Statistical Inference in Financial and Insurance Mathematics with R

This bookexamines 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