BIBLIOTECA MANUEL BELGRANO - Facultad de Ciencias Económicas - UNC

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Mathematical statistics : basics ideas and selected topics / Peter J. Bickel, Kjell A. Docsum.

Por: Colaborador(es): Tipo de material: TextoTextoSeries texts in statistical science seriesDetalles de publicación: Boca Raton, Fl. : CRC Press, c2015Edición: 2nd edDescripción: xxiv, 556 pISBN:
  • 9781498723800
Tema(s): Clasificación CDD:
  • 21 519.5
Recursos en línea:
Contenidos:
Preface -- 1. Statistical models, goals and performance criteria -- 2. Methods of estimation -- 3. Measures of performance -- 4. Testing and confidence regions -- 5. Asymptotic approximations -- 6. Inference in the multiparameter case -- A review of basic probability theory -- Additional topics in probability and analysis -- Tables.
Resumen: Volume 1 presents fundamental, classical statistical concepts at the doctorate level. It covers estimation, prediction, testing, confidence sets, Bayesian analysis, and the general approach of decision theory. This edition gives careful proofs of major results and explains how the theory sheds light on the properties of practical methods. The book first discusses non- and semiparametric models before covering parameters and parametric models. It then offers a detailed treatment of maximum likelihood estimates (MLEs) and examines the theory of testing and confidence regions, including optimality theory for estimation and elementary robustness considerations. It next presents basic asymptotic approximations with one-dimensional parameter models as examples. The book also describes inference in multivariate (multiparameter) models, exploring asymptotic normality and optimality of MLEs, Wald and Rao statistics, generalized linear models, and more.
Existencias
Tipo de ítem Biblioteca actual Signatura Estado Fecha de vencimiento Código de barras
Libro Libro Biblioteca Manuel Belgrano 519.5 B 56635 (Navegar estantería(Abre debajo)) Disponible 56635

Preface -- 1. Statistical models, goals and performance criteria -- 2. Methods of estimation -- 3. Measures of performance -- 4. Testing and confidence regions -- 5. Asymptotic approximations -- 6. Inference in the multiparameter case -- A review of basic probability theory -- Additional topics in probability and analysis -- Tables.

Volume 1 presents fundamental, classical statistical concepts at the doctorate level. It covers estimation, prediction, testing, confidence sets, Bayesian analysis, and the general approach of decision theory. This edition gives careful proofs of major results and explains how the theory sheds light on the properties of practical methods.
The book first discusses non- and semiparametric models before covering parameters and parametric models. It then offers a detailed treatment of maximum likelihood estimates (MLEs) and examines the theory of testing and confidence regions, including optimality theory for estimation and elementary robustness considerations. It next presents basic asymptotic approximations with one-dimensional parameter models as examples. The book also describes inference in multivariate (multiparameter) models, exploring asymptotic normality and optimality of MLEs, Wald and Rao statistics, generalized linear models, and more.

Bibliografía de la asignatura Teoría Estadística I de la Maestría en Estadística Aplicada.

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