The curve-fitting problem: An objectivist view

Philosophy of Science 68 (2):218-241 (2001)
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Abstract

Model simplicity in curve fitting is the fewness of parameters estimated. I use a vector model of least squares estimation to show that degrees of freedom, the difference between the number of observed parameters fit by the model and the number of explanatory parameters estimated, are the number of potential dimensions in which data are free to differ from a model and indicate the disconfirmability of the model. Though often thought to control for parameter estimation, the AIC and similar indices do not do so for all model applications, while goodness of fit indices like chi-square, which explicitly take into account degrees of freedom, do. Hypothesis testing with prespecified values for parameters is based on a metaphoric regulative subject/object schema taken from object perception and has as its goal the accumulation of objective knowledge

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Citations of this work

On the Philosophy of Unsupervised Learning.David S. Watson - 2023 - Philosophy and Technology 36 (2):1-26.
Predictive accuracy as an achievable goal of science.Malcolm R. Forster - 2002 - Proceedings of the Philosophy of Science Association 2002 (3):S124-S134.
Predictive Accuracy as an Achievable Goal of Science.Malcolm R. Forster - 2002 - Philosophy of Science 69 (S3):S124-S134.

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