Mathematical preparation

Hajime Seya, Yoshiki Yamagata

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

Section 2.1 of this chapter defines the typical notations. Section 2.2 explains the classical linear regression model with typical assumptions and discusses what may happen when these assumptions are violated. Estimation techniques such as ordinary least squares, two-stage least squares, and generalized method of moments are briefly explained. Sections 2.3 and 2.4 introduce two advanced regression models, the generalized linear model and the additive model. Section 2.5 explains the basics of Bayesian statistics and the Markov chain Monte Carlo in terms of a regression model.

Original languageEnglish
Title of host publicationSpatial Analysis Using Big Data
Subtitle of host publicationMethods and Urban Applications
PublisherElsevier
Pages9-31
Number of pages23
ISBN (Electronic)9780128131329
ISBN (Print)9780128131275
DOIs
Publication statusPublished - 2019 Nov 2
Externally publishedYes

Keywords

  • Additive model
  • Bayesian statistics
  • Classical linear regression model
  • Generalized linear model
  • MCMC

ASJC Scopus subject areas

  • Economics, Econometrics and Finance(all)
  • General Business,Management and Accounting

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