Search results for "Missing covariates"

showing 3 items of 3 documents

Regression with imputed covariates: A generalized missing-indicator approach

2011

A common problem in applied regression analysis is that covariate values may be missing for some observations but imputed values may be available. This situation generates a trade-off between bias and precision: the complete cases are often disarmingly few, but replacing the missing observations with the imputed values to gain precision may lead to bias. In this paper, we formalize this trade-off by showing that one can augment the regression model with a set of auxiliary variables so as to obtain, under weak assumptions about the imputations, the same unbiased estimator of the parameters of interest as complete-case analysis. Given this augmented model, the bias-precision trade-off may the…

Economics and EconometricsApplied MathematicsRegression analysisMissing dataRegressionSet (abstract data type)Reduction (complexity)Economic dataBias of an estimatorStatisticsCovariateMissing covariates ImputationsBias precision trade-off Model reduction Model averaging BMI and incomeEconometricsStatistics::MethodologyC12C13C19Missing covariatesImputationsBias-precision trade-offModel reductionModel averagingBMI and incomeMathematics
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Model averaging estimation of generalized linear models with imputed covariates

2015

a b s t r a c t We address the problem of estimating generalized linear models when some covariate values are missing but imputations are available to fill-in the missing values. This situation generates a bias-precision trade- off in the estimation of the model parameters. Extending the generalized missing-indicator method proposed by Dardanoni et al. (2011) for linear regression, we handle this trade-off as a problem of model uncertainty using Bayesian averaging of classical maximum likelihood estimators (BAML). We also propose a block model averaging strategy that incorporates information on the missing-data patterns and is computationally simple. An empirical application illustrates our…

Generalized linear modelEconomics and EconometricsApplied MathematicsSettore SECS-P/05 - EconometriaEstimatorMissing dataGeneralized linear mixed modelModel averaging Bayesian averaging of maximum likelihood destimators Generalized linear models Missing covariates Generalized missing-indicator method shareHierarchical generalized linear modelStatisticsLinear regressionCovariateApplied mathematicsGeneralized estimating equationMathematics
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A Generalized Missing-Indicator Approach to Regression with Imputed Covariates

2011

We consider estimation of a linear regression model using data where some covariate values are missing but imputations are available to fill in the missing values. This situation generates a tradeoff between bias and precision when estimating the regression parameters of interest. Using only the subsample of complete observations does not cause bias but may imply a substantial loss of precision because the complete cases may be too few. On the other hand, filling in the missing values with imputations may cause bias. We provide the new Stata command gmi, which handles such tradeoff by using either model reduction or Bayesian model averaging techniques in the context of the generalized miss…

Settore SECS-P/05Computer scienceSettore SECS-P/05 - EconometriaMissing dataBayesian inferenceRegressiongmi missing covariates imputation bias–precision tradeoff model reduction model averagingMathematics (miscellaneous)CovariateLinear regressionStatisticsEconometricsStatistics::MethodologyImputation (statistics)Settore SECS-P/01 - Economia PoliticaThe Stata Journal: Promoting communications on statistics and Stata
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