The Experts below are selected from a list of 1033305 Experts worldwide ranked by ideXlab platform

Sung Kyun Park - One of the best experts on this subject based on the ideXlab platform.

  • graphical diagnostics to check Model misspecification for the proportional odds Regression Model
    Statistics in Medicine, 2009
    Co-Authors: Bhramar Mukherjee, Thomas F Suesse, David Sparrow, Sung Kyun Park
    Abstract:

    The cumulative logit or the proportional odds Regression Model is commonly used to study covariate effects on ordinal responses. This paper provides some graphical and numerical methods for checking the adequacy of the proportional odds Regression Model. The methods focus on evaluating functional misspecification for specific covariate effects, but misspecification of the link function can also be dealt with under the same framework. For the logistic Regression Model with binary responses, Arbogast and Lin (Statist. Med. 2005; 24:229–247) developed similar graphical and numerical methods for assessing the adequacy of the Model using the cumulative sums of residuals. The paper generalizes their methods to ordinal responses and illustrates them using an example from the VA Normative Aging Study. Simulation studies comparing the performance of the different diagnostic methods indicate that some of the graphical methods are more powerful in detecting Model misspecification than the Hosmer–Lemeshow-type goodness-of-fit statistics for the class of Models studied. Copyright © 2008 John Wiley & Sons, Ltd.

  • graphical diagnostics to check Model misspecification for the proportional odds Regression Model
    Statistics in Medicine, 2009
    Co-Authors: Ivy Liu, Bhramar Mukherjee, Thomas F Suesse, David Sparrow, Sung Kyun Park
    Abstract:

    The cumulative logit or the proportional odds Regression Model is commonly used to study covariate effects on ordinal responses. This paper provides some graphical and numerical methods for checking the adequacy of the proportional odds Regression Model. The methods focus on evaluating functional misspecification for specific covariate effects, but misspecification of the link function can also be dealt with under the same framework. For the logistic Regression Model with binary responses, Arbogast and Lin (Statist. Med. 2005; 24:229-247) developed similar graphical and numerical methods for assessing the adequacy of the Model using the cumulative sums of residuals. The paper generalizes their methods to ordinal responses and illustrates them using an example from the VA Normative Aging Study. Simulation studies comparing the performance of the different diagnostic methods indicate that some of the graphical methods are more powerful in detecting Model misspecification than the Hosmer-Lemeshow-type goodness-of-fit statistics for the class of Models studied.

Chenchien Wang - One of the best experts on this subject based on the ideXlab platform.

  • matrix variate logistic Regression Model with application to eeg data
    Biostatistics, 2013
    Co-Authors: Hung Hung, Chenchien Wang
    Abstract:

    SUMMARY Logistic Regression has been widely applied in the field of biomedical research for a long time. In some applications, the covariates of interest have a natural structure, such as that of a matrix, at the time of collection. The rows and columns of the covariate matrix then have certain physical meanings, and they must contain useful information regarding the response. If we simply stack the covariate matrix as a vector and fit a conventional logistic Regression Model, relevant information can be lost, and the problem of inefficiency will arise. Motivated from these reasons, we propose in this paper the matrix variate logistic (MV-logistic) Regression Model. The advantages of the MV-logistic Regression Model include the preservation of the inherent matrix structure of covariates and the parsimony of parameters needed. In the EEG Database Data Set, we successfully extract the structural effects of covariate matrix, and a high classification accuracy is achieved.

  • matrix variate logistic Regression Model with application to eeg data
    arXiv: Applications, 2011
    Co-Authors: Hung Hung, Chenchien Wang
    Abstract:

    Logistic Regression has been widely applied in the field of biomedical research for a long time. In some applications, covariates of interest have a natural structure, such as being a matrix, at the time of collection. The rows and columns of the covariate matrix then have certain physical meanings, and they must contain useful information regarding the response. If we simply stack the covariate matrix as a vector and fit the conventional logistic Regression Model, relevant information can be lost, and the problem of inefficiency will arise. Motivated from these reasons, we propose in this paper the matrix variate logistic (MV-logistic) Regression Model. Advantages of MV-logistic Regression Model include the preservation of the inherent matrix structure of covariates and the parsimony of parameters needed. In the EEG Database Data Set, we successfully extract the structural effects of covariate matrix, and a high classification accuracy is achieved.

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

  • Nonparametric estimation of an additive quantile Regression Model
    Journal of the American Statistical Association, 2005
    Co-Authors: S. Lee
    Abstract:

    This article is concerned with estimating the additive components of a nonparametric additive quantile Regression Model. We develop an estimator that is asymptotically normally distributed with a rate of convergence in probability of n−r/(2r+1) when the additive components are r-times continuously differentiable for some r ≥ 2. This result holds regardless of the dimension of the covariates, and thus the new estimator has no curse of dimensionality. In addition, the estimator has an oracle property and is easily extended to a generalized additive quantile Regression Model with a link function. The numerical performance and usefulness of the estimator are illustrated by Monte Carlo experiments and an empirical example.

