Testing Statistical Hypotheses

David J. Olive
Additional contact information
David J. Olive: Southern Illinois University, Department of Mathematics

Chapter Chapter 7 in Statistical Theory and Inference, 2014, pp 183-213 from Springer

Abstract: Abstract A hypothesis is a statement about a population parameter θ, and in hypothesis testing there are two competing hypotheses called the null hypothesis Ho ≡ H 0 and the alternative hypothesis H 1 ≡ H A $$H_{1} \equiv H_{A}$$ . Let Θ 1 and Θ 0 be disjoint sets with Θ i ⊂ Θ where Θ is the parameter space. Then Ho: θ ∈ Θ 0 and H 1: θ ∈ Θ 1.

Keywords: Neyman-Pearson Lemma; Left-tailed Test; Rejection Region; Exponential Family; Likelihood Ratio Test Statistic (search for similar items in EconPapers)
Date: 2014
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:978-3-319-04972-4_7

Ordering information: This item can be ordered from
http://www.springer.com/9783319049724

DOI: 10.1007/978-3-319-04972-4_7

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 ().