arXiv subjects

Amy Herring

Publications and source records attributed to Amy Herring.

3 recordsLinked to original sources

arXiv2020

Supervised Robust Profile Clustering

In many studies, dimension reduction methods are used to profile participant characteristics. For example, nutrition epidemiologists often use latent class models to characterize dietary patterns. One challenge with such approaches is understanding subtle variations in patterns across subpopulations. Robust Profile Clustering (RPC) provides a dual flexible clustering model, where participants may cluster at two levels: (1) globally, where participants are clustered according to behaviors shared across an overall population, and (2) locally, where individual behaviors can deviate and cluster in subpopulations. We link clusters to a health outcome using a joint model. This model is used to derive dietary patterns in the United States and evaluate case proportion of orofacial clefts. Using dietary consumption data from the 1997-2009 National Birth Defects Prevention Study, a population-based case-control study, we determine how maternal dietary profiles are associated with an orofacial cleft among offspring. Results indicated that mothers who consumed a high proportion of fruits and vegetables compared to meats, such as chicken and beef, had lower odds delivering a child with an orofacial cleft defect.

stat.AP↗

arXiv2018

Robust Clustering with Subpopulation-specific Deviations

The National Birth Defects Prevention Study (NBDPS) is a case-control study of birth defects conducted across 10 U.S. states. Researchers are interested in characterizing the etiologic role of maternal diet, collected using a food frequency questionnaire. Because diet is multi-dimensional, dimension reduction methods such as cluster analysis are often used to summarize dietary patterns. In a large, heterogeneous population, traditional clustering methods, such as latent class analysis, used to estimate dietary patterns can produce a large number of clusters due to a variety of factors, including study size and regional diversity. These factors result in a loss of interpretability of patterns that may differ due to minor consumption changes. Based on adaptation of the local partition process, we propose a new method, Robust Profile Clustering, to handle these data complexities. Here, participants may be clustered at two levels: (1) globally, where women are assigned to an overall population-level cluster via an overfitted finite mixture model, and (2) locally, where regional variations in diet are accommodated via a beta-Bernoulli process dependent on subpopulation differences. We use our method to analyze the NBDPS data, deriving pre-pregnancy dietary patterns for women in the NBDPS while accounting for regional variability.

stat.ME↗

arXiv2013

Bayesian factorizations of big sparse tensors

It has become routine to collect data that are structured as multiway arrays (tensors). There is an enormous literature on low rank and sparse matrix factorizations, but limited consideration of extensions to the tensor case in statistics. The most common low rank tensor factorization relies on parallel factor analysis (PARAFAC), which expresses a rank $k$ tensor as a sum of rank one tensors. When observations are only available for a tiny subset of the cells of a big tensor, the low rank assumption is not sufficient and PARAFAC has poor performance. We induce an additional layer of dimension reduction by allowing the effective rank to vary across dimensions of the table. For concreteness, we focus on a contingency table application. Taking a Bayesian approach, we place priors on terms in the factorization and develop an efficient Gibbs sampler for posterior computation. Theory is provided showing posterior concentration rates in high-dimensional settings, and the methods are shown to have excellent performance in simulations and several real data applications.

stat.ME↗