Semiparametric analysis of linear transformation models with covariate measurement errors

Samiran Sinha, Yanyuan Ma

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

We take a semiparametric approach in fitting a linear transformation model to a right censored data when predictive variables are subject to measurement errors. We construct consistent estimating equations when repeated measurements of a surrogate of the unobserved true predictor are available. The proposed approach applies under minimal assumptions on the distributions of the true covariate or the measurement errors. We derive the asymptotic properties of the estimator and illustrate the characteristics of the estimator in finite sample performance via simulation studies. We apply the method to analyze an AIDS clinical trial data set that motivated the work.

Original languageEnglish (US)
Pages (from-to)21-32
Number of pages12
JournalBiometrics
Volume70
Issue number1
DOIs
StatePublished - Jan 1 2014

Fingerprint

Linear Transformation Model
Linear transformations
Measurement errors
Measurement Error
Covariates
Linear Models
Acquired Immunodeficiency Syndrome
Clinical Trials
Estimator
Right-censored Data
Repeated Measurements
Estimating Equation
Asymptotic Properties
Predictors
Simulation Study
clinical trials
Datasets
sampling
methodology

All Science Journal Classification (ASJC) codes

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

Cite this

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Semiparametric analysis of linear transformation models with covariate measurement errors. / Sinha, Samiran; Ma, Yanyuan.

In: Biometrics, Vol. 70, No. 1, 01.01.2014, p. 21-32.

Research output: Contribution to journalArticle

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