Weighted Semiparametric Estimation in Regression Analysis with Missing Covariate Data

Wang, C. Y.; Wang, Suojin; Zhao, Lueping; Ou, Shyh-Tyan

doi:10.1080/01621459.1997.10474004

Cited by 98 publications

(77 citation statements)

References 37 publications

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“…Either way, the identification problem is solved and efficiency of estimation becomes the central matter of concern to statisticians. See, for example, Little (1992), Robins, Rotnitzky, and Zhao (1994), and Wang, Wang, Zhao, and Ou (1997).…”

Section: Introductionmentioning

confidence: 99%

Nonparametric Analysis of Randomized Experiments with Missing Covariate and Outcome Data

Horowitz

Manski

2000

Journal of the American Statistical Association

190

273

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Analysis of randomized experiments with missing covariate and outcome data is problematic because the population parameters of interest are not identified unless one makes untestable assumptions about the distribution of the missing data. This paper shows how population parameters can be bounded without making untestable distributional assumptions. Bounds are also derived under the assumption that covariate data are missing completely at random. In each case the bounds are sharp; they exhaust all of the information that is available given the data and the maintained assumptions. The bounds are illustrated with applications to data obtained from a clinical trial and data relating family structure to the probability that a youth graduates from high school.

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Section: Introductionmentioning

confidence: 99%

Nonparametric Analysis of Randomized Experiments with Missing Covariate and Outcome Data

Horowitz

Manski

2000

Journal of the American Statistical Association

190

273

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“…Condition 5 provides that the parametric weights are asymptotically consistent and is used to understand the asymptotic variance of the weights. Condition 6 are standard conditons for kernel smoothing estimators and similar to those used by Chen, Wan and Zhou (2015) and Wang et al (1997).…”

Section: Condition 3 (Condition On the Nonlinear Functions) Formentioning

confidence: 99%

“…The nonparametric approach follows the work of Wang et al (1997) for linear mean regression and Chen, Wan and Zhou (2015) for linear quantile regression by using a kernel smoother (Nadaraya (1964);Watson (1964)) as an estimator of π i0 . The nonparametric estimator of π i0 is defined as…”

Section: Quantile Regression With Missing Covariatesmentioning

confidence: 99%

Variable selection for additive partial linear quantile regression with missing covariates

Sherwood

2016

Journal of Multivariate Analysis

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Abstract:The standard quantile regression model assumes a linear relationship at the quantile of interest and that all variables are observed. These assumptions are relaxed by considering a partial linear model with missing covariates. A weighted objective function using inverse probability weighting can be used to remove the potential bias caused by missing data. Estimators using parametric and nonparametric estimates of the probability an observation has fully observed covariates are examined. A penalized and weighted objective function using the nonconvex penalties MCP or SCAD is used for variable selection of the linear terms in the presence of missing data. Assuming the missing data problems remains a low dimensional problem the penalized estimator has the oracle property including cases where p >> n. Theoretical challenges include handling missing data and partial linear models while working with a nonsmooth loss function and a nonconvex penalty function. The performance of the method is evaluated using Monte Carlo simulations and the methods are applied to model amount of time sober for patients leaving a rehabilitation center.

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“…Due to the importance of the missing mechanisms, the estimation of the selection probability has been of great concern. For instance, Rosenbaum and Rubin (1983) and Robins et al (1994) proposed a parametric estimation method, whereas Wang et al (1997) and Wang and Wang (2001) proposed a nonparametric estimation method. Many techniques can be applied to estimate the selection probability provided that the condition that the estimate of selection probability π∈ [0, 1] holds.…”

Section: Some Important Concepts Of Missing Datamentioning

confidence: 99%

“…The proposed estimating method was a Horvitz and Thompson (1952)-type weighted estimating method where the selection probability was π(Y, V) = P(δ = 1|Y,X, V). Following Wang et al (1997) and Reilly and Pepe (1995), Lukusa et al (2016) …”

Section: Zero-inflated Poisson Modelsmentioning

confidence: 99%

Review of Zero-Inflated Models with Missing Data

Lukusa¹,

Lee²,

Li³

2017

Current Research in Biostatistics

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Abstract:The literature of count regression models covers a large scope of studies and applications that implemented simple and standard models for count response variables by using Poisson regression models, binomial regression models, negative binomial regression models, geometric regression models, or generalized Poisson regression models. These regression models have received considerable attention in various situations. Nevertheless in many fields, the distribution of the count response variable may display a feature of excess zeros for which the aforementioned regression models may fail to provide an adequate fit. To remedy this handicap, a class of distributions known as zero-inflated models is considered as the most appropriate approach for dealing properly with this issue of excess zeros. In addition to the zero-inflated problem, it happens quite often that the sample data sets under investigation are not completely observed. This refers to the missing data problem. In this study, our primary interest is in reviewing studies that considered simultaneously the missing data problem and the zero-inflated feature in modeling zero-inflated data. Moreover, we discuss their methodologies and results and some potential directions of the future research.

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Weighted Semiparametric Estimation in Regression Analysis with Missing Covariate Data

Cited by 98 publications

References 37 publications

Nonparametric Analysis of Randomized Experiments with Missing Covariate and Outcome Data

Nonparametric Analysis of Randomized Experiments with Missing Covariate and Outcome Data

Variable selection for additive partial linear quantile regression with missing covariates

Review of Zero-Inflated Models with Missing Data

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