A new non-parametric estimator of the cumulative distribution function under time-and random-censoring

07/10/2023
by   N. Balakrishnan, et al.
0

In this paper, we first provide a review of different non-parametric estimators for the cumulative distribution function under left-censoring. We then propose a new estimator based on a non-parametric likelihood approach using reversed hazard rate. Finally, we conclude with an application to a real data.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
09/24/2019

The Trimmed Mean in Non-parametric Regression Function Estimation

This article studies a trimmed version of the Nadaraya-Watson estimator ...
research
03/06/2019

A comment on "New non-parametric inferences for low-income proportions" by Shan Luo and Gengsheng Qin

Shan Luo and Gengsheng Qin published the article "New non-parametric inf...
research
09/11/2018

Multivariate Brenier cumulative distribution functions and their application to non-parametric testing

In this work we introduce a novel approach of construction of multivaria...
research
09/21/2021

Non-parametric Kernel-Based Estimation of Probability Distributions for Precipitation Modeling

The probability distribution of precipitation amount strongly depends on...
research
03/27/2019

Non-parametric Archimedean generator estimation with implications for multiple testing

In multiple testing, the family-wise error rate can be bounded under som...
research
11/13/2020

Estimating the Copula of a class of Time-Changed Brownian Motions: A non-parametric Approach

Within a high-frequency framework, we propose a non-parametric approach ...
research
10/12/2020

Distributionally Robust Local Non-parametric Conditional Estimation

Conditional estimation given specific covariate values (i.e., local cond...

Please sign up or login with your details

Forgot password? Click here to reset