A note on estimating the conditional expectation under censoring and association: strong uniform consistency

Autor:	Nassira Menni, Abdelkader Tatachak
Rok vydání:	2016
Předmět:	Statistics and Probability Combinatorics Consistency (statistics) Sample size determination Statistics Kernel density estimation Estimator Statistics Probability and Uncertainty Stationary sequence Conditional expectation Censoring (statistics) Mathematics Variable (mathematics)
Zdroj:	Statistical Papers. 59:1009-1030
ISSN:	1613-9798 0932-5026
DOI:	10.1007/s00362-016-0801-8
Popis:	Let $$\left\{ (X_{i},Y_{i}), i \ge 1 \right\} $$ be a strictly stationary sequence of associated random vectors distributed as (X, Y). This note deals with kernel estimation of the regression function $$r(x)=\mathbb {E}[Y|X=x]$$ in the presence of randomly right censored data caused by another variable C. For this model we establish a strong uniform consistency rate of the proposed estimator, say $$r_{n}(x)$$ . Simulations are drawn to illustrate the results and to show how the estimator behaves for moderate sample sizes.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a94a2710fa6e128366c8bcaf61eb6874 https://doi.org/10.1007/s00362-016-0801-8 Zobrazit plný text záznamu