Semiparametric probit models with univariate and bivariate current-status data

Hao Liu, Jing Qin

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Multivariate current-status data are frequently encountered in biomedical and public health studies. Semiparametric regression models have been extensively studied for univariate current-status data, but most existing estimation procedures are computationally intensive, involving either penalization or smoothing techniques. It becomes more challenging for the analysis of multivariate current-status data. In this article, we study the maximum likelihood estimations for univariate and bivariate current-status data under the semiparametric probit regression models. We present a simple computational procedure combining the expectation–maximization algorithm with the pool-adjacent-violators algorithm for solving the monotone constraint on the baseline function. Asymptotic properties of the maximum likelihood estimators are investigated, including the calculation of the explicit information bound for univariate current-status data, as well as the asymptotic consistency and convergence rate for bivariate current-status data. Extensive simulation studies showed that the proposed computational procedures performed well under small or moderate sample sizes. We demonstrate the estimation procedure with two real data examples in the areas of diabetic and HIV research.

Original languageEnglish (US)
Pages (from-to)68-76
Number of pages9
JournalBiometrics
Volume74
Issue number1
DOIs
StatePublished - Mar 1 2018
Externally publishedYes

Fingerprint

Maximum likelihood estimation
Public health
Regression analysis
Numerical models
Statistical methods
Computer simulation
Biometrics
Maximum likelihood
Multivariate Analysis
Statistical Models

Keywords

  • EM algorithm
  • Isotonic regression
  • Maximum likelihood estimation
  • Multivariate current-status data
  • Survival analysis

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)
  • Agricultural and Biological Sciences(all)
  • Applied Mathematics

Cite this

Semiparametric probit models with univariate and bivariate current-status data. / Liu, Hao; Qin, Jing.

In: Biometrics, Vol. 74, No. 1, 01.03.2018, p. 68-76.

Research output: Contribution to journalArticle

@article{de627ed7e42a4f98896c80fe17f82270,
title = "Semiparametric probit models with univariate and bivariate current-status data",
abstract = "Multivariate current-status data are frequently encountered in biomedical and public health studies. Semiparametric regression models have been extensively studied for univariate current-status data, but most existing estimation procedures are computationally intensive, involving either penalization or smoothing techniques. It becomes more challenging for the analysis of multivariate current-status data. In this article, we study the maximum likelihood estimations for univariate and bivariate current-status data under the semiparametric probit regression models. We present a simple computational procedure combining the expectation–maximization algorithm with the pool-adjacent-violators algorithm for solving the monotone constraint on the baseline function. Asymptotic properties of the maximum likelihood estimators are investigated, including the calculation of the explicit information bound for univariate current-status data, as well as the asymptotic consistency and convergence rate for bivariate current-status data. Extensive simulation studies showed that the proposed computational procedures performed well under small or moderate sample sizes. We demonstrate the estimation procedure with two real data examples in the areas of diabetic and HIV research.",
keywords = "EM algorithm, Isotonic regression, Maximum likelihood estimation, Multivariate current-status data, Survival analysis",
author = "Hao Liu and Jing Qin",
year = "2018",
month = "3",
day = "1",
doi = "10.1111/biom.12709",
language = "English (US)",
volume = "74",
pages = "68--76",
journal = "Biometrics",
issn = "0006-341X",
publisher = "Wiley-Blackwell",
number = "1",

}

TY - JOUR

T1 - Semiparametric probit models with univariate and bivariate current-status data

AU - Liu, Hao

AU - Qin, Jing

PY - 2018/3/1

Y1 - 2018/3/1

N2 - Multivariate current-status data are frequently encountered in biomedical and public health studies. Semiparametric regression models have been extensively studied for univariate current-status data, but most existing estimation procedures are computationally intensive, involving either penalization or smoothing techniques. It becomes more challenging for the analysis of multivariate current-status data. In this article, we study the maximum likelihood estimations for univariate and bivariate current-status data under the semiparametric probit regression models. We present a simple computational procedure combining the expectation–maximization algorithm with the pool-adjacent-violators algorithm for solving the monotone constraint on the baseline function. Asymptotic properties of the maximum likelihood estimators are investigated, including the calculation of the explicit information bound for univariate current-status data, as well as the asymptotic consistency and convergence rate for bivariate current-status data. Extensive simulation studies showed that the proposed computational procedures performed well under small or moderate sample sizes. We demonstrate the estimation procedure with two real data examples in the areas of diabetic and HIV research.

AB - Multivariate current-status data are frequently encountered in biomedical and public health studies. Semiparametric regression models have been extensively studied for univariate current-status data, but most existing estimation procedures are computationally intensive, involving either penalization or smoothing techniques. It becomes more challenging for the analysis of multivariate current-status data. In this article, we study the maximum likelihood estimations for univariate and bivariate current-status data under the semiparametric probit regression models. We present a simple computational procedure combining the expectation–maximization algorithm with the pool-adjacent-violators algorithm for solving the monotone constraint on the baseline function. Asymptotic properties of the maximum likelihood estimators are investigated, including the calculation of the explicit information bound for univariate current-status data, as well as the asymptotic consistency and convergence rate for bivariate current-status data. Extensive simulation studies showed that the proposed computational procedures performed well under small or moderate sample sizes. We demonstrate the estimation procedure with two real data examples in the areas of diabetic and HIV research.

KW - EM algorithm

KW - Isotonic regression

KW - Maximum likelihood estimation

KW - Multivariate current-status data

KW - Survival analysis

UR - http://www.scopus.com/inward/record.url?scp=85018765740&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85018765740&partnerID=8YFLogxK

U2 - 10.1111/biom.12709

DO - 10.1111/biom.12709

M3 - Article

C2 - 28437561

AN - SCOPUS:85018765740

VL - 74

SP - 68

EP - 76

JO - Biometrics

JF - Biometrics

SN - 0006-341X

IS - 1

ER -