Statistical inferenceThis book builds theoretical statistics from the first principles of probability theory. Starting from the basics of probability, the authors develop the theory of statistical inference using techniques, definitions, and concepts that are statistical and are natural extensions and consequences of previous concepts. Intended for firstyear graduate students, this book can be used for students majoring in statistics who have a solid mathematics background. It can also be used in a way that stresses the more practical uses of statistical theory, being more concerned with understanding basic statistical concepts and deriving reasonable statistical procedures for a variety of situations, and less concerned with formal optimality investigations. 
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Review: Statistical Inference
User Review  Fleur_de_soie  GoodreadsRead this book because it is the text for our PhD Econometrics I course, also mainly because it is recommended by Professor D, so first comes his comments on the book. "The standard PhD level first ... Read full review
Review: Statistical Inference
User Review  Cristina  GoodreadsI like how theorems and corollaries are presented, but I'm not crazy about the lack of proofs and some of the examples. This isn't a book from which I could very easily selfteach myself statistics ... Read full review
Contents
Probability Theory  1 
Transformations and Expectations  47 
Common Families of Distributions  85 
Copyright  
14 other sections not shown
Other editions  View all
Studyguide for Statistical Inference by Casella & Berger, ISBN 9780534243128 George Casella,Roger L. Berger No preview available  2006 
Common terms and phrases
acceptance region algorithm ancillary statistic ANOVA approximation assumptions asymptotic Bayes estimator best unbiased estimator bivariate bootstrap calculate compute confidence interval confidence set constant Continuation of Example converges coverage probability defined Definition denote density derived equal equations equivariant error Exercise exponential family finite fx(x fx{x gamma given hence Inequality inference integral interval estimator joint pdf least squares Lemma Let Xi level a test likelihood function Likelihood Principle linear Mestimator marginal distribution maximum mean and variance median method of moments minimal sufficient statistic Miscellanea observed obtain order statistics pdf or pmf point estimator Poisson Poisson(A population power function problem proof properties prove random sample random variable random vector regression relationship risk function sample mean sample space satisfies Section sequence Show sufficient statistic Suppose Theorem transformation Type I Error unbiased estimator verify Xn be iid