Abstract Quite often real data exhibit non-normal features, such as asymmetry and heavy tails, present a latent group structure. In this paper, we first propose the multivariate skew shifted exponential normal distribution that can account for these characteristics. Then, use in finite mixture modeling framework. An EM algorithm is illustrated maximum-likelihood parameter estimation. We provide...

Motivated by recent work studying massive imaging data in the neuroimaging literature, we propose multivariate varying coefficient models (MVCM) for modeling the relation between multiple functional responses and a set of covariates. We develop several statistical inference procedures for MVCM and systematically study their theoretical properties. We first establish the weak convergence of the ...

Univariate prediction models of schizophrenia may be adequate for hypothesis testing but are narrowly focused and limited in predictive efficacy. Therefore, we used a multivariate design to maximize the prediction of schizophrenia from premorbid measures and to evaluate the relative importance of various predictors. Two hundred twelve Danish subjects with at least one parent diagnosed in the sc...

We discuss a Lévy multivariate model for financial assets which incorporates jumps, skewness, kurtosis and stochastic volatility. We use it to describe the behavior of a series of stocks or indexes and to study a multi-firm, value-based default model. Starting from an independent Brownian world, we introduce jumps and other deviations from normality, including non-Gaussian dependence. We use a ...

We present a general method-denoted MoDef-to help specify (or define) the model used to analyze brain imaging data. This method is based on the use of the multivariate linear model on a training data set. We show that when the a priori knowledge about the expected brain response is not too precise, the method allows for the specification of a model that yields a better sensitivity in the statis...

This paper proposes a threshold multivariate GARCH model (Threshold MGARCH) which integrates threshold nonlinearity, mean and volatility asymmetries and time-varying correlation in financial markets. The main feature of this model is that the mean, volatility and time-varying correlation can be governed by different threshold variables with different number of regimes. Estimation is performed u...