Robust Prediction and Interpolation for Vector Stationary Processes. 2d Enriched Version

摘要

The main objectives of this research have been the development of smooth nonparametric estimators of quantile functions from right-censored data and the further study of smooth density estimators from censored observations. In particular, kernel type and generalized quantile estimators have been obtained under censoring which give better estimates of percentiles of the lifetime distribution than the usual product-limit quantile estimator. Other new results include the study of linear empirical Bayes estimators, prediction intervals for the inverse Gaussian distribution, nonparametric hazard rate estimation under censoring, nonparametric inference for step-stress accelerated life tests under censoring. Discrete failure models, reliability estimation when cause of failure is partially known, Gompertzian failure models, simultaneous confidence intervals for pairwise differences of normal means, and optimal designs for comparing treatments with a control.