Conditional likelihood estimation and efficiency comparisons in proportional odds model with missing covariates

Hsieh, Shu Hui; Lee, S. M.; Shen, Pao‐Sheng; Liu, M. F.

doi:10.1007/s10463-009-0266-9

Cited by 1 publication

(2 citation statements)

References 23 publications

(24 reference statements)

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“…Note that v 1 , …, v m denote the distinct values of V i . Hsieh et al (2010) proposed a Breslow and Cain (1988) type estimator of Θ based on the validation data set. The large‐sample theory was presented under the assumption that the observed covariates and surrogate variables are categorical.…”

Section: Review Of the Conditional Estimation Methodsmentioning

confidence: 99%

“…Based on (), the estimation score for Θ is expressed as follows: where The CEM estimator of Θ , denoted by , is the solution of . Hsieh et al (2010) compared the efficiencies of several estimators of the parameters based on different estimation approaches of in model (). They showed that the CEM estimators that use the nonparametric estimators for are more efficient than those based on the true selection probabilities.…”

Section: Review Of the Conditional Estimation Methodsmentioning

confidence: 99%

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Semiparametric Methods in the Proportional Odds Model for Ordinal Response Data with Missing Covariates

2010

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We consider the estimation problem of a proportional odds model with missing covariates. Based on the validation and nonvalidation data sets, we propose a joint conditional method that is an extension of Wang et al. (2002, Statistica Sinica 12, 555-574). The proposed method is semiparametric since it requires neither an additional model for the missingness mechanism, nor the specification of the conditional distribution of missing covariates given observed variables. Under the assumption that the observed covariates and the surrogate variable are categorical, we derived the large sample property. The simulation studies show that in various situations, the joint conditional method is more efficient than the conditional estimation method and weighted method. We also use a real data set that came from a survey of cable TV satisfaction to illustrate the approaches.

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Section: Review Of the Conditional Estimation Methodsmentioning

confidence: 99%

Section: Review Of the Conditional Estimation Methodsmentioning

confidence: 99%

Semiparametric Methods in the Proportional Odds Model for Ordinal Response Data with Missing Covariates

2010

View full text Add to dashboard Cite

show abstract

Conditional likelihood estimation and efficiency comparisons in proportional odds model with missing covariates

Abstract: Missing value, Proportional odds model, Ordinal categorical data, Conditional likelihood,

Cited by 1 publication

References 23 publications

Semiparametric Methods in the Proportional Odds Model for Ordinal Response Data with Missing Covariates

Semiparametric Methods in the Proportional Odds Model for Ordinal Response Data with Missing Covariates

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