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pro vyhledávání: '"KIM, JAE KWANG"'
Incorporating the auxiliary information into the survey estimation is a fundamental problem in survey sampling. Calibration weighting is a popular tool for incorporating the auxiliary information. The calibration weighting method of Deville and Sarnd
Externí odkaz:
http://arxiv.org/abs/2404.01076
Autor:
Kim, Jae Kwang
Survey sampling theory and methods are introduced. Sampling designs and estimation methods are carefully discussed as a textbook for survey sampling. Topics includes Horvitz-Thompson estimation, simple random sampling, stratified sampling, cluster sa
Externí odkaz:
http://arxiv.org/abs/2401.07625
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 probability sample from the same population with some common measureme
Externí odkaz:
http://arxiv.org/abs/2307.11651
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 response propensity model, but correct specification of the statistica
Externí odkaz:
http://arxiv.org/abs/2306.15173
Autor:
Kim, Jae Kwang, Morikawa, Kosuke
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 can compute a consistent estimator of the model parameter and construct
Externí odkaz:
http://arxiv.org/abs/2211.02998
This paper proposes a flexible Bayesian approach to multiple imputation using conditional Gaussian mixtures. We introduce novel shrinkage priors for covariate-dependent mixing proportions in the mixture models to automatically select the suitable num
Externí odkaz:
http://arxiv.org/abs/2208.07535
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 elimination of selection bias. The Horvitz-Thompson estimator is a well-known
Externí odkaz:
http://arxiv.org/abs/2208.06039
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 unobserved latent variables of recent interest. Its advantage is that the joint
Externí odkaz:
http://arxiv.org/abs/2207.09891
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 sample to the benchmark by adjusting the subject weights. However, hard c
Externí odkaz:
http://arxiv.org/abs/2206.01084