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Locally Weighted Maximum Likelihood Estimation: Monte Carlo Evidence and an Application

  • Daniel P. McMillen
  • John F. McDonald
Part of the Advances in Spatial Science book series (ADVSPATIAL)

Abstract

Even small cities have complicated spatial patterns that are difficult to model adequately with a small number of explanatory variables. Shopping centers, parks, lakes, and the like have local effects on variables such as housing prices, land values, and population density. Proximity to such sites can be included as explanatory variables, but the number of potential sites is large and some may be unknown beforehand. Coefficient estimates are biased when relevant sites are omitted, but are inefficient when unimportant ones are included. Moreover, functional forms are often complex for urban spatial patterns even in the absence of local peaks and valleys.

Keywords

Root Mean Square Error South Side Locally Weight Monte Carlo Result Density Zoning 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Daniel P. McMillen
    • 1
  • John F. McDonald
    • 1
  1. 1.University of IllinoisChicagoUSA

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