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Clinical studies on periodontal disease (PD) often lead to data collected which are clustered in nature (viz. clinical attachment level, or CAL, measured at tooth-sites and clustered within subjects) that are routinely analyzed under a linear mixed model framework, with underlying normality assumptions of the random effects and random errors. However, a careful look reveals that these data might exhibit skewness and tail behavior, and hence the usual normality assumptions might be questionable. Besides, PD progression is often hypothesized to be spatially associated, that is, a diseased tooth-site may influence the disease status of a set of neighboring sites. Also, the presence/absence of a tooth is informative, as the number and location of missing teeth informs about the periodontal health in that region. In this paper, we develop a (shared) random effects model for site-level CAL and binary presence/absence status of a tooth under a Bayesian paradigm. The random effects are modeled using a spatial skew-normal/independent (S-SNI) distribution, whose dependence structure is conditionally autoregressive (CAR). Our S-SNI density presents an attractive parametric tool to model spatially referenced asymmetric thick-tailed structures. Both simulation studies and application to a clinical dataset recording PD status reveal the advantages of our proposition in providing a significantly improved fit, over models that do not consider these features in a unified way. [ABSTRACT FROM AUTHOR]

The estimation of hidden sub-populations is a hard task that appears in many fields. For example, public health planning in Brazil depends crucially of the number of people who holds a private health insurance plan and hence rarely uses the public services. Different sources of information about these sub-populations may be available at different geographical levels. The available information can be transferred between these different geographic levels to improve the estimation of the hidden population size. In this study, we propose a model that use individual level information to learn about the dependence between the response variable and explanatory variables by proposing a family of link functions with asymptotes that are flexible enough to represent the real aspects of the data and robust to departures from the model. We use the fitted model to estimate the size of the sub-population at any desired level. We illustrate our methodology estimating the sub-population that uses the public health system in each neighborhood of large cities in Brazil. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]