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Summary on Application of Regression Analysis in Water Science

Wang Guo

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Abstract

With the development of Statistics, regression analysis theory is evolving, and the regression algorithms are found wide applications in water science, and the wide applications of the regression algorithms can promote the development of water science. The new regression analysis methods applied in water science such as bridge regression, principal component regression, robust regression, auto-regression, envelope regression, multi-stratum recursive regression, fuzzy regression and grey regression are introduced systematically in this paper. The advantages and applicable scopes of each regression method are also induced and summarized.

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What this paper is about

With the development of Statistics, regression analysis theory is evolving, and the regression algorithms are found wide applications in water science, and the wide applications of the regression algorithms can promote the development of water science. The new regression analysis methods applied in water science such as bridge regression, principal component regression, robust regression, auto-regression, envelope regression, multi-stratum recursive regression, fuzzy regression and grey regression are introduced systematically in this paper. The advantages and applicable scopes of each regression method are also induced and summarized.

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Available abstract

With the development of Statistics, regression analysis theory is evolving, and the regression algorithms are found wide applications in water science, and the wide applications of the regression algorithms can promote the development of water science. The new regression analysis methods applied in water science such as bridge regression, principal component regression, robust regression, auto-regression, envelope regression, multi-stratum recursive regression, fuzzy regression and grey regression are introduced systematically in this paper. The advantages and applicable scopes of each regression method are also induced and summarized.

Key concepts: Regression analysis, Regression, Segmented regression, Factor regression model, Regression diagnostic, Cross-sectional regression, Multivariate adaptive regression splines, Principal component regression

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