The Experts below are selected from a list of 267 Experts worldwide ranked by ideXlab platform
Simon A T Redfern - One of the best experts on this subject based on the ideXlab platform.
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Unit cell refinement from powder diffraction data: the use of Regression Diagnostics
Mineralogical Magazine, 1997Co-Authors: T J B Holland, Simon A T RedfernAbstract:AbstractWe discuss the use of Regression Diagnostics combined with nonlinear least-squares to refine cell parameters from powder diffraction data, presenting a method which minimizes residuals in the experimentally-determined quantity (usually 2θhkl or energy, Ehkl). Regression Diagnostics, particularly deletion Diagnostics, are invaluable in detection of outliers and influential data which could be deleterious to the regressed results. The usual practice of simple inspection of calculated residuals alone often fails to detect the seriously deleterious outliers in a dataset, because bare residuals provide no information on the leverage (sensitivity) of the datum concerned. The Regression Diagnostics which predict the change expected in each cell constant upon deletion of each observation (hkl reflection) are particularly valuable in assessing the sensitivity of the calculated results to individual reflections. A new computer program, implementing nonlinear Regression methods and providing the diagnostic output, is described.
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unit cell refinement from powder diffraction data the use of Regression Diagnostics
Mineralogical Magazine, 1997Co-Authors: T J B Holland, Simon A T RedfernAbstract:We discuss the use of Regression Diagnostics combined with nonlinear least-squares to refine cell parameters from powder diffraction data, presenting a method which minimizes residuals in the experimentallydetermined quantity (usually 20hkt or energy, Ehkt). Regression Diagnostics, particularly deletion Diagnostics, are invaluable in detection of outliers and influential data which could be deleterious to the regressed results. The usual practice of simple inspection of calculated residuals alone often fails to detect the seriously deleterious outliers in a dataset, because bare residuals provide no information on the leverage (sensitivity) of the datum concerned. The Regression Diagnostics which predict the change expected in each cell constant upon deletion of each observation (hkl reflection) are particularly valuable in assessing the sensitivity of the calculated results to individual reflections. A new computer program, implementing nonlinear Regression methods and providing the diagnostic output, is described. I~YWORDS: powder diffraction, Regression Diagnostics, lattice parameters, computer program.
John S Preisser - One of the best experts on this subject based on the ideXlab platform.
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A SAS/IML software program for GEE and Regression Diagnostics
Computational Statistics & Data Analysis, 2006Co-Authors: Bradley G Hammill, John S PreisserAbstract:A SAS/IML software program is described that computes Regression Diagnostics for generalized estimating equations. These Diagnostics are computationally efficient and accurate approximations for the effect of deleting one observation or one cluster on individual Regression coefficients (DFBETA) or on the overall fit of the model (Cook's Distance). New formulae for the Diagnostics are presented which are equivalent to those introduced by Preisser and Qaqish [1996. Deletion Diagnostics for generalised estimating equations. Biometrika 83, 551-562]. The new formulae expose the relationships of the diagnostic measures to the GEE score equations and to a bias-corrected GEE variance estimator which is also implemented in the SAS macro. The macro is applied to three clustered data sets.
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a sas iml software program for gee and Regression Diagnostics
Computational Statistics & Data Analysis, 2006Co-Authors: Bradley G Hammill, John S PreisserAbstract:A SAS/IML software program is described that computes Regression Diagnostics for generalized estimating equations. These Diagnostics are computationally efficient and accurate approximations for the effect of deleting one observation or one cluster on individual Regression coefficients (DFBETA) or on the overall fit of the model (Cook's Distance). New formulae for the Diagnostics are presented which are equivalent to those introduced by Preisser and Qaqish [1996. Deletion Diagnostics for generalised estimating equations. Biometrika 83, 551-562]. The new formulae expose the relationships of the diagnostic measures to the GEE score equations and to a bias-corrected GEE variance estimator which is also implemented in the SAS macro. The macro is applied to three clustered data sets.
