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Statistical Inference in Financial and Insurance with R
Language: English
Published by ISTE Press - Elsevier, 2017
ISBN 10: 1785480839 ISBN 13: 9781785480836
Condition: New. pp. 150.
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Statistical Inference in Financial and Insurance Mathematics With R
Language: English
Published by Elsevier Science Ltd, 2017
ISBN 10: 1785480839 ISBN 13: 9781785480836
Hardcover. Condition: Brand New. 186 pages. 9.25x6.25x1.00 inches. In Stock.
-
Statistical Inference in Financial and Insurance with R
Language: English
Published by ISTE Press - Elsevier, 2017
ISBN 10: 1785480839 ISBN 13: 9781785480836
Condition: New. pp. 150.
-
Statistical Inference in Financial and Insurance with R
Language: English
Published by ISTE Press - Elsevier, 2017
ISBN 10: 1785480839 ISBN 13: 9781785480836
Condition: New. pp. 150.
-
Statistical Inference in Financial and Insurance Mathematics with R
Language: English
Published by ISTE Press Ltd - Elsevier Inc, 2017
ISBN 10: 1785480839 ISBN 13: 9781785480836
Seller: THE SAINT BOOKSTORE, Southport, United Kingdom
Seller rating 5 out of 5 stars
£ 144.22
£ 15.72 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketHardback. Condition: New. New copy - Usually dispatched within 4 working days.
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Statistical Inference in Financial and Insurance Mathematics with R
Language: English
Published by ISTE Press - Elsevier, 2017
ISBN 10: 1785480839 ISBN 13: 9781785480836
Condition: new. Questo è un articolo print on demand.
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Statistical Inference in Financial and Insurance Mathematics with R
Language: English
Published by Elsevier Science Jul 2017, 2017
ISBN 10: 1785480839 ISBN 13: 9781785480836
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: 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.
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Buch. Condition: 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.
