This thesis presents work on two distinct topics: multiple imputation and shrinkage
estimation. For the former topic, we provide a general theory on the model uncongeniality …

MULTIPLE IMPUTATION FROM A MULTI-PHASE INFERENCE PERSPECTIVE 1585 dence
procedure to cover more than its nominal coverage. We particul Desmond for a very helpful …

We consider two-level models where a continuous response R and continuous covariates C
are assumed missing at random. Inferences based on maximum likelihood or Bayes are …

Real-life data are almost never really real. By the time the data arrive at an investigator's
desk or disk, the raw data, however defined, have most likely gone through at least one …

This article is aimed at practitioners who plan to use Bayesian inference on multiply-imputed
datasets in settings where posterior distributions of the parameters of interest are not …

Bayesian multiple imputation (MI) has become a highly useful paradigm for handling
missing values in many settings. In this paper, I compare Bayesian MI with other methods …

Multiple imputation was first conceived as a tool that statistical agencies could use to handle
nonresponse in large-sample public use surveys. In the last two decades, the multiple …

Imputation is a common method for replacing a missing value with one or more fabricated
values. The terminology and methodology of imputation is often confusing because no …

ProQuest Dissertations Page 1 BAYESIAN PREDICTIVE INFERENCE FOR THREE TOPICS IN
SURVEY SAMPLES by Qixuan Chen A dissertation submitted in partial fulfillment of the …

Real-life data are almost never really real. By the time the data arrive at an investigator's
desk or disk, the raw data, however defined, have most likely gone through at least one" …