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A SAS/IML software program for GEE and Regression Diagnostics
Computational Statistics and Data Analysis, 2006Co-Authors: Bradley G Hammill, John S PreisserAbstract:A SAS/IML software program is described that computes Regression Diagnostics for generalized estimating equations. These Diagnostics are computationally efficient and accurate approximations for the effect of deleting one observation or one cluster on individual Regression coefficients (DFBETA) or on the overall fit of the model (Cook's Distance). New formulae for the Diagnostics are presented which are equivalent to those introduced by Preisser and Qaqish [1996. Deletion Diagnostics for generalised estimating equations. Biometrika 83, 551-562]. The new formulae expose the relationships of the diagnostic measures to the GEE score equations and to a bias-corrected GEE variance estimator which is also implemented in the SAS macro. The macro is applied to three clustered data sets. © 2005 Elsevier B.V. All rights reserved.
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Regression Diagnostics and resistant fits for generalized estimating equations
1995Co-Authors: John S PreisserAbstract:The Generalized Estimating Equations (GEE) procedure ofLiang and Zeger (1986) can be highly influenced by the presence ofunusual data points. Deletion Diagnostics are introduced that consider leverage and residuals to measure the influence ofa subset ofobservations on the estimated Regression parameters and on the estimated values ofthe linear predictor. Computational formulae are provided which correspond to the influence ofa single observation and ofan entire cluster ofcorrelated observations. The proposed Diagnostics are generalizations ofDBETA (Belsley et al. (1980» and Cook's D (Cook (1977» oflinear Regression. As an alternative approach, the influence ofobservations is addressed by Resistant Generalized Estimating Equations (REGEE). A generalization of the GEE procedure, REGEE gives parameter estimates and fitted values which are resistant to influential data. Robustness is achieved in REGEE through the inclusion of a diagonal weight matrix for each cluster in the estimating equations which downweight the multivariate response vector element-wise. The weights are defined with respect to leverage, corresponding to the Mallows class (Carroll and Pederson (1993», or residual, the Schweppe class (pregibon (1982». The large sample and small sample properties are studied. An example of medical practice data is given to illustrate the use ofthe deletion Diagnostics and REGEE for correlated binary Regression.
T J B Holland - One of the best experts on this subject based on the ideXlab platform.
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Unit cell refinement from powder diffraction data: the use of Regression Diagnostics
Mineralogical Magazine, 1997Co-Authors: T J B Holland, Simon A T RedfernAbstract:AbstractWe discuss the use of Regression Diagnostics combined with nonlinear least-squares to refine cell parameters from powder diffraction data, presenting a method which minimizes residuals in the experimentally-determined quantity (usually 2θhkl or energy, Ehkl). Regression Diagnostics, particularly deletion Diagnostics, are invaluable in detection of outliers and influential data which could be deleterious to the regressed results. The usual practice of simple inspection of calculated residuals alone often fails to detect the seriously deleterious outliers in a dataset, because bare residuals provide no information on the leverage (sensitivity) of the datum concerned. The Regression Diagnostics which predict the change expected in each cell constant upon deletion of each observation (hkl reflection) are particularly valuable in assessing the sensitivity of the calculated results to individual reflections. A new computer program, implementing nonlinear Regression methods and providing the diagnostic output, is described.
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unit cell refinement from powder diffraction data the use of Regression Diagnostics
Mineralogical Magazine, 1997Co-Authors: T J B Holland, Simon A T RedfernAbstract:We discuss the use of Regression Diagnostics combined with nonlinear least-squares to refine cell parameters from powder diffraction data, presenting a method which minimizes residuals in the experimentallydetermined quantity (usually 20hkt or energy, Ehkt). Regression Diagnostics, particularly deletion Diagnostics, are invaluable in detection of outliers and influential data which could be deleterious to the regressed results. The usual practice of simple inspection of calculated residuals alone often fails to detect the seriously deleterious outliers in a dataset, because bare residuals provide no information on the leverage (sensitivity) of the datum concerned. The Regression Diagnostics which predict the change expected in each cell constant upon deletion of each observation (hkl reflection) are particularly valuable in assessing the sensitivity of the calculated results to individual reflections. A new computer program, implementing nonlinear Regression methods and providing the diagnostic output, is described. I~YWORDS: powder diffraction, Regression Diagnostics, lattice parameters, computer program.
