We consider the estimation of cell probabilities in a two-way contingency table where the two-dimensional categorical data have nonrespondents imputed by using a conditional hot deck imputation method. Under simple random sampling, we establish asymptotic normality of cell probability estimators based on imputed data and derive explicitly the form of their asymptotic covariance matrix, which can be used for large sample inference. We also show that estimators based on imputed data are more efficient than those obtained by ignoring nonrespondents and re-weighting when the proportion of nonrespondents is large. The results are extended to stratified sampling, under imputation, within each stratum or across strata. Two types of asymptotics are...
Contingency tables can be parametrized by probabilities of each cell in a multinomial sampling. Thes...
Dealing with data files statisticians often have to consider the problem of missing data due both to...
We propose an algorithm modifying a popular exact conditional test involving the goodness-of-fit of ...
Abstract: We consider the estimation of cell probabilities in a two-way contingency table where the ...
iAbstract We consider estimating the cell probabilities and testing hypotheses in a two-way continge...
We describe and illustrate approaches to Bayesian inference in partially observed contingency tables...
This thesis is mainly concerned with the estimation of conditional probabilities in two-way continge...
We propose Bayesian methods with five types of priors to estimate cell probabilities in an incomplet...
Hot deck imputation is a procedure in which missing items are replaced with values from respondents....
This article studies Bayesian analysis of contingency tables (or multinomial data) where the cell co...
<div><p>To many, the foundations of statistical inference are cryptic and irrelevant to routine stat...
Fractional hot deck imputation, considered in Fuller and Kim (2005), is extended to multivariate mis...
A Monte Carlo exact conditional test of quasi-independence in two-way incomplete contingency tables ...
Abstract. Statistical methods for disclosure limitation (or control) have seen coupling of tools fro...
We propose new sequential importance sampling methods for sampling contingency tables with fixed mar...
Contingency tables can be parametrized by probabilities of each cell in a multinomial sampling. Thes...
Dealing with data files statisticians often have to consider the problem of missing data due both to...
We propose an algorithm modifying a popular exact conditional test involving the goodness-of-fit of ...
Abstract: We consider the estimation of cell probabilities in a two-way contingency table where the ...
iAbstract We consider estimating the cell probabilities and testing hypotheses in a two-way continge...
We describe and illustrate approaches to Bayesian inference in partially observed contingency tables...
This thesis is mainly concerned with the estimation of conditional probabilities in two-way continge...
We propose Bayesian methods with five types of priors to estimate cell probabilities in an incomplet...
Hot deck imputation is a procedure in which missing items are replaced with values from respondents....
This article studies Bayesian analysis of contingency tables (or multinomial data) where the cell co...
<div><p>To many, the foundations of statistical inference are cryptic and irrelevant to routine stat...
Fractional hot deck imputation, considered in Fuller and Kim (2005), is extended to multivariate mis...
A Monte Carlo exact conditional test of quasi-independence in two-way incomplete contingency tables ...
Abstract. Statistical methods for disclosure limitation (or control) have seen coupling of tools fro...
We propose new sequential importance sampling methods for sampling contingency tables with fixed mar...
Contingency tables can be parametrized by probabilities of each cell in a multinomial sampling. Thes...
Dealing with data files statisticians often have to consider the problem of missing data due both to...
We propose an algorithm modifying a popular exact conditional test involving the goodness-of-fit of ...