Luis Ernesto Bueno Salasar

Reliability of intra- and inter-rater palpation discrepancy and estimation of its effects on joint angle measurements

This study presents data on the intra- and inter-rater reliability of palpation on normal and overweight subjects and shows the influence of palpation discrepancy on angular variability for a collected data set, using computer simulation. Thirty healthy males were recruited. Two physiotherapists identified 12 anatomical landmarks that enabled measurement of eight joint angles. Palpation discrepancy was determined by photographic recordings under ultraviolet light. Angular discrepancies were determined from photos of the subjects orthostatic posture. A computer simulation was developed to predict expected angular variation according to observed palpation discrepancy. The results showed that the inter-rater reliability was lower than the intra-rater reliability for both palpation and angle measurements. Palpation of the greater trochanter (GT), anterior superior iliac spine (ASIS), seventh cervical vertebra (C7) and femoral epicondyle (FE) showed larger discrepancies. The overweight group presented a significant difference in palpation discrepancy for ASIS (P<0.03). Angular variations were associated with palpation discrepancies for trunk flexion (TF), hip flexion (HF) and pelvic inclination (PI). Therefore, measurements should be performed by a single rater, rather than by different raters, if reliable angular measurements are intended. Specific anatomical landmarks require careful identification. Simulation was useful for providing estimates of variations due to palpation discrepancy.

A Bayesian approach for predicting match outcomes: The 2006 (Association) Football World Cup

In this paper we propose a Bayesian methodology for predicting match outcomes. The methodology is illustrated on the 2006 Soccer World Cup. As prior information, we make use of the specialists’ opinions and the FIFA ratings. The method is applied to calculate the win, draw and loss probabilities at each match and also to simulate the whole competition in order to estimate classification probabilities in group stage and winning tournament chances for each team. The prediction capability of the proposed methodology is determined by the DeFinetti measure and by the percentage of correct forecasts.

On the integrated maximum likelihood estimators for a closed population capture–recapture model with unequal capture probabilities

Nuisance parameter elimination is a central problem in capture–recapture modelling. In this paper, we consider a closed population capture–recapture model which assumes the capture probabilities varies only with the sampling occasions. In this model, the capture probabilities are regarded as nuisance parameters and the unknown number of individuals is the parameter of interest. In order to eliminate the nuisance parameters, the likelihood function is integrated with respect to a weight function (uniform and Jeffreys) of the nuisance parameters resulting in an integrated likelihood function depending only on the population size. For these integrated likelihood functions, analytical expressions for the maximum likelihood estimates are obtained and it is proved that they are always finite and unique. Variance estimates of the proposed estimators are obtained via a parametric bootstrap resampling procedure. The proposed methods are illustrated on a real data set and their frequentist properties are assessed by means of a simulation study.

Comparing probabilistic predictive models applied to football

We propose two Bayesian multinomial-Dirichlet models to predict the final outcome of football (soccer) matches and compare them to three well-known models regarding their predictive power. All the models predicted the full-time results of 1710 matches of the first division of the Brazilian football championship and the comparison used three proper scoring rules, the proportion of errors and a calibration assessment. We also provide a goodness of fit measure. Our results show that multinomial-Dirichlet models are not only competitive with standard approaches, but they are also well calibrated and present reasonable goodness of fit.

A Nonparametric Bayesian Approach for the Two-Sample Problem

In this work, we propose a novel nonparametric Bayesian approach to the so-called two-sample problem. Let \(X_1, \ldots , X_n\) and \(Y_1, \ldots , Y_m\) be two independent i.i.d samples generated from \(P_1\) and \(P_2\), respectively. Using a nonparametric prior distribution for \((P_1,P_2)\), we propose a new evidence index for the null hypothesis \(H_0: P_1 = P_2\) based on the posterior distribution of the distance \(d(P_1, P_2)\) between \(P_1\) and \(P_2\). This evidence index is easy to compute, has an intuitive interpretation, and can also be justified from a Bayesian decision-theoretic framework. We provide a simulation study to show that our method achieves greater power than the Kolmogorov–Smirnov and the Wilcoxon tests in several settings. Finally, we apply the method to a dataset on Alzheimer’s disease.

A Bayesian Approach to Predicting Football Match Outcomes Considering Time Effect Weight

In this chapter we propose a simulation-based method for predicting football match outcomes. We adopt a Bayesian perspective, modeling the number of goals of two opposing teams as a Poisson distribution whose mean is proportional to the relative technical level of opponents. Federation Internationale de Football Association (FIFA) ratings were taken as the measure of technical level of teams saw well as experts’ opinions on the scores of the matches were taken in account to construct the prior distributions of the parameters. Tournament simulations were performed in order to estimate probabilities of winning the tournament assuming different values for the weight attached to the experts’ information and different choices for the sequence of weights attached to the previous observed matches. The methodology is illustrated on the 2010 Football Word Cup.

A generalized negative binomial distribution based on an extended Poisson process

In this article we propose a generalized negative binomial distribution, which is constructed based on an extended Poisson process (a generalization of the homogeneous Poisson process). This distribution is intended to model discrete data with presence of zero-inflation and over-dispersion. For a dataset on animal abundance which presents over-dispersion and a high frequency of zeros, a comparison between our extended distribution and other common distributions used for modeling this kind of data is addressed, supporting the fitting of the proposed model.