FUNCTIONAL ESTIMATION FOR DENSITY, REGRESSION MODELS AND PROCESSES (SECOND EDITION)

Odile Pons

ISBN 10: 9811272832 / ISBN 13: 9789811272837

Published by World Scientific Publishing Co Pte Ltd, SG, 2023

About this Item

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 real p larger than one, 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.

Seller Inventory # LU-9789811272837

Bibliographic Details

• Title: FUNCTIONAL ESTIMATION FOR DENSITY, REGRESSION MODELS AND PROCESSES (SECOND EDITION)
• Publisher: World Scientific Publishing Co Pte Ltd, SG
• Publication Date: 2023
• Language: English
• ISBN 10: 9811272832
• ISBN 13: 9789811272837
• Binding: Hardback
• Edition: Second.
• Condition: New
• Dimensions: 15.24W x 1.6H x 22.86D centimeters 15.24 centimeters width by 1.6 centimeters height by 22.86 centimeters depth

About this title

Synopsis

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 real p larger than one, 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.