Variable selection for semiparametric errors-in-variables regression model with longitudinal data

Tian, Ruiqin; Xue, Liugen

doi:10.1080/00949655.2012.757767

Cited by 5 publications

(4 citation statements)

References 25 publications

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“…In this section, we have highlighted only a few approaches in detail. Other approaches to address variable selection in high dimensional data include Datta and Zou, Loh and Wainwright, Sorensen et al, Yang and Xia, Tian and Xue, and Wang et al Zhang et al discuss generally model selection in the setting linear regression models with measurement error.…”

Section: Special Topicsmentioning

confidence: 99%

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2—More complex methods of adjustment and advanced topics

Shaw

Gustafson

Carroll

et al. 2020

Statistics in Medicine

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We continue our review of issues related to measurement error and misclassification in epidemiology. We further describe methods of adjusting for biased estimation caused by measurement error in continuous covariates, covering likelihood methods, Bayesian methods, moment reconstruction, moment-adjusted imputation, and multiple imputation. We then describe which methods can also be used with misclassification of categorical covariates. Methods of adjusting estimation of distributions of continuous variables for measurement error are then reviewed. Illustrative examples are provided throughout these sections. We provide lists of available software for implementing these methods and also provide the code for implementing our examples in the Supporting Information. Next, we present several advanced topics, including data subject to both classical and Berkson error, modeling continuous exposures with measurement error, and categorical exposures with misclassification in the same model, variable selection when some of the variables are measured with error, adjusting analyses or design for error in an outcome variable, and categorizing continuous variables measured with error. Finally, we provide some advice for the often met situations where variables are known to be measured with substantial error, but there is only an external reference standard or partial (or no) information about the type or magnitude of the error.

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Section: Special Topicsmentioning

confidence: 99%

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2—More complex methods of adjustment and advanced topics

Shaw

Gustafson

Carroll

et al. 2020

Statistics in Medicine

View full text Add to dashboard Cite

show abstract

“…To consider the within correlation, Tian et al [22] proposed a generalized empirical likelihood (GEL) method by combining quadratic inference functions for generalized linear model with longitudinal data. Tian et al [21] discussed the variable selection for the partial linear EV model with longitudinal data when some covariates are measured with errors.…”

Section: Introductionmentioning

confidence: 99%

Generalized Empirical Likelihood Inference in Partially Linear Errors-in-Variables Models with Longitudinal data

Liu

Xue

Tian

2016

HJMS

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This article is concerned with estimations for longitudinal partial linear models with covariate that is measured with error. We propose a generalized empirical likelihood method by combining correction attenuation and quadratic inference functions. The method takes into account the within-subject correlation without involving direct estimation of nuisance parameters in the correlation matrix. We define a generalized empirical likelihood-based statistic for the regression coefficients and residual adjusted empirical likelihood for the baseline function.The empirical log-likelihood ratios are proven to be asymptotically chi-squared, and the corresponding confidence regions are then constructed. Compared with methods based on normal approximations, the generalized empirical likelihood does not require consistent estimators for the asymptotic variance and bias. Furthermore, a simulation study is conducted to evaluate the performance of the proposed method.

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“…On the other hand, various methods are available for fitting the semiparametric models, such as, the kernel smoothing method and the spline method. See for example, [5,19,26] and so on. Recently, the B-spline method is widely used to fit semiparametric models because of its advantages.…”

Section: Introductionmentioning

confidence: 99%

“…See for example, [5,19,26] and so on. Recently, the B-spline method is widely used to fit semiparametric models because of its advantages.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Analysis for Semiparametric Mixed-E®ects Double Regression Models

Xu¹,

Zhang

Wu³

2014

HJMS

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In recent years, based on jointly modeling the mean and variance, double regression models are widely used in practice. In order to assess the effects of continuous covariates or of time scales in a flexible way, a class of semiparametric mixed-effects double regression models(SMMEDRMs) is considered, in which we model the variance of the mixed effects directly as a function of the explanatory variables. In this paper, we propose a fully Bayesian inference for SMMEDRMs on the basis of B-spline estimates of nonparametric components. A computational efficient MCMC method which combines the Gibbs sampler and Metropolis-Hastings algorithm is implemented to simultaneously obtain the Bayesian estimates of unknown parameters and the smoothing function, as well as their standard deviation estimates. Finally, some simulation studies and a real example are used to illustrate the proposed methodology.

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Variable selection for semiparametric errors-in-variables regression model with longitudinal data

Cited by 5 publications

References 25 publications

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2—More complex methods of adjustment and advanced topics

STRATOS guidance document on measurement error and misclassification of variables in observational epidemiology: Part 2—More complex methods of adjustment and advanced topics

Generalized Empirical Likelihood Inference in Partially Linear Errors-in-Variables Models with Longitudinal data

Bayesian Analysis for Semiparametric Mixed-E®ects Double Regression Models

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