79 citations · 242 across the 33 of their papers we have counts for
37 papers
Pattern Graph Models for Non-monotone Missing Data with an Application to Myocardial Infarction Complications
Yanghyeon Cho, Jae Kwang Kim, Emily Berg
Non-monotone missingness is frequently encountered in practice, with intermittent dropout and re-entry in longitudinal studies as a canonical example. Under non-monotone missingnes…
Coherent and robust Bayesian inference under informative sampling via sample-level weight models
Kosuke Morikawa, Jae Kwang Kim, Won Chang
We develop two complementary Bayesian procedures for analytic inference under informative sampling. The first constructs a coherent likelihood by modeling the sample-level distribu…
Multiple bias-calibration for adjusting selection bias of non-probability samples using data integration
Zhonglei Wang, Shu Yang, Jae Kwang Kim
Valid statistical inference is challenging when the sample is subject to unknown selection bias. Data integration can be used to correct for selection bias when we have a parallel…
Robust propensity score weighting estimation under missing at random
Hengfang Wang, Jae Kwang Kim, Jeongseop Han +1
Missing data is frequently encountered in many areas of statistics. Propensity score weighting is a popular method for handling missing data. The propensity score method employs a…
An empirical likelihood approach to reduce selection bias in voluntary samples
Jae Kwang Kim, Kosuke Morikawa
We address the weighting problem in voluntary samples under a nonignorable sample selection model. Under the assumption that the sample selection model is correctly specified, we c…
Semiparametric imputation using latent sparse conditional Gaussian mixtures for multivariate mixed outcomes
Shonosuke Sugasawa, Jae Kwang Kim, Kosuke Morikawa
This paper proposes a flexible Bayesian approach to multiple imputation using conditional Gaussian mixtures. We introduce novel shrinkage priors for covariate-dependent mixing prop…
Semiparametric adaptive estimation under informative sampling
Kosuke Morikawa, Yoshikazu Terada, Jae Kwang Kim
In survey sampling, survey data do not necessarily represent the target population, and the samples are often biased. However, information on the survey weights aids in the elimina…
Maximum Likelihood Imputation
Jeongseop Han, Youngjo Lee, Jae Kwang Kim
Maximum likelihood (ML) estimation is widely used in statistics. The h-likelihood has been proposed as an extension of Fisher's likelihood to statistical models including unobserve…
Soft calibration for selection bias problems under mixed-effects models
Chenyin Gao, Shu Yang, Jae Kwang Kim
Calibration weighting has been widely used to correct selection biases in non-probability sampling, missing data, and causal inference. The main idea is to calibrate the biased sam…
Functional Calibration under Non-Probability Survey Sampling
Zhonglei Wang, Xiaojun Mao, Jae Kwang Kim
Non-probability sampling is prevailing in survey sampling, but ignoring its selection bias leads to erroneous inferences. We offer a unified nonparametric calibration method to est…
Nearest neighbor ratio imputation with incomplete multi-nomial outcome in survey sampling
Chenyin Gao, Katherine Jenny Thompson, Shu Yang +1
Nonresponse is a common problem in survey sampling. Appropriate treatment can be challenging, especially when dealing with detailed breakdowns of totals. Often, the nearest neighbo…
Statistical inference using Regularized M-estimation in the reproducing kernel Hilbert space for handling missing data
Hengfang Wang, Jae Kwang Kim
Imputation and propensity score weighting are two popular techniques for handling missing data. We address these problems using the regularized M-estimation techniques in the repro…
Survey data integration for regression analysis using model calibration
Zhonglei Wang, Hang J. Kim, Jae Kwang Kim
We consider regression analysis in the context of data integration. To combine partial information from external sources, we employ the idea of model calibration which introduces a…
Information projection approach to propensity score estimation for handling selection bias under missing at random
Hengfang Wang, Jae Kwang Kim
Propensity score weighting is widely used to improve the representativeness and correct the selection bias in the voluntary sample. The propensity score is often developed using a…
Maximum sampled conditional likelihood for informative subsampling
HaiYing Wang, Jae Kwang Kim
Subsampling is a computationally effective approach to extract information from massive data sets when computing resources are limited. After a subsample is taken from the full dat…
Maximum Entropy classification for record linkage
Danhyang Lee, Li-Chun Zhang, Jae-Kwang Kim
By record linkage one joins records residing in separate files which are believed to be related to the same entity. In this paper we approach record linkage as a classification pro…
Statistical Data Integration in Survey Sampling: A Review
Shu Yang, Jae Kwang Kim
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…
Semiparametric Imputation Using Conditional Gaussian Mixture Models under Item Nonresponse
Danhyang Lee, Jae Kwang Kim
Imputation is a popular technique for handling item nonresponse in survey sampling. Parametric imputation is based on a parametric model for imputation and is less robust against t…
An Approximate Bayesian Approach to Model-assisted Survey Estimation with Many Auxiliary Variables
Shonosuke Sugasawa, Jae Kwang Kim
Model-assisted estimation with complex survey data is an important practical problem in survey sampling. When there are many auxiliary variables, selecting significant variables as…
Doubly Robust Inference when Combining Probability and Non-probability Samples with High-dimensional Data
Shu Yang, Jae Kwang Kim, Rui Song
Non-probability samples become increasingly popular in survey statistics but may suffer from selection biases that limit the generalizability of results to the target population. W…
Imputation estimators for unnormalized models with missing data
Masatoshi Uehara, Takeru Matsuda, Jae Kwang Kim
Several statistical models are given in the form of unnormalized densities, and calculation of the normalization constant is intractable. We propose estimation methods for such unn…
Bootstrap inference for the finite population total under complex sampling designs
Zhonglei Wang, Jae Kwang Kim, Liuhua Peng
Bootstrap is a useful tool for making statistical inference, but it may provide erroneous results under complex survey sampling. Most studies about bootstrap-based inference are de…
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…