Standard

On estimation algorithms in nonparametric analysis of the current status right-censored data. / Malova, I.Yu.; Малов, Сергей Васильевич.

Applied Methods of Statistical Analysis. Statistical Computation and Simulation : Proceedings of the International Workshop AMSA'19 . ред. / Boris Lemeshko; Mikhail Nikulin; Narayanaswamy Balakrishnan. Novosibirsk : Новосибирский государственный технический университет, 2019. стр. 74-84.

Результаты исследований: Публикации в книгах, отчётах, сборниках, трудах конференций › статья в сборнике материалов конференции › научная › Рецензирование

Harvard

Malova, IY & Малов, СВ 2019, On estimation algorithms in nonparametric analysis of the current status right-censored data. в B Lemeshko, M Nikulin & N Balakrishnan (ред.), Applied Methods of Statistical Analysis. Statistical Computation and Simulation : Proceedings of the International Workshop AMSA'19 . Новосибирский государственный технический университет, Novosibirsk, стр. 74-84, Applied Methods of Statistical Analysis. Statistical Computation and Simulation , Novosibirsk, Российская Федерация, 18/09/19.

APA

Malova, I. Y., & Малов, С. В. (2019). On estimation algorithms in nonparametric analysis of the current status right-censored data. в B. Lemeshko, M. Nikulin, & N. Balakrishnan (Ред.), Applied Methods of Statistical Analysis. Statistical Computation and Simulation : Proceedings of the International Workshop AMSA'19 (стр. 74-84). Новосибирский государственный технический университет.

Vancouver

Malova IY, Малов СВ. On estimation algorithms in nonparametric analysis of the current status right-censored data. в Lemeshko B, Nikulin M, Balakrishnan N, Редакторы, Applied Methods of Statistical Analysis. Statistical Computation and Simulation : Proceedings of the International Workshop AMSA'19 . Novosibirsk: Новосибирский государственный технический университет. 2019. стр. 74-84

Author

Malova, I.Yu. ; Малов, Сергей Васильевич. / On estimation algorithms in nonparametric analysis of the current status right-censored data. Applied Methods of Statistical Analysis. Statistical Computation and Simulation : Proceedings of the International Workshop AMSA'19 . Редактор / Boris Lemeshko ; Mikhail Nikulin ; Narayanaswamy Balakrishnan. Novosibirsk : Новосибирский государственный технический университет, 2019. стр. 74-84

BibTeX

@inproceedings{34eaa274911a4807ab5a48d0d58c6dc2,
title = "On estimation algorithms in nonparametric analysis of the current status right-censored data",
abstract = "We consider nonparametric estimation algorithms for current status rightcensored data model. In the model right-censored event times are not observed exactly, but at some inspection times. The model covers right-censored data, current status data and life table survival data with a single inspection time. We consider the nonparametric estimation algorithms to obtain three nonparametric estimators for the survival function of failure time: maximum likelihood, pseudo maximum likelihood and the na{\"i}ve estimator. We discuss large sample properties of the estimators. Using the standard R packages we perform simulations, which compare the estimators under small and moderate sample sizes. ",
keywords = "survival data, right censoring, interval censoring, current status data, nonparametric estimation",
author = "I.Yu. Malova and Малов, {Сергей Васильевич}",
year = "2019",
language = "English",
pages = "74--84",
editor = "Lemeshko, {Boris } and Nikulin, {Mikhail } and Balakrishnan, { Narayanaswamy }",
booktitle = "Applied Methods of Statistical Analysis. Statistical Computation and Simulation",
publisher = "Новосибирский государственный технический университет",
address = "Russian Federation",
note = "Applied Methods of Statistical Analysis. Statistical Computation and Simulation , AMSA 2019 ; Conference date: 18-09-2019 Through 20-09-2019",

}

RIS

TY - GEN

T1 - On estimation algorithms in nonparametric analysis of the current status right-censored data

AU - Malova, I.Yu.

AU - Малов, Сергей Васильевич

PY - 2019

Y1 - 2019

N2 - We consider nonparametric estimation algorithms for current status rightcensored data model. In the model right-censored event times are not observed exactly, but at some inspection times. The model covers right-censored data, current status data and life table survival data with a single inspection time. We consider the nonparametric estimation algorithms to obtain three nonparametric estimators for the survival function of failure time: maximum likelihood, pseudo maximum likelihood and the naïve estimator. We discuss large sample properties of the estimators. Using the standard R packages we perform simulations, which compare the estimators under small and moderate sample sizes.

AB - We consider nonparametric estimation algorithms for current status rightcensored data model. In the model right-censored event times are not observed exactly, but at some inspection times. The model covers right-censored data, current status data and life table survival data with a single inspection time. We consider the nonparametric estimation algorithms to obtain three nonparametric estimators for the survival function of failure time: maximum likelihood, pseudo maximum likelihood and the naïve estimator. We discuss large sample properties of the estimators. Using the standard R packages we perform simulations, which compare the estimators under small and moderate sample sizes.

KW - survival data

KW - right censoring

KW - interval censoring

KW - current status data

KW - nonparametric estimation

UR - http://www.amsa.conf.nstu.ru/amsa2019/proceedings/AMSA2019-proceedings.pdf

UR - https://www.elibrary.ru/item.asp?id=41186611

M3 - Conference contribution

SP - 74

EP - 84

BT - Applied Methods of Statistical Analysis. Statistical Computation and Simulation

A2 - Lemeshko, Boris

A2 - Nikulin, Mikhail

A2 - Balakrishnan, Narayanaswamy

PB - Новосибирский государственный технический университет

CY - Novosibirsk

T2 - Applied Methods of Statistical Analysis. Statistical Computation and Simulation

Y2 - 18 September 2019 through 20 September 2019

ER -

ID: 47589570