Mathematical preparation

Hajime Seya; Yoshiki Yamagata

doi:10.1016/B978-0-12-813127-5.00002-3

Mathematical preparation

Research output: Chapter in Book/Report/Conference proceeding › Chapter

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 language	English
Title of host publication	Spatial Analysis Using Big Data
Subtitle of host publication	Methods and Urban Applications
Publisher	Elsevier
Pages	9-31
Number of pages	23
ISBN (Electronic)	9780128131329
ISBN (Print)	9780128131275
DOIs	https://doi.org/10.1016/B978-0-12-813127-5.00002-3
Publication status	Published - 2019 Nov 2
Externally published	Yes

Keywords

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

ASJC Scopus subject areas

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

Access to Document

10.1016/B978-0-12-813127-5.00002-3

Cite this

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KW - Additive model

KW - Bayesian statistics

KW - Classical linear regression model

KW - Generalized linear model

KW - MCMC

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Mathematical preparation

Abstract

Keywords

ASJC Scopus subject areas

Access to Document

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