EconPapers    
Economics at your fingertips  
 

A Survey of Nonparametric Mixing Density Estimation via the Predictive Recursion Algorithm

Ryan Martin ()
Additional contact information
Ryan Martin: North Carolina State University

Sankhya B: The Indian Journal of Statistics, 2021, vol. 83, issue 1, No 5, 97-121

Abstract: Abstract Nonparametric estimation of a mixing density based on observations from the corresponding mixture is a challenging statistical problem. This paper surveys the literature on a fast, recursive estimator based on the predictive recursion algorithm. After introducing the algorithm and giving a few examples, I summarize the available asymptotic convergence theory, describe an important semiparametric extension, and highlight two interesting applications. I conclude with a discussion of several recent developments in this area and some open problems.

Keywords: Empirical Bayes; high-dimensional inference; Jayanta K. Ghosh; mixture model; recursive estimation.; Primary 62G07; Secondary 62C12 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s13571-019-00206-w Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:sankhb:v:83:y:2021:i:1:d:10.1007_s13571-019-00206-w

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/13571

DOI: 10.1007/s13571-019-00206-w

Access Statistics for this article

Sankhya B: The Indian Journal of Statistics is currently edited by Dipak Dey

More articles in Sankhya B: The Indian Journal of Statistics from Springer, Indian Statistical Institute
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2022-05-12
Handle: RePEc:spr:sankhb:v:83:y:2021:i:1:d:10.1007_s13571-019-00206-w