Many machine learning problems inherently involve multiple views. Kernel combination approaches to multiview learning [1] are particularly effective when the views are independent. In contrast, other methods take advantage of the dependencies in the data. The best-known example is Canonical Correlation Analysis (CCA), which learns latent representations of the views whose correlation is maximal...

This paper reviews some of the main approaches to the analysis of multivariate censored survival data. Such data typically have correlated failure times. The correlation can be a consequence of the observational design, for example with clustered sampling and matching, or it can be a focus of interest as in genetic studies, longitudinal studies of recurrent events and other studies involving mu...

BACKGROUND Frailty is related to adverse outcomes in the elderly. However, current status and clinical significance of frailty have not been evaluated for the Korean elderly population. We aimed to investigate the usefulness of established frailty criteria for community-dwelling Korean elderly. We also tried to develop and validate a new frailty index based on a multidimensional model. METHOD...

The sample covariance matrix (SCM) is commonly used in direction-of-arrival (DOA) estimation methods when the noise or observations are circular complex Gaussian (C CG) distributed. However, with a very heavy-tailed non-Gaussian model, SCM-based DOA fail to provide an accurate estimate of DOA. This paper presents numerical analysis resolving capability subspace-based (C) and non-circular (NC) m...

Department of Statistics, University of Pune, Pune-411007, India. Email: david−[email protected]; [email protected] Abstract In this paper, we introduce shared gamma frailty models with three different baseline distributions namely, Weibull, generalized exponential and exponential power distributions. We develop Bayesian estimation procedure using Markov Chain Monte Carlo(MCMC) technique t...

Mixed models are widely used to analyze longitudinal data. In their conventional formulation as linear mixed models (LMMs) and generalized LMMs (GLMMs), a commonly indispensable assumption in settings involving longitudinal non-Gaussian data is that the longitudinal observations from subjects are conditionally independent, given subject-specific random effects. Although conventional Gaussian...

We propose a new class of semiparametric frailty models for spatially correlated survival data. Specifically, we extend the ordinary frailty models by allowing random effects accommodating spatial correlations to enter into the baseline hazard function multiplicatively. We prove identifiability of the models and give sufficient regularity conditions. We propose drawing inference based on a marg...

The classical Cox proportional hazards model is a benchmark approach to analyze continuous survival times in the presence of covariate information. In a number of applications, there is a need to relax one or more of its inherent assumptions, such as linearity of the predictor or the proportional hazards property. Also, one is often interested in jointly estimating the baseline hazard together ...

Hadrien Charvatab* a Faculty of International Liberal Arts, Juntendo University, Tokyo, Japanb Division Health Policy Research, Institute for Cancer Control, National Center, Japan