Home > Research > Publications & Outputs > Analysis of censored correlated observations.
View graph of relations

Analysis of censored correlated observations.

Research output: Contribution to journalJournal articlepeer-review

Published

Standard

Analysis of censored correlated observations. / Oskrochi, Gholam.

In: Journal of Statistical Planning and Inference, Vol. 47, No. 1-2, 01.10.1995, p. 165-180.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Oskrochi, G 1995, 'Analysis of censored correlated observations.', Journal of Statistical Planning and Inference, vol. 47, no. 1-2, pp. 165-180. https://doi.org/10.1016/0378-3758(94)00129-J

APA

Oskrochi, G. (1995). Analysis of censored correlated observations. Journal of Statistical Planning and Inference, 47(1-2), 165-180. https://doi.org/10.1016/0378-3758(94)00129-J

Vancouver

Oskrochi G. Analysis of censored correlated observations. Journal of Statistical Planning and Inference. 1995 Oct 1;47(1-2):165-180. https://doi.org/10.1016/0378-3758(94)00129-J

Author

Oskrochi, Gholam. / Analysis of censored correlated observations. In: Journal of Statistical Planning and Inference. 1995 ; Vol. 47, No. 1-2. pp. 165-180.

Bibtex

@article{0e273bff800b4098bfcf8987454cbf17,
title = "Analysis of censored correlated observations.",
abstract = "In survival analysis, the most frequently used parametric survival models are the exponential and the Weibull distribution. A random effects model based on a generalized Weibull distribution is proposed for censored correlated observations. The specific individual effects play the role of the random effects part. A conceptually simple and very useful algorithm using the generalized linear model is given, to apply random effects Weibull models to data, and calculate the asymptotic variance-covariance matrix. The model is applied to two real data sets and the results are compared with previous work.",
keywords = "Generalized linear models, Censored data likelihood, Random effect models, EM algorithm",
author = "Gholam Oskrochi",
year = "1995",
month = oct,
day = "1",
doi = "10.1016/0378-3758(94)00129-J",
language = "English",
volume = "47",
pages = "165--180",
journal = "Journal of Statistical Planning and Inference",
issn = "0378-3758",
publisher = "Elsevier",
number = "1-2",

}

RIS

TY - JOUR

T1 - Analysis of censored correlated observations.

AU - Oskrochi, Gholam

PY - 1995/10/1

Y1 - 1995/10/1

N2 - In survival analysis, the most frequently used parametric survival models are the exponential and the Weibull distribution. A random effects model based on a generalized Weibull distribution is proposed for censored correlated observations. The specific individual effects play the role of the random effects part. A conceptually simple and very useful algorithm using the generalized linear model is given, to apply random effects Weibull models to data, and calculate the asymptotic variance-covariance matrix. The model is applied to two real data sets and the results are compared with previous work.

AB - In survival analysis, the most frequently used parametric survival models are the exponential and the Weibull distribution. A random effects model based on a generalized Weibull distribution is proposed for censored correlated observations. The specific individual effects play the role of the random effects part. A conceptually simple and very useful algorithm using the generalized linear model is given, to apply random effects Weibull models to data, and calculate the asymptotic variance-covariance matrix. The model is applied to two real data sets and the results are compared with previous work.

KW - Generalized linear models

KW - Censored data likelihood

KW - Random effect models

KW - EM algorithm

U2 - 10.1016/0378-3758(94)00129-J

DO - 10.1016/0378-3758(94)00129-J

M3 - Journal article

VL - 47

SP - 165

EP - 180

JO - Journal of Statistical Planning and Inference

JF - Journal of Statistical Planning and Inference

SN - 0378-3758

IS - 1-2

ER -