We introduce the truncated Gaussian graphical model (TGGM) as a novel framework for designing statistical models for nonlinear learning. A TGGM is a Gaussian graphical model (GGM) with a subset of variables truncated to be nonnegative. The truncated variables are assumed latent and integrated out to induce a marginal model. We show that the variables in the marginal model are non-Gaussian distr...

LONG-TERM GOALS The primary goal of this project is to enhance our understanding of the dynamics of oceanic overflows, which are characterized by high levels of turbulence and mixing near strategic straits connecting various marginal seas and oceans. OBJECTIVES 1) To complement field studies and to develop a better understanding of the characteristics of mixing and its influence on the subseque...

When using a machine translation (MT) model trained on OLD-domain parallel data to translate NEW-domain text, one major challenge is the large number of out-of-vocabulary (OOV) and new-translation-sense words. We present a method to identify new translations of both known and unknown source language words that uses NEW-domain comparable document pairs. Starting with a joint distribution of sour...

The classical integer valued first-order autoregressive (INA- R(1)) model has been defined on the basis of Poisson innovations. This model has Poisson marginal distribution and is suitable for modeling equidispersed count data. In this paper, we introduce an modification of the INAR(1) model with geometric innovations (INARG(1)) for model- ing overdispersed count data. We discuss some structu...

For square contingency tables with ordered categories, the present paper considers two kinds of weak marginal homogeneity and gives measures to represent the degree of departure from weak marginal homogeneity. The proposed measures lie between –1 to 1. When the marginal cumulative logistic model or the extended marginal homogeneity model holds, the proposed measures represent the degree of depa...

Abstract The present paper considers a model that indicates the structure of inhomogeneity for marginal distributions ordinal categorical data. is based on complementary log–log transformation cumulative probability. A theorem homogeneity holds if and only proposed mean variance equality given. Also, conditional distribution considered under certain condition.

An approach to log-linear modelling for a large contingency table is proposed in this paper. A main idea in this approach is that we group the random variables that are involved in the data into several subsets of variables with corresponding marginal contingency tables, build graphical log-linear models for the marginal tables, and then combine the marginal models using graphs of prime separat...

This paper is concerned with the estimation of the regression coeecients for a count data model when one of the explanatory variables is subject to hete-roscedastic measurement error. The observed values W are related to the true regressor X by the additive error model W=X+U. The errors U are assumed to be normally distributed with zero mean but heteroscedastic variances, which are known or can...

Ultrahigh dimensional variable selection plays an increasingly important role in contemporary scientific discoveries and statistical research. A simple and effective method is the correlation screening. For generalized linear models, we propose a more general version of the independent learning with ranking the maximum marginal likelihood estimates or the maximum marginal likelihood itself. We ...

A semiparametric method is developed for estimating the dependence parameter and the joint distribution of the error term in the multivariate linear regression model. The nonpara-metric part of the method treats the marginal distributions of the error term as unknown, and estimates them by suitable empirical distribution functions. Then a pseudolikelihood is maximized to estimate the dependence...