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176
On choosing and bounding probability metrics
 INTERNAT. STATIST. REV.
, 2002
"... When studying convergence of measures, an important issue is the choice of probability metric. We provide a summary and some new results concerning bounds among some important probability metrics/distances that are used by statisticians and probabilists. Knowledge of other metrics can provide a mea ..."
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Cited by 151 (2 self)
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When studying convergence of measures, an important issue is the choice of probability metric. We provide a summary and some new results concerning bounds among some important probability metrics/distances that are used by statisticians and probabilists. Knowledge of other metrics can provide a means of deriving bounds for another one in an applied problem. Considering other metrics can also provide alternate insights. We also give examples that show that rates of convergence can strongly depend on the metric chosen. Careful consideration is necessary when choosing a metric.
Effects of alcohol on human aggression: An integrative research review
 Psychological Bulletin
, 1990
"... This review used quantitative and qualitative techniques to integrate the alcohol and aggression literature. The primary purpose of the review was to determine if a causal relation exists between alcohol and aggression. The main rectaanalysis included 30 experimental studies that used betweensubje ..."
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Cited by 66 (2 self)
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This review used quantitative and qualitative techniques to integrate the alcohol and aggression literature. The primary purpose of the review was to determine if a causal relation exists between alcohol and aggression. The main rectaanalysis included 30 experimental studies that used betweensubjects designs, male confederates, and male subjects who were social drinkers. Studies using other designs or subject populations were integrated with rectaanalytic procedures when possible and summarized descriptively when not. The results of the review indicate that alcohol does indeed cause aggression. However, alcohol effects were moderated by certain methodological parameters. •.. O thou invisible spirit of wine, if thou hast no name to be known by, let us call thee dev i l!... O God, that men should put an enemy in their mouths to steal away their brains! that we should, with joy, pleasance, revel and applause, transform ourselves into beasts!Wil l iam Shakespeare, Othello (II, iii) For hundreds of years, it has been assumed that individuals behave more aggressively while under the influence of alcohol. Correlational evidence offers some support for conventional wisdom. For instance, numerous tudies have found a strong relation between alcohol intoxication and homicide. In an early book, MacDonald (1961) reviewed 10 studies and found that the proportion of murderers who had been drinking before their crimes ranged from. 19 to.83, with a median of.54. More recent studies have reported similar findings among both adult (Holcomb & Anderson, 1983) and juvenile (Sorrells, 1977) assailants. In addition, alcohol intoxication has been linked to other types of aggression, such as assault (Myers, 1982), wife
Toward Simplifying and Accurately Formulating Fragment Assembly
 JOURNAL OF COMPUTATIONAL BIOLOGY
, 1995
"... The fragment assembly problem is that of reconstructing a DNA sequence from a collection of randomly sampled fragments. Traditionally the objective of this problem has been to produce the shortest string that contains all the fragments as substrings, but in the case of repetitive target sequence ..."
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Cited by 63 (1 self)
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The fragment assembly problem is that of reconstructing a DNA sequence from a collection of randomly sampled fragments. Traditionally the objective of this problem has been to produce the shortest string that contains all the fragments as substrings, but in the case of repetitive target sequences this objective produces answers that are overcompressed. In this paper, the problem is reformulated as one of finding a maximumlikelihood reconstruction with respect to the 2sided KolmogorovSmirnov statistic, and it is argued that this is a better formulation of the problem. Next the fragment assembly problem is recast in graphtheoretic terms as one of finding a noncyclic subgraph with certain properties and the objectives of being shortest or maximallylikely are also recast in this framework. Finally, a series of graph reduction transformations are given that dramatically reduce the size of the graph to be explored in practical instances of the problem. This reduction is ...
Evaluating Kolmogorov’s Distribution
 Journal of Statistical Software
"... Kolmogorov’s goodnessoffit measure, Dn, for a sample CDF has consistently been set aside for methods such as the D+n or D n of Smirnov, primarily, it seems, because of the difficulty of computing the distribution of Dn. As far as we know, no easy way to compute that distribution has ever been prov ..."