  • nonparametric estimation of an additive quantile Regression Model
    2004
    Co-Authors: Joel L Horowitz, S. Lee
    Abstract:

    This paper is concerned with estimating the additive components of a nonparametric additive quantile Regression Model. We develop an estimator that is asymptotically normally distributed with a rate of convergence in probability of n-r/(2r+1) when the additive components are r-times continuously differentiable for some r = 2. This result holds regardless of the dimension of the covariates and, therefore, the new estimator has no curse of dimensionality. In addition, the estimator has an oracle property and is easily extended to a generalized additive quantile Regression Model with a link function. The numerical performance and usefulness of the estimator are illustrated by Monte Carlo experiments and an empirical example.

Bhramar Mukherjee - One of the best experts on this subject based on the ideXlab platform.

  • graphical diagnostics to check Model misspecification for the proportional odds Regression Model
    Statistics in Medicine, 2009
    Co-Authors: Bhramar Mukherjee, Thomas F Suesse, David Sparrow, Sung Kyun Park
    Abstract:

    The cumulative logit or the proportional odds Regression Model is commonly used to study covariate effects on ordinal responses. This paper provides some graphical and numerical methods for checking the adequacy of the proportional odds Regression Model. The methods focus on evaluating functional misspecification for specific covariate effects, but misspecification of the link function can also be dealt with under the same framework. For the logistic Regression Model with binary responses, Arbogast and Lin (Statist. Med. 2005; 24:229–247) developed similar graphical and numerical methods for assessing the adequacy of the Model using the cumulative sums of residuals. The paper generalizes their methods to ordinal responses and illustrates them using an example from the VA Normative Aging Study. Simulation studies comparing the performance of the different diagnostic methods indicate that some of the graphical methods are more powerful in detecting Model misspecification than the Hosmer–Lemeshow-type goodness-of-fit statistics for the class of Models studied. Copyright © 2008 John Wiley & Sons, Ltd.

  • graphical diagnostics to check Model misspecification for the proportional odds Regression Model
    Statistics in Medicine, 2009
    Co-Authors: Ivy Liu, Bhramar Mukherjee, Thomas F Suesse, David Sparrow, Sung Kyun Park
    Abstract:

    The cumulative logit or the proportional odds Regression Model is commonly used to study covariate effects on ordinal responses. This paper provides some graphical and numerical methods for checking the adequacy of the proportional odds Regression Model. The methods focus on evaluating functional misspecification for specific covariate effects, but misspecification of the link function can also be dealt with under the same framework. For the logistic Regression Model with binary responses, Arbogast and Lin (Statist. Med. 2005; 24:229-247) developed similar graphical and numerical methods for assessing the adequacy of the Model using the cumulative sums of residuals. The paper generalizes their methods to ordinal responses and illustrates them using an example from the VA Normative Aging Study. Simulation studies comparing the performance of the different diagnostic methods indicate that some of the graphical methods are more powerful in detecting Model misspecification than the Hosmer-Lemeshow-type goodness-of-fit statistics for the class of Models studied.

David Sparrow - One of the best experts on this subject based on the ideXlab platform.

  • graphical diagnostics to check Model misspecification for the proportional odds Regression Model
    Statistics in Medicine, 2009
    Co-Authors: Bhramar Mukherjee, Thomas F Suesse, David Sparrow, Sung Kyun Park
    Abstract:

    The cumulative logit or the proportional odds Regression Model is commonly used to study covariate effects on ordinal responses. This paper provides some graphical and numerical methods for checking the adequacy of the proportional odds Regression Model. The methods focus on evaluating functional misspecification for specific covariate effects, but misspecification of the link function can also be dealt with under the same framework. For the logistic Regression Model with binary responses, Arbogast and Lin (Statist. Med. 2005; 24:229–247) developed similar graphical and numerical methods for assessing the adequacy of the Model using the cumulative sums of residuals. The paper generalizes their methods to ordinal responses and illustrates them using an example from the VA Normative Aging Study. Simulation studies comparing the performance of the different diagnostic methods indicate that some of the graphical methods are more powerful in detecting Model misspecification than the Hosmer–Lemeshow-type goodness-of-fit statistics for the class of Models studied. Copyright © 2008 John Wiley & Sons, Ltd.

  • graphical diagnostics to check Model misspecification for the proportional odds Regression Model
    Statistics in Medicine, 2009
    Co-Authors: Ivy Liu, Bhramar Mukherjee, Thomas F Suesse, David Sparrow, Sung Kyun Park
    Abstract:

    The cumulative logit or the proportional odds Regression Model is commonly used to study covariate effects on ordinal responses. This paper provides some graphical and numerical methods for checking the adequacy of the proportional odds Regression Model. The methods focus on evaluating functional misspecification for specific covariate effects, but misspecification of the link function can also be dealt with under the same framework. For the logistic Regression Model with binary responses, Arbogast and Lin (Statist. Med. 2005; 24:229-247) developed similar graphical and numerical methods for assessing the adequacy of the Model using the cumulative sums of residuals. The paper generalizes their methods to ordinal responses and illustrates them using an example from the VA Normative Aging Study. Simulation studies comparing the performance of the different diagnostic methods indicate that some of the graphical methods are more powerful in detecting Model misspecification than the Hosmer-Lemeshow-type goodness-of-fit statistics for the class of Models studied.