Linear Modelling: LM, GLM, GAM and Mixed Models

Marcel van Oijen

Chapter Chapter 20 in Bayesian Compendium, 2024, pp 165-170 from Springer

Abstract: Abstract Science increasingly recognises the nonlinearities in nature, and Bayesian methods can handle nonlinear models without any problem. However, linear modelling remains the default statistical approach for many, and it is therefore important to be familiar with the field. This chapter gives a brief overview of the most common classes of linear modelling.

Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-66085-6_20

Ordering information: This item can be ordered from
http://www.springer.com/9783031660856

DOI: 10.1007/978-3-031-66085-6_20

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().