RESUMO
In this paper, we consider the problem of classification of misaligned multivariate functional data. We propose to use a model-based approach for the joint registration and classification of such data. The observed functional inputs are modeled as a functional nonlinear mixed effects model containing a nonlinear functional fixed effect constructed upon warping functions to account for curve alignment, and a nonlinear functional random effects component to address the variability among subjects. The warping functions are also modeled to accommodate common effect within groups and the variability between subjects. Then, a functional logistic regression model defined upon the representation of the aligned curves and scalar inputs is used to account for curve classification. EM-based algorithms are developed to perform maximum likelihood inference of the proposed models. The identifiability of the registration model and the asymptotical properties of the proposed method are established. The performance of the proposed procedure is illustrated via simulation studies and an analysis of a hyoid bone movement data application. The statistical developments proposed in this paper were motivated by the hyoid bone movement study, the methodology is designed and presented generality and can be applied to numerous areas of scientific research.
RESUMO
Meta-analysis of multi-arm trials has been used increasingly in recent years. The aim of meta-analysis for multi-arm trials is to combine evidence from all possible similar studies. In this paper we propose normal approximation models by using empirical logistic transform to compare different treatments in multi-arm trials, allowing studies of both direct and indirect comparisons. Additionally, a hierarchical structure is introduced in the models to address the problem of heterogeneity among different studies. The proposed models are performed using the data from 31 randomized clinical trials (RCTs) which determine the efficacy of antiplatelet therapy in maintaining vascular patency.