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{ "item_title" : "Probability and Statistical Inference", "item_author" : [" Nitis Mukhopadhyay "], "item_description" : "Priced very competitively compared with other textbooks at this levelThis 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.Beginning with an introduction to the basic ideas and techniques in probability theory and progressing to more rigorous topics, Probability and Statistical Inferencestudies the Helmert transformation for normal distributions and the waiting time between failures for exponential distributionsdevelops notions of convergence in probability and distributionspotlights the central limit theorem (CLT) for the sample varianceintroduces sampling distributions and the Cornish-Fisher expansionsconcentrates on the fundamentals of sufficiency, information, completeness, and ancillarityexplains Basu's Theorem as well as location, scale, and location-scale families of distributionscovers moment estimators, maximum likelihood estimators (MLE), Rao-Blackwellization, and the Cram r-Rao inequalitydiscusses uniformly minimum variance unbiased estimators (UMVUE) and Lehmann-Scheff Theoremsfocuses on the Neyman-Pearson theory of most powerful (MP) and uniformly most powerful (UMP) tests of hypotheses, as well as confidence intervalsincludes the likelihood ratio (LR) tests for the mean, variance, and correlation coefficientsummarizes Bayesian methodsdescribes the monotone likelihood ratio (MLR) propertyhandles variance stabilizing transformationsprovides a historical context for statistics and statistical discoveriesshowcases great statisticians through biographical notesEmploying 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.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/0/82/470/379/0824703790_b.jpg", "price_data" : { "retail_price" : "200.00", "online_price" : "200.00", "our_price" : "200.00", "club_price" : "200.00", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }
Probability and Statistical Inference|Nitis Mukhopadhyay

Probability and Statistical Inference

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Overview

Priced very competitively compared with other textbooks at this level
This 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.

Beginning with an introduction to the basic ideas and techniques in probability theory and progressing to more rigorous topics, Probability and Statistical Inference
  • studies the Helmert transformation for normal distributions and the waiting time between failures for exponential distributions
  • develops notions of convergence in probability and distribution
  • spotlights the central limit theorem (CLT) for the sample variance
  • introduces sampling distributions and the Cornish-Fisher expansions
  • concentrates on the fundamentals of sufficiency, information, completeness, and ancillarity
  • explains Basu's Theorem as well as location, scale, and location-scale families of distributions
  • covers moment estimators, maximum likelihood estimators (MLE), Rao-Blackwellization, and the Cram r-Rao inequality
  • discusses uniformly minimum variance unbiased estimators (UMVUE) and Lehmann-Scheff Theorems
  • focuses on the Neyman-Pearson theory of most powerful (MP) and uniformly most powerful (UMP) tests of hypotheses, as well as confidence intervals
  • includes the likelihood ratio (LR) tests for the mean, variance, and correlation coefficient
  • summarizes Bayesian methods
  • describes the monotone likelihood ratio (MLR) property
  • handles variance stabilizing transformations
  • provides a historical context for statistics and statistical discoveries
  • showcases great statisticians through biographical notes
Employing 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.

This item is Non-Returnable

Details

  • ISBN-13: 9780824703790
  • ISBN-10: 0824703790
  • Publisher: CRC Press
  • Publish Date: March 2000
  • Dimensions: 9.04 x 6.26 x 1.44 inches
  • Shipping Weight: 2.33 pounds
  • Page Count: 690

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