Abstract
In the practical cases, we are usually faced with the more difficult problem of multicollinearity in our fitted regression model. Multicollinearity will arise when there are approximate linear relationships between two or more independent variables. It may cause some serious problems in validation, interpretation, and analysis of the model, such as unstable estimates, unreasonable sing, high-standard errors, and so on. Although there are some methods to solve or avoid this problem, we will propose another alternative from the practical view in this paper, called nested estimate procedure. The first half of the paper explains the concept and process of this procedure, and the second half provides two examples to illustrate this procedure’s suitability and reliability.
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Lin, FJ. Solving Multicollinearity in the Process of Fitting Regression Model Using the Nested Estimate Procedure. Qual Quant 42, 417–426 (2008). https://doi.org/10.1007/s11135-006-9055-1
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DOI: https://doi.org/10.1007/s11135-006-9055-1