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Ding-geng Chen - One of the best experts on this subject based on the ideXlab platform.

  • A Homoscedasticity test for the accelerated failure time model
    Computational Statistics, 2018
    Co-Authors: Liang Liu, Ding-geng Chen
    Abstract:

    The semiparametric accelerated failure time (AFT) model is a popular linear model in survival analysis. AFT model and its associated inference methods assume Homoscedasticity of the survival data. It is shown that violation of this Assumption will lead to inefficient parameter estimation and anti-conservative confidence interval estimation, and thus, misleading conclusions in survival data analysis. However, there is no valid statistical test proposed to test the Homoscedasticity Assumption. In this paper, we propose the first novel quasi-likelihood ratio test for the Homoscedasticity Assumption in the AFT model. Simulation studies show the test performs well. A real dataset is used to demonstrate the usefulness of the developed test.

Alexander Von Eye - One of the best experts on this subject based on the ideXlab platform.

  • heteroscedasticity as a basis of direction dependence in reversible linear regression models
    Multivariate Behavioral Research, 2017
    Co-Authors: Wolfgang Wiedermann, Richard Artner, Alexander Von Eye
    Abstract:

    ABSTRACTHeteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the Homoscedasticity Assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diag...

  • Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models
    2017
    Co-Authors: Wolfgang Wiedermann, Richard Artner, Alexander Von Eye
    Abstract:

    Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the Homoscedasticity Assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diagnostics and nine Homoscedasticity tests that can be used to make decisions concerning the direction of effects in linear models. Results of a Monte Carlo simulation that demonstrate the adequacy of the approach are presented. An empirical example is provided, and applicability of the methodology in cases of violated Assumptions is discussed.

Liang Liu - One of the best experts on this subject based on the ideXlab platform.

  • A Homoscedasticity test for the accelerated failure time model
    Computational Statistics, 2018
    Co-Authors: Liang Liu, Ding-geng Chen
    Abstract:

    The semiparametric accelerated failure time (AFT) model is a popular linear model in survival analysis. AFT model and its associated inference methods assume Homoscedasticity of the survival data. It is shown that violation of this Assumption will lead to inefficient parameter estimation and anti-conservative confidence interval estimation, and thus, misleading conclusions in survival data analysis. However, there is no valid statistical test proposed to test the Homoscedasticity Assumption. In this paper, we propose the first novel quasi-likelihood ratio test for the Homoscedasticity Assumption in the AFT model. Simulation studies show the test performs well. A real dataset is used to demonstrate the usefulness of the developed test.

Wolfgang Wiedermann - One of the best experts on this subject based on the ideXlab platform.

  • heteroscedasticity as a basis of direction dependence in reversible linear regression models
    Multivariate Behavioral Research, 2017
    Co-Authors: Wolfgang Wiedermann, Richard Artner, Alexander Von Eye
    Abstract:

    ABSTRACTHeteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the Homoscedasticity Assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diag...

  • Heteroscedasticity as a Basis of Direction Dependence in Reversible Linear Regression Models
    2017
    Co-Authors: Wolfgang Wiedermann, Richard Artner, Alexander Von Eye
    Abstract:

    Heteroscedasticity is a well-known issue in linear regression modeling. When heteroscedasticity is observed, researchers are advised to remedy possible model misspecification of the explanatory part of the model (e.g., considering alternative functional forms and/or omitted variables). The present contribution discusses another source of heteroscedasticity in observational data: Directional model misspecifications in the case of nonnormal variables. Directional misspecification refers to situations where alternative models are equally likely to explain the data-generating process (e.g., x → y versus y → x). It is shown that the Homoscedasticity Assumption is likely to be violated in models that erroneously treat true nonnormal predictors as response variables. Recently, Direction Dependence Analysis (DDA) has been proposed as a framework to empirically evaluate the direction of effects in linear models. The present study links the phenomenon of heteroscedasticity with DDA and describes visual diagnostics and nine Homoscedasticity tests that can be used to make decisions concerning the direction of effects in linear models. Results of a Monte Carlo simulation that demonstrate the adequacy of the approach are presented. An empirical example is provided, and applicability of the methodology in cases of violated Assumptions is discussed.

Yu Lili - One of the best experts on this subject based on the ideXlab platform.

  • Quasi-likelihood Ratio Tests for Homoscedasticity of Variance in Linear Regression
    Digital Commons@Georgia Southern, 2019
    Co-Authors: Yu Lili, Sevilimedu Varadan, Vogel Robert, Samawi Hani
    Abstract:

    Two quasi-likelihood ratio tests are proposed for the Homoscedasticity Assumption in the linear regression models. They require few Assumptions than the existing tests. The properties of the tests are investigated through simulation studies. An example is provided to illustrate the usefulness of the new proposed tests

  • Weighted least-squares method for right-censored data in accelerated failure time model
    Digital Commons@Georgia Southern, 2014
    Co-Authors: Yu Lili
    Abstract:

    Presented at 2014 ICSA symposium Program The classical accelerated failure time (AFT) model has been extensively investigated due to its direct interpretation of the covariate effects on the mean survival time in survival analysis. However, this classical AFT model and its associated methodologies are built on the fundamental Assumption of data Homoscedasticity. Consequently, when the Homoscedasticity Assumption is violated as often seen in the real applications, the estimators lose efficiency and the associated inference is not reliable. Furthermore, none of the existing methods can estimate the intercept consistently. To overcome these drawbacks, we propose a semiparametric approach in this paper for both homoscedastic and heteroscedastic data. This approach utilizes a weighted least-squares equation with synthetic observations weighted by square root of their variances where the variances are estimated via the local polynomial regression. We establish the limiting distributions of the resulting coefficient estimators and prove that both slope parameters and the intercept can be consistently estimated. We evaluate the finite sample performance of the proposed approach through simulation studies and demonstrate its superiority through real example on its efficiency and reliability over the existing methods when the data is heteroscedastic

  • Weighted Least-Squares Method for Right-Censored Data in Accelerated Failure Time Model
    'Wiley', 2013
    Co-Authors: Yu Lili, Liu Liang, Chen Dinggeng
    Abstract:

    The classical accelerated failure time (AFT) model has been extensively investigated due to its direct interpretation of the covariate effects on the mean survival time in survival analysis. However, this classical AFT model and its associated methodologies are built on the fundamental Assumption of data Homoscedasticity. Consequently, when the Homoscedasticity Assumption is violated as often seen in the real applications, the estimators lose efficiency and the associated inference is not reliable. Furthermore, none of the existing methods can estimate the intercept consistently. To overcome these drawbacks, we propose a semiparametric approach in this article for both homoscedastic and heteroscedastic data. This approach utilizes a weighted least-squares equation with synthetic observations weighted by square root of their variances where the variances are estimated via the local polynomial regression. We establish the limiting distributions of the resulting coefficient estimators and prove that both slope parameters and the intercept can be consistently estimated. We evaluate the finite sample performance of the proposed approach through simulation studies and demonstrate its superiority through real example on its efficiency and reliability over the existing methods when the data is heteroscedastic