Abstracts Only

ENAR 2015SPRING MEETING With IMS & Sections of ASA MARCH 15 –18 Hyatt Regency Miami Miami, FL ABSTRACTS ENAR 2015 dimensions are found if the excess zeroes are Abstracts & Poster accounted for correctly. A mixture model, i.e. hybrid latent class latent factor model, is used to assess Presentations the dimensionality of the underlying subgroup corresponding to those who come from the part of the population with some measurable trait. Implications 1. POSTERS: of the findings are discussed, in particular regard- Latent Variable and ing the potential for different findings in community Mixture Models versus patient populations. email: [email protected] 1a. ASSESSMENT OF DIMENSIONALITY CAN BE DISTORTED BY TOO MANY ZEROES: 1b. LOCAL INFLUENCE DIAGNOSTICS AN EXAMPLE FROM PSYCHIATRY AND FOR HIERARCHICAL COUNT DATA A SOLUTION USING MIXTURE MODELS MODELS WITH OVERDISPERSION AND EXCESS ZEROS Melanie M. Wall*, Columbia University Trias Wahyuni Rakhmawati*, Universiteit Hasselt Irini Moustaki, London School of Economics Geert Molenberghs, Universiteit Hasselt and Common methods for determining the number of Katholieke Universiteit Leuven latent dimensions underlying an item set include eigenvalue analysis and examination of fit statistics Geert Verbeke, Katholieke Universiteit Leuven for factor analysis models with varying number of and Universiteit Hasselt factors. Given a set of dichotomous items, we will Christel Faes, Universiteit Hasselt and Katholieke demonstrate that these empirical assessments of Universiteit Leuven dimensionality are likely to underestimate the number of dimensions when there is a preponderance We consider models for hierarchically observed of individuals in the sample with all zeros as their and possibly overdispersed count data, that in responses, i.e. all incorrect answers. A simulated addition allow for excess zeros. The model extends data experiment is conducted to demonstrate this the Poisson-normal generalized linear mixed phenomena. An example is shown from psychiatry model by including gamma random effects to assessing the dimensionality of a social anxiety accommodate overdispersion. Excess zeros are disorder battery where only one latent dimension handled using either a zero-inflation or a hurdle is found if the full sample is used, while three latent component. These models were studied by Kas- sahun et al. (2014). While flexible, the model is 2 ENAR 2015 | Spring Meeting | March 15–18 quite elaborate in parametric specifica- may experience zero lateral competition, mixtures. In a hurdle, the second compo- tion and therefore model assessment for example. To handle this problem we nent follows a zero-truncated distribution, is imperative. We derive local influence have developed an R package [fitmixst4] while in a zero-inflated it follows a count measures to detect influential subjects, that accommodates collapse clusters by distribution with positive probability of i.e., subjects who have undue influence constraining the variance of the cluster to generating zeroes. However models on either the fit of the model as a whole, be below a fixed upper bound. We apply that deal with this problem are not well or on specific important sub-vectors. the R package to retrospectively describe developed for zero-heavy continuous out- The latter include the fixed effects for clusters of trees that died and remained comes. Thus, in this paper, we propose the Poisson component and for the alive over series of five-year follow-up and evaluate a two-component Weibull excess zeros component, the variance periods from 9,292 beech trees in a mixture model for effectively dealing with components for the normal random Bavarian long-term forest research plot the problem. We use simulated and real effects, and the parameters describing network. Heat maps of the mixture den- data from a randomized-controlled-trial the gamma random effects. Interpretable sity ratios corresponding to dead versus (RCT) to demonstrate its application and influence components are derived. The alive trees are assessed as a potential make comparisons with other methods methods are illustrated using data from prediction tool for forest mortality for via statistical information and mean a longitudinal clinical trial in patients with future forest management. squared error criteria. Our results show dermatophyte onychomycosis. that the two component Weibull mixture email: [email protected] model is superior for modeling zero- email: triaswahyuni.rakhmawati@uhass heavy continuous data. 1d. WEIBULL MIXTURE REGRESSION email: [email protected] 1c. FINITE MULTIVARIATE MIXTURES FOR ZERO-HEAVY CONTINUOUS OF SKEW-T DISTRIBUTIONS WITH SUBSTANCE USE OUTCOMES COLLAPSE CLUSTERS WITH 1e. MODEL-FREE ESTIMATION OF Mulugeta Gebregziabher, Medical APPLICATION IN FORESTRY