Bradley G Hammill - One of the best experts on this subject based on the ideXlab platform.
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A SAS/IML software program for GEE and Regression Diagnostics
Computational Statistics & Data Analysis, 2006Co-Authors: Bradley G Hammill, John S PreisserAbstract:A SAS/IML software program is described that computes Regression Diagnostics for generalized estimating equations. These Diagnostics are computationally efficient and accurate approximations for the effect of deleting one observation or one cluster on individual Regression coefficients (DFBETA) or on the overall fit of the model (Cook's Distance). New formulae for the Diagnostics are presented which are equivalent to those introduced by Preisser and Qaqish [1996. Deletion Diagnostics for generalised estimating equations. Biometrika 83, 551-562]. The new formulae expose the relationships of the diagnostic measures to the GEE score equations and to a bias-corrected GEE variance estimator which is also implemented in the SAS macro. The macro is applied to three clustered data sets.
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a sas iml software program for gee and Regression Diagnostics
Computational Statistics & Data Analysis, 2006Co-Authors: Bradley G Hammill, John S PreisserAbstract:A SAS/IML software program is described that computes Regression Diagnostics for generalized estimating equations. These Diagnostics are computationally efficient and accurate approximations for the effect of deleting one observation or one cluster on individual Regression coefficients (DFBETA) or on the overall fit of the model (Cook's Distance). New formulae for the Diagnostics are presented which are equivalent to those introduced by Preisser and Qaqish [1996. Deletion Diagnostics for generalised estimating equations. Biometrika 83, 551-562]. The new formulae expose the relationships of the diagnostic measures to the GEE score equations and to a bias-corrected GEE variance estimator which is also implemented in the SAS macro. The macro is applied to three clustered data sets.
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A SAS/IML software program for GEE and Regression Diagnostics
Computational Statistics and Data Analysis, 2006Co-Authors: Bradley G Hammill, John S PreisserAbstract:A SAS/IML software program is described that computes Regression Diagnostics for generalized estimating equations. These Diagnostics are computationally efficient and accurate approximations for the effect of deleting one observation or one cluster on individual Regression coefficients (DFBETA) or on the overall fit of the model (Cook's Distance). New formulae for the Diagnostics are presented which are equivalent to those introduced by Preisser and Qaqish [1996. Deletion Diagnostics for generalised estimating equations. Biometrika 83, 551-562]. The new formulae expose the relationships of the diagnostic measures to the GEE score equations and to a bias-corrected GEE variance estimator which is also implemented in the SAS macro. The macro is applied to three clustered data sets. © 2005 Elsevier B.V. All rights reserved.
Andrés F. Barrientos - One of the best experts on this subject based on the ideXlab platform.
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is my model any good differentially private Regression Diagnostics
Knowledge and Information Systems, 2018Co-Authors: Yan Chen, Ashwin Machanavajjhala, Andrés F. Barrientos, Jerome P. ReiterAbstract:Linear and logistic Regression are popular statistical techniques for analyzing multi-variate data. Typically, analysts do not simply posit a particular form of the Regression model, estimate its parameters, and use the results for inference or prediction. Instead, they first use a variety of diagnostic techniques to assess how well the model fits the relationships in the data and how well it can be expected to predict outcomes for out-of-sample records, revising the model as necessary to improve fit and predictive power. In this article, we develop $$\epsilon $$ -differentially private Diagnostics tools for Regression, beginning to fill a gap in privacy-preserving data analysis. Specifically, we create differentially private versions of residual plots for linear Regression and of receiver operating characteristic (ROC) curves as well as binned residual plot for logistic Regression. The residual plot and binned residual plot help determine whether or not the data satisfy the assumptions underlying the Regression model, and the ROC curve is used to assess the predictive power of the logistic Regression model. These Diagnostics improve the usefulness of algorithms for computing differentially private Regression output, which alone does not allow analysts to assess the quality of the posited model. Our empirical studies show that these algorithms can be effective tools for allowing users to evaluate the quality of their models.
