๐”– Bobbio Scriptorium
โœฆ   LIBER   โœฆ

Kernel density estimation under weak dependence with sampled data

โœ Scribed by Berlin Wu

Publisher
Elsevier Science
Year
1997
Tongue
English
Weight
458 KB
Volume
61
Category
Article
ISSN
0378-3758

No coin nor oath required. For personal study only.

โœฆ Synopsis

Kernel type estimators of the density of continuous time R<valued stochastic processes are studied. Uniform strong consistency on R e of the estimators and their rates of convergence are obtained. The stochastic processes are assumed to satisfy the strong mixing condition and the sampling instants are random. It is shown that the estimators can attain the optimal L ~ rates of convergence.

๐Ÿ“œ SIMILAR VOLUMES

Kernel Density and Hazard Rate Estimatio
Kernel Density and Hazard Rate Estimation for Censored Dependent Data
โœ Zongwu Cai ๐Ÿ“‚ Article ๐Ÿ“… 1998 ๐Ÿ› Elsevier Science ๐ŸŒ English โš– 266 KB

In some long term studies, a series of dependent and possibly censored failure times may be observed. Suppose that the failure times have a common marginal distribution function having a density, and the nonparametric estimation of density and hazard rate under random censorship is of our interest.