{"product_id":"probability-and-statistical-inference-9780367659493","title":"Probability and Statistical Inference","description":"\u003cp\u003e • Author(s): Nitis Mukhopadhyay\u003cbr\u003e • Publisher: Taylor \u0026amp; Francis\u003cbr\u003e • Publisher Imprint: CRC Press\u003cbr\u003e • BISAC: Probability \u0026amp; Statistics - Bayesian Analysis\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003ePriced very competitively compared with other textbooks at this level!\u003cbr\u003eThis gracefully organized textbook reveals the rigorous theory of probability and statistical inference in the style of a tutorial, using worked examples, exercises, numerous figures and tables, and computer simulations to develop and illustrate concepts. \u003c\/p\u003e\u003cp\u003e\u003c\/p\u003eBeginning with an introduction to the basic ideas and techniques in probability theory and progressing to more rigorous topics, Probability and Statistical Inference\u003cli\u003estudies the Helmert transformation for normal distributions and the waiting time between failures for exponential distributions \u003cbr\u003e \u003c\/li\u003e\u003cli\u003edevelops notions of convergence in probability and distribution \u003cbr\u003e \u003c\/li\u003e\u003cli\u003espotlights the central limit theorem (CLT) for the sample variance \u003cbr\u003e \u003c\/li\u003e\u003cli\u003eintroduces sampling distributions and the Cornish-Fisher expansions \u003cbr\u003e \u003c\/li\u003e\u003cli\u003econcentrates on the fundamentals of sufficiency, information, completeness, and ancillarity \u003cbr\u003e \u003c\/li\u003e\u003cli\u003eexplains Basu's Theorem as well as location, scale, and location-scale families of distributions \u003cbr\u003e \u003c\/li\u003e\u003cli\u003ecovers moment estimators, maximum likelihood estimators (MLE), Rao-Blackwellization, and the Cramér-Rao inequality \u003cbr\u003e \u003c\/li\u003e\u003cli\u003ediscusses uniformly minimum variance unbiased estimators (UMVUE) and Lehmann-Scheffé Theorems \u003cbr\u003e \u003c\/li\u003e\u003cli\u003efocuses on the Neyman-Pearson theory of most powerful (MP) and uniformly most powerful (UMP) tests of hypotheses, as well as confidence intervals \u003cbr\u003e \u003c\/li\u003e\u003cli\u003eincludes the likelihood ratio (LR) tests for the mean, variance, and correlation coefficient \u003cbr\u003e \u003c\/li\u003e\u003cli\u003esummarizes Bayesian methods \u003cbr\u003e \u003c\/li\u003e\u003cli\u003edescribes the monotone likelihood ratio (MLR) property \u003cbr\u003e \u003c\/li\u003e\u003cli\u003ehandles variance stabilizing transformations \u003cbr\u003e \u003c\/li\u003e\u003cli\u003eprovides a historical context for statistics and statistical discoveries \u003cbr\u003e \u003c\/li\u003e\u003cli\u003eshowcases great statisticians through biographical notes \u003cp\u003e\u003c\/p\u003eEmploying over 1400 equations to reinforce its subject matter, Probability and Statistical Inference is a groundbreaking text for first-year graduate and upper-level undergraduate courses in probability and statistical inference who have completed a calculus prerequisite, as well as a supplemental text for classes in Advanced Statistical Inference or Decision Theory.\u003c\/li\u003e","brand":"Taylor \u0026 Francis","offers":[{"title":"Paperback","offer_id":45240577261719,"sku":"9780367659493","price":4357.0,"currency_code":"INR","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0666\/3471\/1191\/files\/9780367659493.webp?v=1769226013","url":"https:\/\/atlanticbooks.com\/products\/probability-and-statistical-inference-9780367659493","provider":"Atlantic Books","version":"1.0","type":"link"}