Marcos O. Prates

Relative risk estimates from spatial and space-time scan statistics: are they biased?

The purely spatial and space-time scan statistics have been successfully used by many scientists to detect and evaluate geographical disease clusters. Although the scan statistic has high power in correctly identifying a cluster, no study has considered the estimates of the cluster relative risk in the detected cluster. In this paper, we evaluate whether there is any bias on these estimated relative risks. Intuitively, one may expect that the estimated relative risks has upward bias, because the scan statistic cherry picks high rate areas to include in the cluster. We show that this intuition is correct for clusters with low statistical power, but with medium to high power, the bias becomes negligible. The same behavior is not observed for the prospective space-time scan statistic, where there is an increasing conservative downward bias of the relative risk as the power to detect the cluster increases.

Likelihood-based inference for Tobit confirmatory factor analysis using the multivariate Student-t distribution

Factor analysis models have been one of the most popular multivariate methods for data analysis among psychometricians, behavioral and educational researchers. But these models, originally developed for normally distributed observed variables, can be seriously affected by the presence of influential observations and censored data. Motivated by this situation, in this paper we propose a likelihood-based estimation for a multivariate Tobit confirmatory factor analysis model using the Student-t distribution (t-TCFA model). An EM-type algorithm is developed for computing the maximum likelihood estimates, obtaining as a byproduct the standard errors of the fixed effects and the exact likelihood value. Unlike other approaches proposed in the literature, our exact EM-type algorithm uses closed form expressions at the E-step based on the first two moments of a truncated multivariate Student-t distribution with the advantage that these expressions can be computed using standard statistical software. The performance of the proposed methods is illustrated through a simulation study and the analysis of a real dataset of early grade reading assessment test scores.

Inference on dynamic models for non-Gaussian random fields using INLA

Robust time series analysis is an important subject in statistical modeling. Models based on Gaussian distribution are sensitive to outliers, which may imply in a significant degradation in estimation performance as well as in prediction accuracy. State-space models, also referred as Dynamic Models, is a very useful way to describe the evolution of a time series variable through a structured latent evolution system. Integrated Nested Laplace Approximation (INLA) is a recent approach proposed to perform fast Bayesian inference in Latent Gaussian Models which naturally comprises Dynamic Models. We present how to perform fast and accurate non-Gaussian dynamic modeling with INLA and show how these models can provide a more robust time series analysis when compared with standard dynamic models based on Gaussian distributions. We formalize the framework used to fit complex non-Gaussian space-state models using the R package INLA and illustrate our approach in both a simulation study and on the brazilian homicide rate dataset.

Assessing intervention efficacy on high-risk drinkers using generalized linear mixed models with a new class of link functions

Unhealthy alcohol use is one of the leading causes of morbidity and mortality in the United States. Brief interventions with high-risk drinkers during an emergency department (ED) visit are of great interest due to their possible efficacy and low cost. In a collaborative study with patients recruited at 14 academic ED across the United States, we examined the self-reported number of drinks per week by each patient following the exposure to a brief intervention. Count data with overdispersion have been mostly analyzed with generalized linear mixed models (GLMMs), of which only a limited number of link functions are available. Different choices of link function provide different fit and predictive power for a particular dataset. We propose a class of link functions from an alternative way to incorporate random effects in a GLMM, which encompasses many existing link functions as special cases. The methodology is naturally implemented in a Bayesian framework, with competing links selected with Bayesian model selection criteria such as the conditional predictive ordinate (CPO). In application to the ED intervention study, all models suggest that the intervention was effective in reducing the number of drinks, but some new models are found to significantly outperform the traditional model as measured by CPO. The validity of CPO in link selection is confirmed in a simulation study that shared the same characteristics as the count data from high-risk drinkers. The dataset and the source code for the best fitting model are available in Supporting Information.

Assessing conditions influencing the longitudinal distribution of exotic brown trout (Salmo trutta) in a mountain stream: a spatially-explicit modeling approach

Trout species often segregate along elevational gradients, yet the mechanisms driving this pattern are not fully understood. On the Logan River, Utah, USA, exotic brown trout (Salmo trutta) dominate at low elevations but are near-absent from high elevations with native Bonneville cutthroat trout (Onchorhynchus clarkii utah). We used a spatially-explicit Bayesian modeling approach to evaluate how abiotic conditions (describing mechanisms related to temperature and physical habitat) as well as propagule pressure explained the distribution of brown trout in this system. Many covariates strongly explained redd abundance based on model performance and coefficient strength, including average annual temperature, average summer temperature, gravel availability, distance from a concentrated stocking area, and anchor ice-impeded distance from a concentrated stocking area. In contrast, covariates that exhibited low performance in models and/or a weak relationship to redd abundance included reach-average water depth, stocking intensity to the reach, average winter temperature, and number of days with anchor ice. Even if climate change creates more suitable summer temperature conditions for brown trout at high elevations, our findings suggest their success may be limited by other conditions. The potential role of anchor ice in limiting movement upstream is compelling considering evidence suggesting anchor ice prevalence on the Logan River has decreased significantly over the last several decades, likely in response to climatic changes. Further experimental and field research is needed to explore the role of anchor ice, spawning gravel availability, and locations of historical stocking in structuring brown trout distributions on the Logan River and elsewhere.

