Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Optimal estimation of direction in regression models with large number of parameters

Gillard, Jonathan ORCID: and Zhigljavsky, Anatoly ORCID: 2018. Optimal estimation of direction in regression models with large number of parameters. Applied Mathematics and Computation 318 , pp. 281-289. 10.1016/j.amc.2017.05.050

[thumbnail of optimal_directions7.pdf]
PDF - Accepted Post-Print Version
Download (340kB) | Preview


We consider the problem of estimating the optimal direction in regression by maximizing the probability that the scalar product between the vector of unknown parameters and the chosen direction is positive. The estimator maximizing this probability is simple in form, and is especially useful for situations where the number of parameters is much larger than the number of observations. We provide examples which show that this estimator is superior to state-of-the-art methods such as the LASSO for estimating the optimal direction.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Mathematics
Subjects: Q Science > QA Mathematics
Uncontrolled Keywords: Random balance; Screening experiments; Box–Wilson methodology; LASSO; Ridge regression
Publisher: Elsevier
ISSN: 0096-3003
Date of First Compliant Deposit: 1 June 2017
Date of Acceptance: 14 May 2017
Last Modified: 09 Nov 2023 18:05

Citation Data

Cited 1 time in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics