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In den WarenkorbHardcover. Zustand: Brand New. 186 pages. 9.25x6.25x1.00 inches. In Stock.
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Anbieter: Biblios, Frankfurt am main, HESSE, Deutschland
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Sprache: Englisch
Verlag: ISTE Press Ltd - Elsevier Inc, 2017
ISBN 10: 1785480839 ISBN 13: 9781785480836
Anbieter: THE SAINT BOOKSTORE, Southport, Vereinigtes Königreich
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In den WarenkorbZustand: new. Questo è un articolo print on demand.
Sprache: Englisch
Verlag: Elsevier Science Jul 2017, 2017
ISBN 10: 1785480839 ISBN 13: 9781785480836
Anbieter: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Deutschland
Buch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Finance and insurance companies are facing a wide range of parametric statistical problems. Statistical experiments generated by a sample of independent and identically distributed random variables are frequent and well understood, especially those consisting of probability measures of an exponential type. However, the aforementioned applications also offer non-classical experiments implying observation samples of independent but not identically distributed random variables or even dependent random variables. Three examples of such experiments are treated in this book. First, the Generalized Linear Models are studied. They extend the standard regression model to non-Gaussian distributions. Statistical experiments with Markov chains are considered next. Finally, various statistical experiments generated by fractional Gaussian noise are also described. In this book, asymptotic properties of several sequences of estimators are detailed. The notion of asymptotical efficiency is discussed for the different statistical experiments considered in order to give the proper sense of estimation risk. Eighty examples and computations with R software are given throughout the text. 202 pp. Englisch.
Anbieter: AHA-BUCH GmbH, Einbeck, Deutschland
Buch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Finance and insurance companies are facing a wide range of parametric statistical problems. Statistical experiments generated by a sample of independent and identically distributed random variables are frequent and well understood, especially those consisting of probability measures of an exponential type. However, the aforementioned applications also offer non-classical experiments implying observation samples of independent but not identically distributed random variables or even dependent random variables. Three examples of such experiments are treated in this book. First, the Generalized Linear Models are studied. They extend the standard regression model to non-Gaussian distributions. Statistical experiments with Markov chains are considered next. Finally, various statistical experiments generated by fractional Gaussian noise are also described. In this book, asymptotic properties of several sequences of estimators are detailed. The notion of asymptotical efficiency is discussed for the different statistical experiments considered in order to give the proper sense of estimation risk. Eighty examples and computations with R software are given throughout the text.