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• W2024452075 abstract "Strategies for model selection within the regression framework typically involve choices among several sometimes competing criteria. In this article, the interrelated criteria of goodness-of-fit and parameter invariance are explored with respect to a class of maximum likelihood network autocorrelation models. A GLS measure of generalized goodness-of-fit, R 2 G , is proposed for these models based on the equivalence of ML and GLS in the exponential family. This R 2 G statistic can be used to test for stability of parameters across various samples or subsamples. A second test of parameter invariance across subsamples is proposed: Schwarz's (1978) information Criterion. An example illustrates how these identification and testing procedures may be jointly used to help select the most adequate model for a given data set." @default.
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• W2024452075 date "1986-05-01" @default.
• W2024452075 modified "2023-09-25" @default.
• W2024452075 title "Model Selection Procedures for Network Autocorrelated Disturbances Models" @default.
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• W2024452075 doi "https://doi.org/10.1177/0049124186014004003" @default.
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