TIME-VARYING CORRELATION University of South Carolina COEFFICIENTS AND THEIR CON- Josef Hoefler*, Technical University Delia Voronca*, Medical University FIDENCE INTERVALS WITH AN Munich of South Carolina APPLICATION TO fMRI DATA Donna Pauler Ankerst, Technical Abeba Teklehaimanot, Medical Maria A. Kudela*, Indiana University University Munich University of South Carolina Richard M. Fairbanks School of Public Finite mixtures of skew-t distributions Elizabeth J. Santa Ana, Ralph Health, Indianapolis offer a flexible framework for model- H. Johnson Department of Veterans Jaroslaw Harezlak, Indiana University ing non-Normal data, in particular data Affairs Medical Center Richard M. Fairbanks School of Public that possess skewness, multiple clus- Health, Indianapolis ters and/or outliers. Such models have Outcomes with preponderance of zero been extended to multivariate data, with values are ubiquitous in data that arise Martin Lindquist, Johns Hopkins Bloom- detailed Expectation-Maximization (EM) from studies of addictive disorders. This berg School of Public Health is known to lead to violation of standard algorithms for fitting. A practical problem One of main interests in fMRI (functional assumptions in parametric inference and that arises with these models is the exis- magnetic resonance imaging) research enhances the risk of misleading conclu- tence of collapsed clusters, which reside is the study of associations between sions unless managed properly. Two of on smaller-dimensional planes than the time series from different brain regions, the most popular models used to handle remaining clusters. This occurs for certain so called functional connectivity (FC). this issue for count outcomes are hurdle competition indices measured in trees Recently, it has become increasingly and zero-inflated models. Both models – all trees in a particular plot or forest important to assess dynamic changes can be expressed as two-component Abstracts 3 in FC, both during resting state and with men (MSM) in 22 social networks advance is problematic. If we put a prior task-based fMRI experiments, as this in Ghana, we consider a zero and one on T, then the distribution on the latent is thought to provide the information inflated beta regression with mixed variables is a mixture of distributions on needed to better understand the brain’s effects. The proposed model addresses spaces of different dimensions, and esti- inner workings. Currently, the most com- the issue of abundance of zeros and mating this mixture distribution by MCMC mon approach to estimate these dynamic ones in the relative frequency of condom is very challenging. We present a variant changes is by computing the correlation uses, and also the dependence of the of the Metropolis-Hastings algorithm that coefficient between time series within a relative frequencies of MSM from the can be used to effectively estimate this sliding-window. However, one of the dis- same social network. We achieve this by mixture distribution and in particular the advantages of this method is that it tends linking the mixed-effects regression to the posterior distribution on the number of to overestimate the association between beta distribution mean through a logit link topics. We also provide theory to justify the time series obtained from different function while keeping the other positive that our algorithm can correctly estimate brain regions (Lindquist et al. 2014). Here parameter constant. We also discuss the true posterior distribution of T given we propose a new approach for estimat- extension of the proposed model by also the words. We evaluate the performance ing time-varying FC using the correlation relating the positive parameter to the of our algorithm on synthetic data, with a between two time series and provide mixed-effects through a log link. comparison with the existing method. We valid confidence bands for this estimator. also give an illustration on a collection of email: [email protected] We propose an algorithm based on the articles from Wikipedia. sliding-window approach which utilizes email: [email protected] the multivariate linear process bootstrap. 1g. INFERENCE FOR THE NUMBER OF Both numerical results and an application TOPICS IN THE LATENT DIRICH- to fMRI study of alcoholism risk factors LET ALLOCATION MODEL VIA A 1h. APPLYING A STOCHASTIC VOLA- will be presented. PSEUDO-MARGINAL METROPO- TILITY MODEL TO US STOCK email: [email protected] LIS-HASTINGS ALGORITHM MARKETS WITH A UMM UNDER- GRADUATE STUDENT Zhe Chen*, University of Florida Jong-Min Kim*, University of Minnesota, Hani Doss, University of Florida 1f. ZERO-AND-ONE INFLATED BETA Morris REGRESSION WITH MIXED Latent Dirichlet Allocation (LDA) is a Li Qin, University of Minnesota,

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