Nonlinear Regression with Structured Inputs
نویسنده
چکیده
For many applications of nonlinear regression, theory does not guide the model building process by suggesting a relevant functional form. This research develops a new technique for use in this situation. Called STAT-ANN, the new method provides modelling flexibility similar to a neural network but provides a statistical basis for model selection, interpretation, and validation by following a two-step process. We first generate an extended input basis and then fit a nonlinear prediction function. We present a theoretical derivation of the new method, develop a model-building paradigm, and demonstrate its use with a marketing application.
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