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.
- 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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