Analysis of regression in game theory approach
Analysis of regression in game theory approach
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DOI：
10.1002/asmb.446
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发表时间：
2001-10-01
影响因子：
1.4
通讯作者：
Conklin, M
中科院分区：
文献类型：
--
作者：
Lipovetsky, S;Conklin, M
使用多元回归分析时，研究人员通常想知道模型中预测因子的相对重要性。然而，由于回归变量之间的多重共线性，分析可能变得困难。其从假定有用的回归量产生偏置系数和负输入到多重确定。为了解决这个问题，我们应用合作博弈论的工具，Shapley值归责。我们证明了Shapley值的理论和实践优势，并表明它在存在多重共线性的情况下提供了一致的结果。版权所有（C）2001约翰威利父子有限公司
Working with multiple regression analysis a researcher usually wants to know a comparative importance of predictors in the model. However, the analysis can be made difficult because of multicollinearity among regressors. which produces biased coefficients and negative inputs to multiple determination from presumably useful regressors. To solve this problem we apply a tool from the co-operative games theory, the Shapley Value imputation. We demonstrate the theoretical and practical advantages of the Shapley Value and show that it provides consistent results in the presence of multicollinearity. Copyright (C) 2001 John Wiley & Sons, Ltd.