Loading...
A convergent algorithm for a generalized multivariate isotonic regression problem
Hansohm, Juergen Hu, Xiaomi
Hansohm, Juergen
Hu, Xiaomi
Authors
Other Names
Location
Time Period
Advisors
Original Date
Digitization Date
Issue Date
2012-02
Type
Article
Genre
Keywords
Multivariate isotonic regression,Projection,Dykstra's algorithm,Partial order,Least squares solution
Subjects (LCSH)
Citation
Hansohm, Jèurgen, and Xiaomi Hu. 2012. "A convergent algorithm for a generalized multivariate isotonic regression problem". Statistical Papers. 53 (1): 107-115.
Abstract
Sasabuchi et al. (Biometrika 70(2):465-472, 1983) introduces a multivariate version of the well-known univariate isotonic regression which plays a key role in the field of statistical inference under order restrictions. His proposed algorithm for computing the multivariate isotonic regression, however, is guaranteed to converge only under special conditions (Sasabuchi et al., J Stat Comput Simul 73(9):619-641, 2003). In this paper, a more general framework for multivariate isotonic regression is given and an algorithm based on Dykstra's method is used to compute the multivariate isotonic regression. Two numerical examples are given to illustrate the algorithm and to compare the result with the one published by Fernando and Kulatunga (Comput Stat Data Anal 52:702-712, 2007).
Table of Contents
Description
Click on the DOI link below to access the article (may not be free).
Publisher
Springer
Journal
Book Title
Series
Statistical Papers;2012:, v.53, no.1
Digital Collection
Finding Aid URL
Use and Reproduction
Archival Collection
PubMed ID
DOI
ISSN
0932-5026
1613-9798
1613-9798