Transformed Gaussian Markov random fields and spatial modeling of species abundance

Abstract Gaussian random field and Gaussian Markov random field have been widely used to accommodate spatial dependence under the generalized linear mixed models framework. To model spatial count and spatial binary data, we present a class of transformed Gaussian Markov random fields, constructed by transforming the margins of a Gaussian Markov random field to desired marginal distributions that accommodate asymmetry and heavy tail, as needed in many empirical circumstances. The Gaussian copula that characterizes the dependence structure facilitates inferences and applications in modeling spatial dependence. This construction leads to new models such as gamma or beta Markov fields with Gaussian copulas, that are used to model Poisson intensities or Bernoulli rates in hierarchical spatial analyses. The method is naturally implemented in a Bayesian framework. To illustrate our methodology, abundances of variety of gastropod species were collected as counts or presence versus absence from a network of spatial locations in the Luquillo Mountains of Puerto Rico. Gastropods are of considerable ecological importance in terrestrial ecosystems because of their species richness, abundances, and critical roles in ecosystem processes such as decomposition and nutrient cycling. The new models outperform the traditional models based on Bayesian model comparison with conditional predictive ordinate. The validity of Bayesian inferences and model selection were assessed through simulation studies for both spatial Poisson regression and spatial Bernoulli regression.

Contrasting People’s Attitudes Towards Self-disclosure in Online Social Networks and Face-to-Face Settings

While Online Social Networks (OSNs) allow closer interaction among their users, they trigger users’ privacy concerns related to self-disclosure. The reason for is that individual’s information and online activities are easily traced, collected and stored in OSNs when compared to face-to-face settings. In this context, this work aims at understanding how similar or different are people’s concerns and attitudes about self-disclosure in both OSNs and face-to-face settings, focusing on investigating what information people consider personal and with whom they feel comfortable in sharing which pieces of their information within these two contexts. Our analysis shows that people associate different degrees of “personalness” to different pieces of information. Furthermore, our data shows that people have different attitudes regarding which information they share in which world and how they share it. This indicates that people understand that OSN and face-to-face settings require different behaviors and that they take into account how personal they perceive a piece of information to be, in deciding if and how to share it.

Individual and Organizational Predictors of Pediatric Psychiatric Inpatient Admission in Connecticut Hospitals: A 6 Month Secondary Analysis

The objective of this study is to test the hypotheses that bipolar disorders or depressive disorders, minority status, and the presence of pediatric inpatient psychiatric unit will be individual predictors of pediatric psychiatric inpatient admission, and to provide a model that will evaluate which individual and organizational characteristics predict pediatric psychiatric inpatient. For this purpose, a secondary analysis of the medical records of 1,520 pediatric patient visits between January 1, 2008 and June 30, 2008, was conducted using univariate and multivariate logistic regression. Independent predictors of pediatric psychiatric inpatient admission were presence of bipolar and depressive disorders, greater average daily census, and increasing operating margin. Minority status was a significant predictor of not being admitted, as was presence of an anxiety disorder, greater total margin and older age. The results indicate that both individual and organizational factors impact disposition outcomes in particular subsets of pediatric patients who present to emergency departments for psychiatric reasons.

Spatial extreme learning machines: An application on prediction of disease counts

Extreme learning machines have gained a lot of attention by the machine learning community because of its interesting properties and computational advantages. With the increase in collection of information nowadays, many sources of data have missing information making statistical analysis harder or unfeasible. In this paper, we present a new model, coined spatial extreme learning machine, that combine spatial modeling with extreme learning machines keeping the nice properties of both methodologies and making it very flexible and robust. As explained throughout the text, the spatial extreme learning machines have many advantages in comparison with the traditional extreme learning machines. By a simulation study and a real data analysis we present how the spatial extreme learning machine can be used to improve imputation of missing data and uncertainty prediction estimation.

MAD-STEC: a method for multiple automatic detection of space-time emerging clusters

Crime or disease surveillance commonly rely in space-time clustering methods to identify emerging patterns. The goal is to detect spatial-temporal clusters as soon as possible after its occurrence and to control the rate of false alarms. With this in mind, a spatio-temporal multiple cluster detection method was developed as an extension of a previous proposal based on a spatial version of the Shiryaev–Roberts statistic. Besides the capability of multiple cluster detection, the method have less input parameter than the previous proposal making its use more intuitive to practitioners. To evaluate the new methodology a simulation study is performed in several scenarios and enlighten many advantages of the proposed method. Finally, we present a case study to a crime data-set in Belo Horizonte, Brazil.