Revisiting Local Asymptotic Normality (LAN) and Passing on to Local Asymptotic Mixed Normality (LAMN) and Local Asymptotic Quadratic (LAQ) Experiments

George G. Roussas () and Debasis Bhattacharya
Additional contact information
George G. Roussas: University of California, Department of Statistics

Chapter Chapter 17 in Advances in Directional and Linear Statistics, 2011, pp 253-280 from Springer

Abstract: Abstract Let X 1, …, X n be a random sample of size n from an underlying parametric statistical model. Then the basic statistical problem may be stated as follows: On the basis of a random sample, whose probability law depends on a parameter θ, discriminate between two values θ and θ ∗ (θ≠θ∗). When the parameters are sufficiently far apart, any decent statistical procedure will do the job. A problem arises when the parameter points are close together, and yet the corresponding probability measures are substantially or even vastly different. The present paper revolves around ways of resolving such a problem. The concepts and methodology used are those of contiguity, Local Asymptotic Normality (LAN), Local Asymptotic Mixed Normality (LAMN), and Local Asymptotic Quadratic (LAQ) experiments.

Keywords: Probability Measure; Exponential Family; Inst Stat Math; Regular Estimate; Local Asymptotic Normality (search for similar items in EconPapers)
Date: 2011
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:978-3-7908-2628-9_17

Ordering information: This item can be ordered from
http://www.springer.com/9783790826289

DOI: 10.1007/978-3-7908-2628-9_17

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 ().