Journal of Data Science

Abstract: In this paper we propose a new bivariate long-term distribution based on the Farlie-Gumbel-Morgenstern copula model. The proposed model allows for the presence of censored data and covariates in the cure parameter. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo (MCMC) is considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and influential observations, we develop a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated on artificial and real HIV data.

Abstract: In any sport competition, there is a strong interest in knowing which team shall be the champion at the end of the championship. Besides this, the end result of a match, the chance of a team to be qualified for a specific tournament, the chance of being relegated, the best attack, the best defense, among others, are also subject of interest. In this paper we present a simple method with good predictive quality, easy implementation, low computational effort, which allows the calculation of all the interesting quantities above. Following Lee (1997), we estimate the average goals scored by each team by assuming that the number of goals scored by a team in a match follows a univariate Poisson distribution but we consider linear models that express the sum and the difference of goals scored in terms of five covariates: the goal average in a match, the home-team advantage, the team’s offensive power, the opponent team’s defensive power and a crisis indicator. The methodology is applied to the 2008-2009 English Premier League.

Compositional data consist of known compositions vectors whose components are positive and defined in the interval (0,1) representing proportions or fractions of a “whole”. The sum of these components must be equal to one. Compositional data is present in different knowledge areas, as in geology, economy, medicine among many others. In this paper, we propose a new statistical tool for volleyball data, i.e., we introduce a Bayesian anal- ysis for compositional regression applying additive log-ratio (ALR) trans- formation and assuming uncorrelated and correlated errors. The Bayesian inference procedure based on Markov Chain Monte Carlo Methods (MCMC). The methodology is applied on an artificial and a real data set of volleyball.