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ICDM - Differentially Private Regression Diagnostics
2016 IEEE 16th International Conference on Data Mining (ICDM), 2016Co-Authors: Yan Chen, Ashwin Machanavajjhala, Jerome P. Reiter, Andrés F. BarrientosAbstract:Linear and logistic Regression are popular statistical techniques for analyzing multi-variate data. Typically, analysts do not simply posit a particular form of the Regression model, estimate its parameters, and use the results for inference orprediction. Instead, they first use a variety of diagnostic techniques to assess how well the model fits the relationships in the data and how well it can be expected to predict outcomes for out-of-sample records, revising the model as necessary to improve fit and predictive power. In this article, we develop e-differentially private Diagnostics for Regression, beginning to fill a gap in privacy-preserving data analysis. Specifically, we create differentially private versions of residual plots for linear Regression and of receiver operating characteristic (ROC) curves for logistic Regression. The former helps determine whether or not the data satisfy the assumptions underlying the linear Regression model, and the latter is used to assess the predictive power of the logistic Regression model. These Diagnostics improve the usefulness of algorithms for computing differentially private Regression output, which alone does not allow analysts to assess the quality of the posited model. Our empirical studies show that these algorithms are adequate for diagnosing the fit and predictive power of Regression models on representative datasets when the size of the dataset times the privacy parameter (e) is at least 1000.
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differentially private Regression Diagnostics
International Conference on Data Mining, 2016Co-Authors: Yan Chen, Ashwin Machanavajjhala, Jerome P. Reiter, Andrés F. BarrientosAbstract:Linear and logistic Regression are popular statistical techniques for analyzing multi-variate data. Typically, analysts do not simply posit a particular form of the Regression model, estimate its parameters, and use the results for inference orprediction. Instead, they first use a variety of diagnostic techniques to assess how well the model fits the relationships in the data and how well it can be expected to predict outcomes for out-of-sample records, revising the model as necessary to improve fit and predictive power. In this article, we develop e-differentially private Diagnostics for Regression, beginning to fill a gap in privacy-preserving data analysis. Specifically, we create differentially private versions of residual plots for linear Regression and of receiver operating characteristic (ROC) curves for logistic Regression. The former helps determine whether or not the data satisfy the assumptions underlying the linear Regression model, and the latter is used to assess the predictive power of the logistic Regression model. These Diagnostics improve the usefulness of algorithms for computing differentially private Regression output, which alone does not allow analysts to assess the quality of the posited model. Our empirical studies show that these algorithms are adequate for diagnosing the fit and predictive power of Regression models on representative datasets when the size of the dataset times the privacy parameter (e) is at least 1000.
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Differentially Private Regression Diagnostics
2016 IEEE 16th International Conference on Data Mining (ICDM), 2016Co-Authors: Yan Chen, Ashwin Machanavajjhala, Jerome P. Reiter, Andrés F. BarrientosAbstract:Linear and logistic Regression are popular statistical techniques for analyzing multi-variate data. Typically, analysts do not simply posit a particular form of the Regression model, estimate its parameters, and use the results for inference orprediction. Instead, they first use a variety of diagnostic techniques to assess how well the model fits the relationships in the data and how well it can be expected to predict outcomes for out-of-sample records, revising the model as necessary to improve fit and predictive power. In this article, we develop ε-differentially private Diagnostics for Regression, beginning to fill a gap in privacy-preserving data analysis. Specifically, we create differentially private versions of residual plots for linear Regression and of receiver operating characteristic (ROC) curves for logistic Regression. The former helps determine whether or not the data satisfy the assumptions underlying the linear Regression model, and the latter is used to assess the predictive power of the logistic Regression model. These Diagnostics improve the usefulness of algorithms for computing differentially private Regression output, which alone does not allow analysts to assess the quality of the posited model. Our empirical studies show that these algorithms are adequate for diagnosing the fit and predictive power of Regression models on representative datasets when the size of the dataset times the privacy parameter (ε) is at least 1000.