In the usual right-censored data situation, let fn, n∈N, denote the convolution of the Kaplan-Meier product limit estimator with the kernels a−1 n K(·/an), where K is a smooth probability density with bounded support and an→0. That is, fn is the usual kernel density estimator based on Kaplan-Meier. Let f̄n denote the convolution of the distribution of the uncensored data, which is assumed to hav...

We propose a beta product confidence procedure (BPCP) that is a non-parametric confidence procedure for the survival curve at a fixed time for right-censored data assuming independent censoring. In such situations, the Kaplan-Meier estimator is typically used with an asymptotic confidence interval (CI) that can have coverage problems when the number of observed failures is not large, and/or whe...

MAXIMUM LIKELIHOOD ESTIMATOR OF Drops out of computation of likelihood ratio. DISTRIBUTION OF CONSTRAINT Under normal errors the Euclidean inner product turns out to be practically normal with variance: CONSTRAINTS Five or more orthogonality relations in the form of the Euclidean inner product determine the absolute conic. Rectifying transformation is obtained by Cholesky decomposition. Active ...

In this paper, we propose a new ridge-type estimator called the new mixed ridge estimator (NMRE) by unifying the sample and prior information in linear measurement error model with additional stochastic linear restrictions. The new estimator is a generalization of the mixed estimator (ME) and ridge estimator (RE). The performances of this new estimator and mixed ridge estimator (MRE) against th...

In this study, we develop an asymptotic theory of nonparametric regression for a locally stationary functional time series. First, introduce the notion series (LSFTS) that takes values in semi-metric space. Then, propose model LSFTS with function changes smoothly over time. We establish uniform convergence rates class kernel estimators, Nadaraya-Watson (NW) estimator function, and central limit...

In this paper, we show that the difference between empirical estimator and Conditional value-at-risk can be written as a simple partial sum + residual term. Starting from decomposition, prove central limit theorem some almost sure results for estimator, large class of stationary sequences. We also construct confidence interval with asymptotic level 1−α, study its coverage through two different ...