79 citations · 242 across the 33 of their papers we have counts for
7 papers · 1 filter
Combining Non-probability and Probability Survey Samples Through Mass Imputation
Jae Kwang Kim, Seho Park, Yilin Chen +1
This paper presents theoretical results on combining non-probability and probability survey samples through mass imputation, an approach originally proposed by Rivers (2007) as sam…
Accounting for model uncertainty in multiple imputation under complex sampling
Gyuhyeong Goh, Jae Kwang Kim
Multiple imputation provides an effective way to handle missing data. When several possible models are under consideration for the data, the multiple imputation is typically perfor…
Semiparametric response model with nonignorable nonresponse
Masatoshi Uehara, Jae Kwang Kim
How to deal with nonignorable response is often a challenging problem encountered in statistical analysis with missing data. Parametric model assumption for the response mechanism…
Semiparametric fractional imputation using Gaussian mixture models for handling multivariate missing data
Hejian Sang, Jae Kwang Kim
Item nonresponse is frequently encountered in practice. Ignoring missing data can lose efficiency and lead to misleading inference. Fractional imputation is a frequentist approach…
Bayesian Sparse Propensity Score Estimation for Unit Nonresponse
Hejian Sang, Gyuhyeong Goh, Jae Kwang Kim
Nonresponse weighting adjustment using propensity score is a popular method for handling unit nonresponse. However, including all available auxiliary variables into the propensity…
Integration of survey data and big observational data for finite population inference using mass imputation
Shu Yang, Jae Kwang Kim
Multiple data sources are becoming increasingly available for statistical analyses in the era of big data. As an important example in finite-population inference, we consider an im…
Sampling techniques for big data analysis in finite population inference
Jae Kwang Kim, Zhonglei Wang
In analyzing big data for finite population inference, it is critical to adjust for the selection bias in the big data. In this paper, we propose two methods of reducing the select…