10,418 research outputs found
A pseudo empirical likelihood approach for stratified samples with nonresponse
Nonresponse is common in surveys. When the response probability of a survey
variable depends on through an observed auxiliary categorical variable
(i.e., the response probability of is conditionally independent of
given ), a simple method often used in practice is to use categories as
imputation cells and construct estimators by imputing nonrespondents or
reweighting respondents within each imputation cell. This simple method,
however, is inefficient when some categories have small sizes and ad hoc
methods are often applied to collapse small imputation cells. Assuming a
parametric model on the conditional probability of given and a
nonparametric model on the distribution of , we develop a pseudo empirical
likelihood method to provide more efficient survey estimators. Our method
avoids any ad hoc collapsing small categories, since reweighting or
imputation is done across categories. Asymptotic distributions for
estimators of population means based on the pseudo empirical likelihood method
are derived. For variance estimation, we consider a bootstrap procedure and its
consistency is established. Some simulation results are provided to assess the
finite sample performance of the proposed estimators.Comment: Published in at http://dx.doi.org/10.1214/07-AOS578 the Annals of
Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical
Statistics (http://www.imstat.org
Developing the Supply Chain Capabilities of Chinese Small and Medium Size Textile & Apparel Manufacturers with E-business Initiatives
Moderate deviations in Poisson approximation: a first attempt
Poisson approximation using Stein's method has been extensively studied in
the literature. The main focus has been on bounding the total variation
distance. This paper is a first attempt on moderate deviations in Poisson
approximation for right-tail probabilities of sums of dependent indicators. We
obtain results under certain general conditions for local dependence as well as
for size-bias coupling. These results are then applied to independent
indicators, 2-runs, and the matching problem.Comment: 21 page
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