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    Volume 47, Issue 1 (January 2019)

    On Comparing Two Dependent Linear and Nonlinear Regression Models

    (Received 7 August 2017; accepted 15 November 2017)

    Published Online: 2018

    CODEN: JTEVAB

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    Abstract

    Linear and nonlinear regression models are flexible methods of data analysis that may be appropriate whenever a quantitative response variable is to be examined in relationship to any other explanatory variables. This relationship can be expressed by different models and equations. In some fields, such as in agriculture, biology, hydrology, neural network, and psychology, researchers need to analyze whether the relationship between response variable and predictor variables differ in two fitted models on the same dataset. In other words, we are interested in the comparison of two regression models for a single dataset. In this article, we will use the nonparametric methods to establish hypothesis testing for the equality of two dependent regression models. Then, a simulation study is provided to investigate the performance of the proposed method. Also, the proposed method is applied to compare the different linear, quadratic, cubic, and exponential models that can be fitted on a real dataset.

    Author Information:

    Mahmoudi, Mohammad Reza
    Department of Statistics, Faculty of Science, Fasa University, Fasa, Fars


    Stock #: JTE20170461

    ISSN:0090-3973

    DOI: 10.1520/JTE20170461

    Author
    Title On Comparing Two Dependent Linear and Nonlinear Regression Models
    Symposium ,
    Committee G03