We consider estimation of the regression function in a semiparametric binary regression model defined through an appropriate link function (with emphasis on the logistic link) using likelihood-ratio based inversion. The dichotomous response variable ∆ is influenced by a set of covariates that can be partitioned as (X,Z) where Z (real valued) is the covariate of primary interest and X (vector va...

This paper considers the maximum likelihood estimation (MLE) of a class of stationary and invertible vector autoregressive fractionally integrated moving-average (VARFIMA) processes considered in (26) of Luceño [1] or Model A of Lobato [2] where each component yi,t is a fractionally integrated process of order di, i = 1, . . . , r. Under the conditions outlined in Assumption 1 of this paper, th...

We propose an efficient and parameter-free scoring criterion, the factorized conditional log-likelihood (f̂CLL), for learning Bayesian network classifiers. The proposed score is an approximation of the conditional log-likelihood criterion. The approximation is devised in order to guarantee decomposability over the network structure, as well as efficient estimation of the optimal parameters, achi...

Training Conditional Random Fields (CRFs) can be very slow for big data. In this paper, we present a new training method for CRFs called Empirical Training which is motivated by the concept of co-occurrence rate. We show that the standard training (unregularized) can have many maximum likelihood estimations (MLEs). Empirical training has a unique closed form MLE which is also a MLE of the stand...

A spatial lattice model for binary data is constructed from two spatial scales linked through conditional probabilities. A coarse grid of lattice locations is specified and all remaining locations (which we call the background) capture fine-scale spatial dependence. Binary data on the coarse grid are modelled with an autologistic distribution, conditional on the binary process on the background...

Multi-Entity Dependence Learning (MEDL) explores conditional correlations among multiple entities. The availability of rich contextual information requires a nimble learning scheme that tightly integrates with deep neural networks and has the ability to capture correlation structures among exponentially many outcomes. We propose MEDL CVAE, which encodes a conditional multivariate distribution a...

Model-based approaches to tracking of articulated objects, such as a human, have a high computational overhead due to the high dimensionality of the state space. In this paper, we present an approach to human motion capture (HMC) that mitigates the problem by performing a probabilistic decomposition of the state space. We achieve this by defining a conditional likelihood for each limb in the ar...

We consider estimation of the regression function in a semiparametric logistic regression model using likelihood-ratio based inversion. The dichotomous response variable ∆ is influenced by a set of covariates that can be partitioned as (X,Z) where Z (real valued) is the covariate of primary interest and X (vector valued) denotes a set of control variables. For any fixed X, the conditional proba...

We consider estimation of the regression function in a semiparametric binary regression model defined through an appropriate link function (with emphasis on the logistic link) using likelihood-ratio based inversion. The dichotomous response variable ∆ is influenced by a set of covariates that can be partitioned as (X,Z) where Z (real valued) is the covariate of primary interest and X (vector va...

Consider an ergodic Markov chain on the real line, with parametric models for the conditional mean and variance of the transition distribution. Such a setting is an instance of a quasi-likelihood model. The customary estimator for the parameter is the maximum quasi-likelihood estimator. It is not eecient, but as good as the best estimator that ignores the parametric model for the conditional va...