EconPapers    
Economics at your fingertips  
 

Multivariate Density Estimation Using a Multivariate Weighted Log-Normal Kernel

Gaku Igarashi ()
Additional contact information
Gaku Igarashi: University of Tsukuba

Sankhya A: The Indian Journal of Statistics, 2018, vol. 80, issue 2, No 3, 247-266

Abstract: Abstract This paper suggests a multivariate asymmetric kernel density estimation using a multivariate weighted log-normal (LN) kernel for non-negative multivariate data. Asymptotic properties of the multivariate weighted LN kernel density estimator are studied. Simulation studies are also conducted in the bivariate situation.

Keywords: Nonparametric density estimation; Boundary problem; Asymmetric kernel; Multivariate log-normal density; Primary 62G07; Secondary 62G20 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
http://link.springer.com/10.1007/s13171-018-0125-y 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:sankha:v:80:y:2018:i:2:d:10.1007_s13171-018-0125-y

Ordering information: This journal article can be ordered from
http://www.springer.com/statistics/journal/13171

DOI: 10.1007/s13171-018-0125-y

Access Statistics for this article

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

More articles in Sankhya A: 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 2025-03-20
Handle: RePEc:spr:sankha:v:80:y:2018:i:2:d:10.1007_s13171-018-0125-y