Nonparametric kernel estimators apply to the statistical analysis
of independent or dependent sequences of random variables and for
samples of continuous or discrete processes. The optimization of
these procedures is based on the choice of a bandwidth that
minimizes an estimation error and the weak convergence of the
estimators is proved. This book introduces new mathematical results
on statistical methods for the density and regression functions
presented in the mathematical literature and for functions defining
more complex models such as the models for the intensity of point
processes, for the drift and variance of auto-regressive diffusions
and the single-index regression models.This second edition presents
minimax properties with Lp risks, for a positive real p, and
optimal convergence results for new kernel estimators of function
defining processes: models for multidimensional variables, periodic
intensities, estimators of the distribution functions of censored
and truncated variables, estimation in frailty models, estimators
for time dependent diffusions, for spatial diffusions and for
diffusions with stochastic volatility.
General
| Imprint: |
World Scientific Publishing Co Pte Ltd
|
| Country of origin: |
Singapore |
| Release date: |
October 2023 |
| Authors: |
Odile Pons
|
| Pages: |
250 |
| Edition: |
Second Edition |
| ISBN-13: |
978-981-12-7283-7 |
| Categories: |
Books
|
| LSN: |
981-12-7283-2 |
| Barcode: |
9789811272837 |
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