NORTHSTAR: A Parameter Estimation Method for the Spatial Autoregression Model

Abstract

Parameter estimation method for the spatial autoregression model (SAR) is important because of the many application domains, such as regional economics, ecology, environmental management, public safety, transportation, public health, business, travel and tourism. However, it is computationally very expensive because of the need to compute the determinant of a large matrix due to Maximum Likelihood Theory. The limitation of previous studies is the need for numerous computations of the computationally expensive determinant term of the likelihood function. In this paper, we present a faster, scalable and NOvel pRediction and estimation TecHnique for the exact SpaTial Auto Regression model solution (NORTHSTAR). We provide a proof of the correctness of this algorithm by showing the objective function to be unimodular. Analytical and experimental results show that the NORTHSTAR algorithm is computationally faster than the related approaches, because it reduces the number of evaluations of the determinant term in the likelihood function.

Open PDF

Document Details

Document Type
Technical Report
Publication Date
Feb 09, 2007
Accession Number
ADA463739

Entities

People

  • Baris M. Kazar
  • Daniel Boley
  • David J. Lilja
  • Mete Celik
  • Shashi Shekhar

Organizations

  • University of Minnesota

Tags

Communities of Interest

  • Space

DTIC Thesaurus Topics

  • Algorithms
  • Birds
  • Computational Complexity
  • Computer Science
  • Data Mining
  • Data Science
  • Databases
  • Electronic Mail
  • Information Science
  • Network Science
  • Normal Distribution
  • Open Water
  • Probability
  • Probability Distributions
  • Spatial Distribution
  • Statistical Analysis
  • Two Dimensional

Readers

  • Distributed Systems and Data Platform Development
  • Economics
  • Regression Analysis.