12 Result(s)
within
Jae-Kwang Kim
Statistics
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Article
Statistical inference using regularized M-estimation in the reproducing kernel Hilbert space for handling missing data
Imputation is a popular technique for handling missing data. We address a nonparametric imputation using the regularized M-estimation techniques in the reproducing kernel Hilbert space. Specifically, we first ...
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Article
Open AccessCorrection to: Statistical data integration in survey sampling: a review
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Article
A calibrated Bayesian method for the stratified proportional hazards model with missing covariates
Missing covariates are commonly encountered when evaluating covariate effects on survival outcomes. Excluding missing data from the analysis may lead to biased parameter estimation and a misleading conclusion....
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Article
Open AccessStatistical data integration in survey sampling: a review
Finite population inference is a central goal in survey sampling. Probability sampling is the main statistical approach to finite population inference. Challenges arise due to high cost and increasing non-resp...
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Article
Mass imputation for two-phase sampling
Two-phase sampling is a cost-effective method of data collection using outcomedependent sampling for the second-phase sample. In order to make efficient use of auxiliary information and to improve domain estim...
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Article
Combining Survey and Non-survey Data for Improved Sub-area Prediction Using a Multi-level Model
Combining information from different sources is an important practical problem in survey sampling. Using a hierarchical area-level model, we establish a framework to integrate auxiliary information to improve ...
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Article
Analysis of inaccurate data with mixture measurement error models
Measurement error, the difference between a measured (observed) value of quantity and its true value, is perceived as a possible source of estimation bias in many surveys. To correct for such bias, a validatio...
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Chapter and Conference Paper
Bottom-Up Estimation and Top-Down Prediction: Solar Energy Prediction Combining Information from Multiple Sources
Accurately forecasting solar power using the data from multiple sources is an important but challenging problem. Our goal is to combine two different physics model forecasting outputs with real measurements fr...
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Article
A measurement error model approach to survey data integration: combining information from two surveys
Combining information from several surveys from the same target population is an important practical problem in survey sampling. The paper is motivated by work that authors undertook, sponsored by the Food and...
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Article
Semiparametric inference with a functional-form empirical likelihood
A functional-form empirical likelihood method is proposed as an alternative method to the empirical likelihood method. The proposed method has the same asymptotic properties as the empirical likelihood method ...
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Article
The factoring likelihood method for non-monotone missing data
We address the problem of parameter estimation in multivariate distributions under ignorable non-monotone missing data. The factoring likelihood method for monotone missing data, termed by Rubin (1974), is app...
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Article
Parametric fractional imputation for nonignorable missing data
Parameter estimation with missing data is a frequently encountered problem in statistics. Imputation is often used to facilitate the parameter estimation by simply applying the complete-sample estimators to th...
