2,112 research outputs found
Bayesian Inference under Cluster Sampling with Probability Proportional to Size
Cluster sampling is common in survey practice, and the corresponding
inference has been predominantly design-based. We develop a Bayesian framework
for cluster sampling and account for the design effect in the outcome modeling.
We consider a two-stage cluster sampling design where the clusters are first
selected with probability proportional to cluster size, and then units are
randomly sampled inside selected clusters. Challenges arise when the sizes of
nonsampled cluster are unknown. We propose nonparametric and parametric
Bayesian approaches for predicting the unknown cluster sizes, with this
inference performed simultaneously with the model for survey outcome.
Simulation studies show that the integrated Bayesian approach outperforms
classical methods with efficiency gains. We use Stan for computing and apply
the proposal to the Fragile Families and Child Wellbeing study as an
illustration of complex survey inference in health surveys
Covariate-adaptive randomization inference in matched designs
It is common to conduct causal inference in matched observational studies by
proceeding as though treatment assignments within matched sets are assigned
uniformly at random and using this distribution as the basis for inference.
This approach ignores observed discrepancies in matched sets that may be
consequential for the distribution of treatment, which are succinctly captured
by within-set differences in the propensity score. We address this problem via
covariate-adaptive randomization inference, which modifies the permutation
probabilities to vary with estimated propensity score discrepancies and avoids
requirements to exclude matched pairs or model an outcome variable. We show
that the test achieves type I error control arbitrarily close to the nominal
level when large samples are available for propensity score estimation. We
characterize the large-sample behavior of the new randomization test for a
difference-in-means estimator of a constant additive effect. We also show that
existing methods of sensitivity analysis generalize effectively to
covariate-adaptive randomization inference. Finally, we evaluate the empirical
value of covariate-adaptive randomization procedures via comparisons to
traditional uniform inference in matched designs with and without propensity
score calipers and regression adjustment using simulations and analyses of
genetic damage among welders and right-heart catheterization in surgical
patients.Comment: 41 pages, 8 figure
Replication : an approach to the analysis of data from complex surveys
Development and evaluation of a replication technique for estimating variance.[By Philip J. McCarthy].Public Health Service publication, no. 1000-Series 2, no. 14.Bibliography: p. 31-32.196
- …