publication . Article . 1997

Adapting regression equations to minimize the mean squared error of predictions made using covariate data from a GIS

D. A. ELSTON; G. JAYASINGHE; S. T. BUCKLAND; D. C. MACMILLAN; R. J. ASPINALL;
Closed Access English
  • Published: 01 Apr 1997
  • Publisher: Taylor & Francis
  • Country: United Kingdom
Abstract
Regression equations between a response variable and candidate explanatory variables are often estimated using a training set of data from closely observed locations but are then applied using covariate data held in a GIS to predict the response variable at locations throughout a region. When the regression assumptions hold and the GIS data are free from error, this procedure gives unbiased estimates of the response variable and minimizes the prediction mean squared error. However, when the explanatory variables in the GIS are recorded with substantially greater errors than were present in the training set, this procedure does not minimize the prediction mean sq...
Subjects
free text keywords: QA75, G, Machine learning, computer.software_genre, computer, Mean integrated squared error, Regression, Covariate, Data mining, Training set, Mean and predicted response, Mean squared prediction error, Statistics, Mean absolute error, Mean squared error, Computer science, Artificial intelligence, business.industry, business
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