Advances in Data Science and Adaptive Analysis

In this work, we proposed the semi-parametric cure rate models with independent and dependent spatial frailties. These models extend the proportional odds cure models and allow for spatial correlations by including spatial frailty for the interval censored data setting. Moreover, since these cure models are obtained by considering the occurrence of an event of interest is caused by the presence of any nonobserved risks, we also study the complementary cure model, that is, the cure models are obtained by assuming the occurrence of an event of interest is caused when all of the nonobserved risks are activated. The MCMC method is used in a Bayesian approach for inferential purposes. We conduct an influence diagnostic through the diagnostic measures in order to detect possible influential or extreme observations that can cause distortions on the results of the analysis. Finally, the proposed models are applied to the analysis of a real data set.

A new method for hierarchical clustering of data points is presented. It combines treelets, a particular multiresolution decomposition of data, with a mapping on a reproducing kernel Hilbert space. The proposed approach, called kernel treelets (KT), uses this mapping to go from a hierarchical clustering over attributes (the natural output of treelets) to a hierarchical clustering over data. KT effectively substitutes the correlation coefficient matrix used in treelets with a symmetric and positive semi-definite matrix efficiently constructed from a symmetric and positive semi-definite kernel function. Unlike most clustering methods, which require data sets to be numeric, KT can be applied to more general data and yields a multiresolution sequence of orthonormal bases on the data directly in feature space. The effectiveness and potential of KT in clustering analysis are illustrated with some examples.

An authentic way for assessing the goodness of a model is to estimate its predictive capability. In this paper, we propose the D-measure, which measures the goodness of a model by comparing how close its predictions are from the observed data based on the survival function. The proposed D-measure can be used for all kinds of survival data in the presence of censoring. It can also be used to compare cure rate models, even in the presence of random effects or frailties. The advantages of the D-measure are verified via simulation, in which it is compared to the deviance information criterion, which is a widely used Bayesian model comparison criterion. The D-measure is illustrated in two real data sets.

Assistive technology allows motor-impaired people to overcome limitations. Several myoelectric interfaces have been developed, however, there is no reported study employing information at a motor unit (MU) level for controlling purposes. Thus, we developed a facial myoelectric interface operating at the level of MU for controlling a computer screen cursor. Data were collected from 11 able-bodied and 1 tetraplegic subjects. Different from traditional approaches, there was no significant difference ($p<0.05$) in learning with respect to the level of difficulty, occurring evenly and faster. Information at MU level opens new possibilities for the development of fine control myoelectric interfaces.