Average Predictive Comparisons for Models with Nonlinearity, Interactions, and Variance Components
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چکیده
In a predictive model, what is the expected difference in the outcome associated with a unit difference in one of the inputs? In a linear regression model without interactions, this average predictive comparison is simply a regression coefficient (with associated uncertainty). In a model with nonlinearity or interactions, however, the average predictive comparison in general depends on the values of the predictors. We consider various definitions based on averages over a population distribution of the predictors, and we compute standard errors based on uncertainty in model parameters. We illustrate with a study of criminal justice data for urban counties in the United States. The outcome of interest measures whether a convicted felon received a prison sentence rather than a
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Average predictive effects for models with nonlinearity, interactions, and variance components∗
In a predictive model, what is the expected change in the outcome associated with a unit change in one of the inputs? In a linear regression model without interactions, this average predictive effect is simply a regression coefficient (with associated uncertainty). In a model with nonlinearity or interactions, however, the average predictive effect in general depends on the values of the predic...
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تاریخ انتشار 2006