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Statistical inference in financial and insurance with R Alexandre Brouste ; Coordinated by Nikolaos Limnios and Yuliya Mishura

By: Contributor(s): Material type: TextTextSeries: Optimization in insurance and finance setPublication details: UK : Elsevier, c2018.Description: xv, 186 p. : ill. ; 24 cmISBN:
  • 9781785480836
Subject(s): DDC classification:
  • 519.2 BR ST
Online resources: Summary: This book examines a range of statistical inference methods in the context of finance and insurance applications, including asymptotical efficiency to give the proper notion of estimation risk, computations with the provided software R, and non-classical statistical experiments. As finance and insurance companies face a wide range of mathematical problems, and statistical experiments with independent and identically distributed samples are relatively common, this book covers topics of value to a wide group. Two examples are treated, including generalized linear models (GLM) extending to non-Gaussian samples (the standard regression method) and homogeneous Markov chains. Examines a range of statistical inference methods in the context of finance and insurance applications Presents the LAN (local asymptotic normality) property of likelihoods Combines the proofs of LAN property for different statistical experiments that appears in financial and insurance mathematics Provides the proper description of such statistical experiments and invites readers to seek optimal estimators (performed in R) for such statistical experiments
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Item type Current library Call number Status Date due Barcode
REGULAR University of Wollongong in Dubai Main Collection 519.2 BR ST (Browse shelf(Opens below)) Available T0054638

This book examines a range of statistical inference methods in the context of finance and insurance applications, including asymptotical efficiency to give the proper notion of estimation risk, computations with the provided software R, and non-classical statistical experiments. As finance and insurance companies face a wide range of mathematical problems, and statistical experiments with independent and identically distributed samples are relatively common, this book covers topics of value to a wide group. Two examples are treated, including generalized linear models (GLM) extending to non-Gaussian samples (the standard regression method) and homogeneous Markov chains.



Examines a range of statistical inference methods in the context of finance and insurance applications
Presents the LAN (local asymptotic normality) property of likelihoods
Combines the proofs of LAN property for different statistical experiments that appears in financial and insurance mathematics
Provides the proper description of such statistical experiments and invites readers to seek optimal estimators (performed in R) for such statistical experiments

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