Probability density estimation for survival data with censoring indicators missing at random

Authors:
Qihua Wang;Wei Liu;Chunling Liu
Affiliations:
Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam, Hong Kong and Academy of Mathematics and Systems Science, Chinese Academy of Science, Beijing 100080, China;Department of Mathematics and Statistics, York University, Toronto, ON M3J 1P3, Canada;Department of Statistics and Actuarial Science, The University of Hong Kong, Pokfulam, Hong Kong
Venue:
Journal of Multivariate Analysis
Year:
2009

Citing 2
Cited 1

Statistical analysis with missing data

Statistical analysis with missing data
Kernel density and hazard function estimation in the presence of censoring

Journal of Multivariate Analysis

Partial imputation of unseen records to improve classification using a hybrid multi-layered artificial immune system and genetic algorithm

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, some nonparametric approaches of density function estimation are developed when censoring indicators are missing at random. A conditional mean score based estimator and a mean score estimator are suggested, respectively. The two estimators are proved to be asymptotically normal and uniformly strongly consistent. The bandwidth selection problem is also discussed. A simulation study is conducted to compare finite-sample behaviors of the proposed estimators.