Household food expenditures in the United States: A Bayesian MCMC approach to censored equation systems

We apply a Bayesian Markov Chain Monte Carlo (MCMC) technique, along with data augmentation to accommodate censoring in the dependent variables, to the estimation of a large expenditure system of food expenditures. Our finding of significant error covariance estimates justifies estimation of the system in improving statistical efficiency. Income, household composition, regions and other socio-demographic variables are found to play significant roles in determining household food expenditures.Bayesian MCMC, Censored equation system, Consumer Expenditure Survey, Food Consumption/Nutrition/Food Safety, C11, C34, D12, C11, C34, D12,

Exploiting Emotional Dependencies with Graph Convolutional Networks for Facial Expression Recognition

Over the past few years, deep learning methods have shown remarkable results in many face-related tasks including automatic facial expression recognition (FER) in-the-wild. Meanwhile, numerous models describing the human emotional states have been proposed by the psychology community. However, we have no clear evidence as to which representation is more appropriate and the majority of FER systems use either the categorical or the dimensional model of affect. Inspired by recent work in multi-label classification, this paper proposes a novel multi-task learning (MTL) framework that exploits the dependencies between these two models using a Graph Convolutional Network (GCN) to recognize facial expressions in-the-wild. Specifically, a shared feature representation is learned for both discrete and continuous recognition in a MTL setting. Moreover, the facial expression classifiers and the valence-arousal regressors are learned through a GCN that explicitly captures the dependencies between them. To evaluate the performance of our method under real-world conditions we perform extensive experiments on the AffectNet and Aff-Wild2 datasets. The results of our experiments show that our method is capable of improving the performance across different datasets and backbone architectures. Finally, we also surpass the previous state-of-the-art methods on the categorical model of AffectNet.Comment: 9 pages, 8 figures, 5 tables, revised submission to the 16th IEEE International Conference on Automatic Face and Gesture Recognitio

A Binary-Ordered Probit Model of Cigarette Demand

This study analyzes the demand for cigarettes fitting observed zero outcomes with a trivariate model consisting of an equation for the starting smoking decision, an equation for the quitting decision, and an equation that models the level of cigarettes consumed. Five competing specifications are considered to explain level, with the ordered probit, which accommodates pile-ups of counts in the dependent variable, providing the best fit. Marginal effects of explanatory variables are calculated providing strong evidence of race and gender differences in consumption patterns. The estimated marginal effects are robust to alternative categorizations of the level of cigarettes.Demand and Price Analysis,