The Experts below are selected from a list of 282 Experts worldwide ranked by ideXlab platform

Joshua D. Naranjo - One of the best experts on this subject based on the ideXlab platform.

  • Robust Measures of Association in the correlation model
    Statistics & Probability Letters, 1994
    Co-Authors: Lee D. Witt, Joseph W. Mckean, Joshua D. Naranjo
    Abstract:

    In the correlation model, the classical coefficient of multiple determination 2 is a Measure of Association between the dependent random variable Y and the random vector of independent variables x. Slight departures from normality, however, can have a pronounced effect on the Measure. In the regression model robust estimates of the regression coefficients are less sensitive to outlying points than least squares estimates. These estimates are often obtained by minimizing an objective (dispersion) function. For such robust estimates, the proportion of explained dispersion is a natural analogue to the statistic R2. Although this statistic is generally not robust, it leads to robust statistics which are consistent estimates of functionals in the correlation model. These functionals are robust Measures of Association between Y and x. They are efficiently robust and have bounded influence provided the robust estimator on which they are based has bounded influence.

Stephane Lallich - One of the best experts on this subject based on the ideXlab platform.

  • a robustness Measure of Association rules
    European conference on Machine Learning, 2010
    Co-Authors: Yannick Le Bras, Philippe Lenca, Patrick Meyer, Stephane Lallich
    Abstract:

    We propose a formal definition of the robustness of Association rules for interestingness Measures. It is a central concept in the evaluation of the rules and has only been studied unsatisfactorily up to now. It is crucial because a good rule (according to a given quality Measure) might turn out as a very fragile rule with respect to small variations in the data. The robustness Measure that we propose here is based on a model we proposed in a previous work. It depends on the selected quality Measure, the value taken by the rule and the minimal acceptance threshold chosen by the user. We present a few properties of this robustness, detail its use in practice and show the outcomes of various experiments. Furthermore, we compare our results to classical tools of statistical analysis of Association rules. All in all, we present a new perspective on the evaluation of Association rules.

  • ECML/PKDD (2) - A robustness Measure of Association rules
    Machine Learning and Knowledge Discovery in Databases, 2010
    Co-Authors: Yannick Le Bras, Philippe Lenca, Patrick Meyer, Stephane Lallich
    Abstract:

    We propose a formal definition of the robustness of Association rules for interestingness Measures. It is a central concept in the evaluation of the rules and has only been studied unsatisfactorily up to now. It is crucial because a good rule (according to a given quality Measure) might turn out as a very fragile rule with respect to small variations in the data. The robustness Measure that we propose here is based on a model we proposed in a previous work. It depends on the selected quality Measure, the value taken by the rule and the minimal acceptance threshold chosen by the user. We present a few properties of this robustness, detail its use in practice and show the outcomes of various experiments. Furthermore, we compare our results to classical tools of statistical analysis of Association rules. All in all, we present a new perspective on the evaluation of Association rules.

Rand R. Wilcox - One of the best experts on this subject based on the ideXlab platform.

  • Regression: Comparing Predictors and Groups of Predictors Based on a Robust Measure of Association
    Journal of data science, 2010
    Co-Authors: Rand R. Wilcox
    Abstract:

    Let ρj be Pearson's correlation between Y and Xj (j = 1, 2). A problem that has received considerable attention is testing H0: ρ1 = ρ2. A well-known concern, however, is that Pearson's correlation is not robust (e.g., Wilcox, 2005), and the usual estimate of ρj, rj has a finite sample breakdown point of only 1/n. The goal in this paper is to consider extensions to situations where Pearson's correlation is replaced by a particular robust Measure of Association. Included are results where there are p > 2 predictors and the goal to compare any two subsets of m < p predictors.

  • Local Measures of Association: estimating the derivative of the regression line.
    British Journal of Mathematical and Statistical Psychology, 2007
    Co-Authors: Rand R. Wilcox
    Abstract:

    A local Measure of Association that allows both heteroscedasticity and a non-linear Association was developed during the 1990s. The basic goal is to Measure the strength of the Association between X and Y, given X, when Y = theta(X) + tau(X)epsilon for some unknown functions theta(X) and tau(X). Application of this method requires the estimation of the derivative of theta(X). The focus in this paper is on four alternatives to a very slight modification of the method used by Doksum et al. when estimating this derivative. The main result is that in simulations, a certain robust analogue of their method dominates in terms of mean squared error, even under normality. The bias of the method is found to be small but a little larger than the bias associated with the method used by Doksum et al. The method is based in part on bootstrap bagging followed by a lowess smooth.

Lee D. Witt - One of the best experts on this subject based on the ideXlab platform.

  • Robust Measures of Association in the correlation model
    Statistics & Probability Letters, 1994
    Co-Authors: Lee D. Witt, Joseph W. Mckean, Joshua D. Naranjo
    Abstract:

    In the correlation model, the classical coefficient of multiple determination 2 is a Measure of Association between the dependent random variable Y and the random vector of independent variables x. Slight departures from normality, however, can have a pronounced effect on the Measure. In the regression model robust estimates of the regression coefficients are less sensitive to outlying points than least squares estimates. These estimates are often obtained by minimizing an objective (dispersion) function. For such robust estimates, the proportion of explained dispersion is a natural analogue to the statistic R2. Although this statistic is generally not robust, it leads to robust statistics which are consistent estimates of functionals in the correlation model. These functionals are robust Measures of Association between Y and x. They are efficiently robust and have bounded influence provided the robust estimator on which they are based has bounded influence.

Yannick Le Bras - One of the best experts on this subject based on the ideXlab platform.

  • a robustness Measure of Association rules
    European conference on Machine Learning, 2010
    Co-Authors: Yannick Le Bras, Philippe Lenca, Patrick Meyer, Stephane Lallich
    Abstract:

    We propose a formal definition of the robustness of Association rules for interestingness Measures. It is a central concept in the evaluation of the rules and has only been studied unsatisfactorily up to now. It is crucial because a good rule (according to a given quality Measure) might turn out as a very fragile rule with respect to small variations in the data. The robustness Measure that we propose here is based on a model we proposed in a previous work. It depends on the selected quality Measure, the value taken by the rule and the minimal acceptance threshold chosen by the user. We present a few properties of this robustness, detail its use in practice and show the outcomes of various experiments. Furthermore, we compare our results to classical tools of statistical analysis of Association rules. All in all, we present a new perspective on the evaluation of Association rules.

  • ECML/PKDD (2) - A robustness Measure of Association rules
    Machine Learning and Knowledge Discovery in Databases, 2010
    Co-Authors: Yannick Le Bras, Philippe Lenca, Patrick Meyer, Stephane Lallich
    Abstract:

    We propose a formal definition of the robustness of Association rules for interestingness Measures. It is a central concept in the evaluation of the rules and has only been studied unsatisfactorily up to now. It is crucial because a good rule (according to a given quality Measure) might turn out as a very fragile rule with respect to small variations in the data. The robustness Measure that we propose here is based on a model we proposed in a previous work. It depends on the selected quality Measure, the value taken by the rule and the minimal acceptance threshold chosen by the user. We present a few properties of this robustness, detail its use in practice and show the outcomes of various experiments. Furthermore, we compare our results to classical tools of statistical analysis of Association rules. All in all, we present a new perspective on the evaluation of Association rules.