Parametric fractional imputation is proposed as a general tool for missing data analysis. Using fractional weights, the observed likelihood can be approximated by the weighted mean of the imputedâ€¦ (More)

Finite sample properties of multiple imputation estimators under the linear regression model are studied. The exact bias of the multiple imputation variance estimator is presented. A method ofâ€¦ (More)

In two-phase sampling, the second-phase sample is often a stratified sample based on the information observed in the first-phase sample. For the total of a population characteristic, either theâ€¦ (More)

Parameter estimation with non-ignorable missing data is a challenging problem in statistics. The fully parametric approach for joint modeling of the response model and the population model canâ€¦ (More)

To reduce nonresponse bias in sample surveys, a method of nonresp s weighting adjustment is often used which consists of multiplying the sampling weight of the responde nt by the inverse of theâ€¦ (More)

Consider a finite population of N elements identified by a set of indices U = {1, 2, ..., N}. Associated with each unit i in the population there is a study variable yi and a vector xi of auxiliaryâ€¦ (More)

Calibration estimation, which can be roughly described as a method of adjusting the original design weights to incorporate the known population totals of the auxiliary variables, has become veryâ€¦ (More)

Abstract Propensity score weighting adjustment is commonly used to handle unit nonresponse. When the response mechanism is nonignorable in the sense that the response probability depends directly onâ€¦ (More)

Calibration estimation, where the sampling weights are adjusted to make certain estimators match known population totals, is commonly used in survey sampling. The generalized regression estimator isâ€¦ (More)

The authors propose a new ratio imputation method using response proba bility. Their estimator can be justified either under the response model or under the imputation mo del; it is thus doublyâ€¦ (More)