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Full-Text Articles in Statistical Models
Latent Class Models For At-Risk Populations, Shuaimin Kang
Latent Class Models For At-Risk Populations, Shuaimin Kang
Doctoral Dissertations
Clustering Network Tree Data From Respondent-Driven Sampling With Application to Opioid Users in New York City There is great interest in finding meaningful subgroups of attributed network data. There are many available methods for clustering complete network. Unfortunately, much network data is collected through sampling, and therefore incomplete. Respondent-driven sampling (RDS) is a widely used method for sampling hard-to-reach human populations based on tracing links in the underlying unobserved social network. The resulting data therefore have tree structure representing a sub-sample of the network, along with many nodal attributes. In this paper, we introduce an approach to adjust mixture models …
Bayesian Methods For The Assessment Of Reporting Errors For Data-Sparse Population-Periods With Applications To Estimating Mortality, Emily Peterson
Bayesian Methods For The Assessment Of Reporting Errors For Data-Sparse Population-Periods With Applications To Estimating Mortality, Emily Peterson
Doctoral Dissertations
Population level mortality data is often subject to substantial reporting errors due to misclassification of cause of death, misclassification of death status, or age reporting errors. Accuracy of error-prone data sources can be assessed by comparing such data to gold standard data for the same population-period. We present Bayesian methods for assessing the extent of reporting errors across different population-periods and generalizing those to settings where gold-standard data are lacking. Firstly, we investigate misclassification errors of maternal cause of death reporting in civil registration vital statistics data. We use a Bayesian hierarchical bivariate random-walk model to estimate country-year specific sensitivity …