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20042026
most citedSoft calibration for selection bias problems under mixed-effects models

8 citations · 16 across the 11 of their papers we have counts for

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Showing 2019Show all

5 papers · 1 filter

stat.ME2019

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…

stat.ME2019

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…

stat.ME2019

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…

stat.ML2019

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…

math.ST2019

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…