Description
Book SynopsisThis book presents a unified approach on nonparametric estimators for models of independent observations, jump processes and continuous processes. New estimators are defined and their limiting behavior is studied. From a practical point of view, the book expounds on the construction of estimators for functionals of processes and densities, and provides asymptotic expansions and optimality properties from smooth estimators.It also presents new regular estimators for functionals of processes, compares histogram and kernel estimators, compares several new estimators for single-index models, and it examines the weak convergence of the estimators.
Table of ContentsIntroduction; Kernel Estimator of a Density; Kernel Estimator of a Regression Function; Limits for the Varying Bandwidths Estimators; Nonparametric Estimation of Quantiles; Nonparametric Estimation for Stochastic Processes; Estimation in Semi-Parametric Regression Models; Diffusions Processes; Applications to Time Series.
