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The Use of Simplified or Misspecified Models: Linear Case
Author(s) -
Wu Shaohua,
Harris T. J.,
Mcauley K. B.
Publication year - 2007
Publication title -
the canadian journal of chemical engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.404
H-Index - 67
eISSN - 1939-019X
pISSN - 0008-4034
DOI - 10.1002/cjce.5450850401
Subject(s) - inference , computer science , point estimation , statistical inference , linear model , point (geometry) , confidence interval , econometrics , mathematics , machine learning , statistics , artificial intelligence , geometry
Simplified models have many appealing properties and sometimes give better parameter estimates and model predictions, in sense of mean‐squared‐error, than extended models, especially when the data are not informative. In this paper, we summarize extensive quantitative and qualitative results in the literature concerned with using simplified or misspecified models. Based on confidence intervals and hypothesis tests, we develop a practical strategy to help modellers decide whether a simplified model should be used, and point out the difficulty in making such a decision. We also evaluate several methods for statistical inference for simplified or misspecified models.