Statistical inference in financial and insurance with R
By: Brouste, Alexandre
Title By: Limnios, Nikolasos [Coordinated by ] | Mishura, Yuliya [Coordinated by ]
Material type:
BookSeries: Optimization in insurance and finance set.Publisher: UK : Elsevier, c2018.Description: xv, 186 p. : ill. ; 24 cm.ISBN: 9781785480836Subject(s): Finance -- Mathematical models | Insurance -- Mathematical models | BUSINESS & ECONOMICS / FinanceDDC classification: 519.2 BR ST Online resources: Location Map | Item type | Home library | Call number | Status | Date due | Barcode | Item holds |
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| REGULAR | University of Wollongong in Dubai Main Collection | 519.2 BR ST (Browse shelf) | Available | T0054638 |
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| 519.02462 WA PR Probability and statistics for engineers and scientists | 519.024658 LE ST Statistics for management : | 519.024658 LE ST Statistics for management : | 519.2 BR ST Statistical inference in financial and insurance with R | 519.2 DE TH Theory of probability : | 519.2 PA FU Fundamentals of probability and stochastic processes with applications to communications | 519.2 PI CO Competing risks : |
Part 1. Inference in Parametric Statistical Experiments 1. Statistical Experiments 2. Statistical Inference 3. Asymptotic Efficiency Part 2. Statistical Inference for Insurance 4. Statistical Experiments in Insurance Part 3. Statistical Inference for Finance 5. Homogeneous Diffusion Processes 6. Statistical Experiments in Finance.
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