries. Great statisticians are showcased through biographical notes.

For first-year graduate and upper-level undergraduate courses in probability and statistical inference, as well as a supplemental text for classes in Advanced Statistical Inference or Decision Theory, Probability and Statistical Inference uses over 1400 equations to reinforce its subject matter.

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