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Cited by 56 (1 self)
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Kolmogorov’s goodnessoffit measure, Dn, for a sample CDF has consistently been set aside for methods such as the D+n or D n of Smirnov, primarily, it seems, because of the difficulty of computing the distribution of Dn. As far as we know, no easy way to compute that distribution has ever been provided in the 70+ years since Kolmogorov’s fundamental paper. We provide one here, a C procedure that provides Pr(Dn < d) with 1315 digit accuracy for n ranging from 2 to at least 16000. We assess the (rather slow) approach to limiting form, and because computing time can become excessive for probabilities>.999 with n’s of several thousand, we provide a quick approximation that gives accuracy to the 7th digit for such cases. 1
Predictive density evaluation
, 2005
"... This chapter discusses estimation, specification testing, and model selection of predictive density models. In particular, predictive density estimation is briefly discussed, and a variety of different specification and model evaluation tests due to various ..."
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Cited by 46 (6 self)
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This chapter discusses estimation, specification testing, and model selection of predictive density models. In particular, predictive density estimation is briefly discussed, and a variety of different specification and model evaluation tests due to various
CONFIDENCE MEASURES FOR MULTIMODAL IDENTITY VERIFICATION
, 2002
"... Multimodal fusion for identity verification has already shown great improvement compared to unimodal algorithms. In this paper, we propose to integrate confidence measures during the fusion process. We present a comparison of three different methods to generate such confidence information from unim ..."
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Cited by 37 (10 self)
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Multimodal fusion for identity verification has already shown great improvement compared to unimodal algorithms. In this paper, we propose to integrate confidence measures during the fusion process. We present a comparison of three different methods to generate such confidence information from unimodal identity verification systems. These methods can be used either to enhance the performance of a multimodal fusion algorithm or to obtain a confidence level on the decisions taken by the system. All the algorithms are compared on the same benchmark database, namely XM2VTS, containing both speech and face information. Results show that some confidence measures did improve statistically significantly the performance, while other measures produced reliable confidence levels over the fusion decisions.
Monte Carlo test methods in econometrics
 Companion to Theoretical Econometrics’, Blackwell Companions to Contemporary Economics
, 2001
"... The authors thank three anonymous referees and the Editor Badi Baltagi for several useful comments. This work was supported by the Bank of Canada and by grants from the Canadian Network of Centres of Excellence [program on Mathematics ..."
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Cited by 35 (24 self)
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The authors thank three anonymous referees and the Editor Badi Baltagi for several useful comments. This work was supported by the Bank of Canada and by grants from the Canadian Network of Centres of Excellence [program on Mathematics
Validation of software for bayesian models using posterior quantiles
 Journal of Computational and Graphical Statistics
"... We present a simulationbased method designed to establish that software developed to fit a specific Bayesian model works properly, capitalizing on properties of Bayesian posterior distributions. We illustrate the validation technique with two examples. The validation method is shown to find errors ..."
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Cited by 28 (6 self)
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We present a simulationbased method designed to establish that software developed to fit a specific Bayesian model works properly, capitalizing on properties of Bayesian posterior distributions. We illustrate the validation technique with two examples. The validation method is shown to find errors in software when they exist and, moreover, the validation output can be informative about the nature and location of such errors.
A Neural Bayesian Estimator for Conditional Probability Densities
, 2008
"... This article describes a robust algorithm to estimate a conditional probability density f(t⃗x) as a nonparametric smooth regression function. It is based on a neural network and the Bayesian interpretation of the network output as a posteriori probabability. The network is trained using example ev ..."
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Cited by 17 (0 self)
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This article describes a robust algorithm to estimate a conditional probability density f(t⃗x) as a nonparametric smooth regression function. It is based on a neural network and the Bayesian interpretation of the network output as a posteriori probabability. The network is trained using example events from history or simulation, which define the underlying probability density f(t,⃗x). Once trained, the network is applied on new, unknown examples ⃗x, for which it can predict the probability distribution of the target variable t. Eventbyevent knowledge of the smooth function f(t⃗x) can be very useful, e.g. in maximum likelihood fits or for forecasting tasks. No assumptions are necessary about the distribution, and nonGaussian tails are accounted for automatically. Important quantities like median, mean value, left and right standard deviations, moments and expectation values of any function of t are readily derived from it. The algorithm can be considered as an eventbyevent unfolding and leads to statistically optimal reconstruction. The largest benefit of the method lies in complicated