(Ebook) Essentials Of Statistical Inference by G. A. Young, R. L. Smith ISBN 9780511126161, 0511126166

(Ebook) Essentials Of Statistical Inference by G. A. Young, R. L. Smith ISBN 9780511126161, 0511126166

PrefaceThis book aims to provide a concise but comprehensive account of the essential elements ofstatistical inference and theory. It is designed to be used as a text for courses on statisticaltheory for students of mathematics or statistics at the advanced undergraduate or Masterslevel (UK) or the first-year graduate level (US), or as a reference for researchers in otherfields seeking a concise treatment of the key concepts of and approaches to statisticalinference. It is intended to give a contemporary and accessible account of procedures usedto draw formal inference from data.The book focusses on a clear presentation of the main concepts and results underlyingdifferent frameworks of inference, with particular emphasis on the contrasts amongfrequentist, Fisherian and Bayesian approaches. It provides a description of basic materialon these main approaches to inference, as well as more advanced material on recentdevelopments in statistical theory, including higher-order likelihood inference, bootstrapmethods, conditional inference and predictive inference. It places particular emphasis oncontemporary computational ideas, such as applied in bootstrap methodology and Markovchain Monte Carlo techniques of Bayesian inference. Throughout, the text concentrateson concepts, rather than mathematical detail, but every effort has been made to presentthe key theoretical results in as precise and rigorous a manner as possible, consistent withthe overall mathematical level of the book. The book contains numerous extended examplesof application of contrasting inference techniques to real data, as well as selectedhistorical commentaries. Each chapter concludes with an accessible set of problems andexercises.Prerequisites for the book are calculus, linear algebra and some knowledge of basicprobability (including ideas such as conditional probability, transformations of densitiesetc., though not measure theory). Some previous familiarity with the objectives of andmain approaches to statistical inference is helpful, but not essential. Key mathematical andprobabilistic ideas are reviewed in the text where appropriate.