| Evaluation of Inference Adequacy in Cumulative Logistic Regression Models： An Empirical Validation of ISW-Ridge Relationships |
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| 作者姓名： | Hsien-Chueh Peter YANG Alex Kung-Hsiung CHANG Tsung-Hao CHEN |
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| 作者单位： | Department of Risk Management and Insurance National Kaohsiung First University of Science and Technology，Department of Business Administration，National Pingtung University of Science and Technology，Department of Business Administration，MingDao University，Kaohsiung 811，China，Pingtung，China，ChangHua52345，China |
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| 基金项目： | This paper was financially supported by NSC96-2628-E-366-004-MY2 and NSC96-2628-E-132-001-MY2 |
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| 摘 要： | Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p<0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to check the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1) and potential energy (X2) significantly impact (p<0.0001) the amplitude-based reflected rate; the P-values for the deviance and Pearson are all >0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height (X1) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model.Investigation of 6 predictive powers (R2, Max-rescaled R2, Somers'D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model.
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| 关 键 词： | 海退 逻辑回归模型 振幅 内部独立波 |
Evaluation of Inference Adequacy in Cumulative Logistic Regression Models:An Empirical Validation of ISW-Ridge Relationships |
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| Hsien-Chueh Peter YANG,Alex Kung-Hsiung CHANG,Tsung-Hao CHEN.Evaluation of Inference Adequacy in Cumulative Logistic Regression Models:An Empirical Validation of ISW-Ridge Relationships[J].China Ocean Engineering,2008,22(1). |
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| Authors: | Cheng-Wu CHEN Hsien-Chueh Peter YANG Chen-Yuan CHEN Alex Kung-Hsiung CHANG Tsung-Hao CHEN |
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| Institution: | [1]Department of Logistics Management, Shu-Te University, Kaohsiung 82445, China [2]Department of Risk Management and Insurance, National Knohsiung First Univershy of Scierwe and Technology, Kaohsiung 811, China [3]Department of Management Information System, Yung- Ta Institute of Technology and Commerce, Pingtung County 90941, China [4]Department of Business Administration, National Pingtung University of Science and Technology Pingtung, China [5]Department of Business Adrninistration, MingDao University, ChangHua 52345, China |
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| Abstract: | Internal solitary wave propagation over a submarine ridge results in energy dissipation, in which the hydrodynamic interaction between a wave and ridge affects marine environment. This study analyzes the effects of ridge height and potential energy during wave-ridge interaction with a binary and cumulative logistic regression model. In testing the Global Null Hypothesis, all values are p<0.001, with three statistical methods, such as Likelihood Ratio, Score, and Wald. While comparing with two kinds of models, tests values obtained by cumulative logistic regression models are better than those by binary logistic regression models. Although this study employed cumulative logistic regression model, three probability functions p^1, p^2 and p^3, are utilized for investigating the weighted influence of factors on wave reflection. Deviance and Pearson tests are applied to check the goodness-of-fit of the proposed model. The analytical results demonstrated that both ridge height (X1) and potential energy (X2) significantly impact (p<0.0001) the amplitude-based reflected rate; the P-values for the deviance and Pearson are all >0.05 (0.2839, 0.3438, respectively). That is, the goodness-of-fit between ridge height (X1) and potential energy (X2) can further predict parameters under the scenario of the best parsimonious model.Investigation of 6 predictive powers (R2, Max-rescaled R2, Somers'D, Gamma, Tau-a, and c, respectively) indicate that these predictive estimates of the proposed model have better predictive ability than ridge height alone, and are very similar to the interaction of ridge height and potential energy. It can be concluded that the goodness-of-fit and prediction ability of the cumulative logistic regression model are better than that of the binary logistic regression model. |
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| Keywords: | binary logistic regression cumulative logistic regression model goodness-of-fit internal solitary wave amplitude-based transmission rate |
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