Essentials of statistical inference : G.A. Young, R.L. Smith.
Contributor(s): Smith, Richard LMaterial type: TextSeries: Cambridge series on statistical and probabilistic mathematics: 16.Publisher: Cambridge, UK ; New York : Cambridge University Press, 2005Description: 1 online resource (x, 225 pages)Content type: text Media type: computer Carrier type: online resourceISBN: 0511126166; 9780511126161; 0511124023; 9780511124020; 0521839718; 9780521839716; 9780511755392; 0511755392; 9780511199646; 0511199643; 1107713749; 9781107713741; 0511567243; 9780511567247; 0511125305; 9780511125300Subject(s): Mathematical statistics | Probabilities | Statistique mathématique | Probabilités | MATHEMATICS -- Probability & Statistics -- General | Mathematical statistics | Probabilities | Inferenzstatistik | Statistische Schlussweise | Statistische toetsen | Afleiding (logica)Genre/Form: Electronic book. | Electronic books. Additional physical formats: Print version:: Essentials of statistical inference.DDC classification: 519.54 LOC classification: QA276 | .Y68 2005ebOther classification: 31.73 | QH 230 | SK 830 | MAT 620f Online resources: Click here to access online
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Series numbering from publisher website.
Includes bibliographical references and index.
Decision theory -- Beyesian methods -- Hypothesis testing -- Special models -- Sufficiency and completeness -- Two-sided tests and conditional inference -- Likelihood theory -- Higher-order theory -- Predictive inference -- Bootstrap methods.
Aimed at advanced undergraduate and graduate students in mathematics and related disciplines, this book presents the concepts and results underlying the Bayesian, frequentist and Fisherian approaches, with particular emphasis on the contrasts between them. Computational ideas are explained, as well as basic mathematical theory. Written in a lucid and informal style, this concise text provides both basic material on the main approaches to inference, as well as more advanced material on developments in statistical theory, including: material on Bayesian computation, such as MCMC, higher-order likelihood theory, predictive inference, bootstrap methods and conditional inference. It contains numerous extended examples of the application of formal inference techniques to real data, as well as historical commentary on the development of the subject. Throughout, the text concentrates on concepts, rather than mathematical detail, while maintaining appropriate levels of formality. Each chapter ends with a set of accessible problems.