David Beaudoin’s research while affiliated with University of Quebec and other places

In this paper, we introduce a new ranking system where the data are preferences resulting from paired comparisons. When direct preferences are missing or unclear, then preferences are determined through indirect comparisons. Given that a ranking of n subjects implies (2n) paired preferences, the resultant computational problem is the determination of an optimal ranking where the agreement between the implied preferences via the ranking and the data preferences is maximized. Comparisons are carried out via simulation studies where the proposed rankings outperform Bradley–Terry in a particular predictive comparison.

The main objective of this paper is to investigate the extent to which the margin of victory can be predicted solely by the rankings of the opposing teams in NCAA Division I men's basketball games. Several past studies have modeled this relationship for the games played during the March Madness tournament, and this work aims at verifying if the models advocated in these papers still perform well for regular season games. Indeed, most previous articles have shown that a simple quadratic regression model provides fairly accurate predictions of the margin of victory when team rankings only range from 1 to 16. Does that still hold true when team rankings can go as high as 351? Do the model assumptions hold? Can we find semi- or non-parametric methods that yield even better results (i.e. predicted margins of victory that more closely resemble actual results)? The analyses presented in this paper suggest that the answer is "yes" on all three counts!

The main objective of this paper is to investigate the extent to which the margin of victory can be predicted solely by the rankings of the opposing teams in NCAA Division I men's basketball games. Several past studies have modeled this relationship for the games played during the March Madness tournament, and this work aims at verifying if the models advocated in these papers still perform well for regular season games. Indeed, most previous articles have shown that a simple quadratic regression model provides fairly accurate predictions of the margin of victory when team rankings only range from 1 to 16. Does that still hold true when team rankings can go as high as 351? Do the model assumptions hold? Can we find semi- or non-parametric methods that yield even better results (i.e. predicted margins of victory that more closely resemble actual results)? The analyses presented in this paper suggest that the answer is "yes" on all three counts!

This paper investigates penalty calls in the National Hockey League (NHL). Our study shows that there are various situational effects that are associated with the next penalty call. These situational effects are related to the accumulated penalty calls, the goal differential, the stage of the match and the relative strengths of the two teams. We also investigate individual referee effects across the NHL. © 2016 Wiley Periodicals, Inc. Statistical Analysis and Data Mining: The ASA Data Science Journal, 2016

This paper develops a simulator for matches in the National Hockey League with the intent of assessing strategies for pulling the goaltender. Aspects of the approach that are novel include breaking the game down into ner and more realistic situations, introducing the eect of penalties and including the home-ice advantage. Parameter estimates used in the simulator are obtained through the analysis of an extensive data set using constrained Bayesian estimation via Markov chain methods. Some surprising strategies are obtained which do not appear to be used in practice.

Many proposals have been made recently for goodness-of-fit testing of copula models. After reviewing them briefly, the authors concentrate on "blanket tests", i.e., those whose implementation requires neither an arbitrary categorization of the data nor any strategic choice of smoothing parameter, weight function, kernel, window, etc. The authors present a critical review of these procedures and suggest new ones. They describe and interpret the results of a large Monte Carlo experiment designed to assess the effect of the sample size and the strength of dependence on the level and power of the blanket tests for various combinations of copula models under the null hypothesis and the alternative. To circumvent problems in the determination of the limiting distribution of the test statistics under composite null hypotheses, they recommend the use of a double parametric bootstrap procedure, whose implementation is detailed. They conclude with a number of practical recommendations.

We investigate the nonparametric estimation of Kendall's coefficient of concordance, τ, for measuring the association between two variables under bivariate censoring. The proposed estimator is a modification of the estimator introduced by Oakes (1982), using a Horvitz-Thompson-type correction for the pairs that are not orderable. With censored data, a pair is orderable if one can establish whether the uncensored pair is discordant or concordant using the data available for that pair. Our estimator is shown to be consistent and asymptotically normally distributed. A simulation study shows that the proposed estimator performs well when compared with competing alternatives. The various methods are illustrated with a real data set.

One-sided truncated survival data arise when a pair of time-to-event variables (X, Y) is observed only when X<Y. Existing methods of analysis rely on the assumption of quasi-independence between X and Y. Recently, Lakhal-Chaieb et al. (Biometrika 2006; 93:655-669) modeled potential dependency between these random variables via a semi-survival Archimedean copula. In this paper, we present a model selection procedure to rank a set of semi-survival Archimedean copula families according to their ability to fit a given data set subject to dependent truncation. The proposed procedure is based on a truncated version of Kendall's tau (J. Multivariate Anal. 1996; 56:60-74). The performance of the proposal is illustrated through simulations and three real data sets.

This paper considers the estimation of Kendall's tau for bivariate data (X,Y) when only Y is subject to right-censoring. Although τ is estimable under weak regularity conditions, the estimators proposed by Brown et al. [1974. Nonparametric tests of independence for censored data, with applications to heart transplant studies. Reliability and Biometry, 327–354], Weier and Basu [1980. An investigation of Kendall's τ modified for censored data with applications. J. Statist. Plann. Inference 4, 381–390] and Oakes [1982. A concordance test for independence in the presence of censoring. Biometrics 38, 451–455], which are standard in this context, fail to be consistent when τ≠0 because they only use information from the marginal distributions. An exception is the renormalized estimator of Oakes [2006. On consistency of Kendall's tau under censoring. Technical Report, Department of Biostatistics and Computational Biology, University of Rochester, Rochester, NY], whose consistency has been established for all possible values of τ, but only in the context of the gamma frailty model. Wang and Wells [2000. Estimation of Kendall's tau under censoring. Statist. Sinica 10, 1199–1215] were the first to propose an estimator which accounts for joint information. Four more are developed here: the first three extend the methods of Brown et al. [1974. Nonparametric tests of independence for censored data, with applications to heart transplant studies. Reliability and Biometry, 327–354], Weier and Basu [1980, An investigation of Kendall's τ modified for censored data with applications. J. Statist. Plann. Inference 4, 381–390] and Oakes [1982, A concordance test for independence in the presence of censoring. Biometrics 38, 451–455] to account for information provided by X, while the fourth estimator inverts an estimation of Pr(Yi⩽y|Xi=xi,Yi>ci) to get an imputation of the value of Yi censored at Ci=ci. Following Lim [2006. Permutation procedures with censored data. Comput. Statist. Data Anal. 50, 332–345], a nonparametric estimator is also considered which averages the obtained from a large number of possible configurations of the observed data (X1,Z1),…,(Xn,Zn), where Zi=min(Yi,Ci). Simulations are presented which compare these various estimators of Kendall's tau. An illustration involving the well-known Stanford heart transplant data is also presented.

Abstract This paper concerns the search for optimal or nearly optimal batting orders in one-day cricket. A search is conducted over the space of permutations of batting orders where simulated annealing is used to explore the space. A non-standard aspect of the optimization is that the objective function (which is the mean,number,of runs per innings) is unavailable and is approximated,via simulation. The simulation component,generates runs ball by ball during an innings taking into account the state of the match and estimated characteristics of individual batsmen. The methods developed in the paper are applied to the national team of India based on their performance,in one-day international cricket matches. 2004 Elsevier Ltd. All rights reserved. Keywords: Log-linear models; Markov chain methods; Monte Carlo simulation; Simulated annealing; WinBUGS software