Best subsets regression using LP-norms with 1≤p<∞

Manfred Precht; H. Redinger

doi:10.1007/BF02933549

Best subsets regression using L_P-norms with 1≤p<∞

Manfred Precht, H. Redinger

Life Sciences

Research output: Contribution to journal › Article › peer-review

Abstract

This paper describes an algorithm for determining the best subset of k variables in regression problems using any L_P-norm with 1≤p<∞. The procedure is based on partial enumeration. It also makes a suggestion for finding the equation with least prediction error.

Original language	English
Pages (from-to)	128-134
Number of pages	7
Journal	Statistische Hefte
Volume	22
Issue number	2
DOIs	https://doi.org/10.1007/BF02933549
State	Published - Jun 1964

Access to Document

10.1007/BF02933549

Cite this

@article{2222eba9c8d241c68ffbdafc2363d7f8,

title = "Best subsets regression using LP-norms with 1≤p<∞",

abstract = "This paper describes an algorithm for determining the best subset of k variables in regression problems using any LP-norm with 1≤p<∞. The procedure is based on partial enumeration. It also makes a suggestion for finding the equation with least prediction error.",

author = "Manfred Precht and H. Redinger",

year = "1964",

month = jun,

doi = "10.1007/BF02933549",

language = "English",

volume = "22",

pages = "128--134",

journal = "Statistische Hefte",

issn = "0039-0631",

publisher = "Springer Verlag",

number = "2",

}

TY - JOUR

T1 - Best subsets regression using LP-norms with 1≤p<∞

AU - Precht, Manfred

AU - Redinger, H.

PY - 1964/6

Y1 - 1964/6

N2 - This paper describes an algorithm for determining the best subset of k variables in regression problems using any LP-norm with 1≤p<∞. The procedure is based on partial enumeration. It also makes a suggestion for finding the equation with least prediction error.

AB - This paper describes an algorithm for determining the best subset of k variables in regression problems using any LP-norm with 1≤p<∞. The procedure is based on partial enumeration. It also makes a suggestion for finding the equation with least prediction error.

UR - https://www.scopus.com/pages/publications/52449148473

U2 - 10.1007/BF02933549

DO - 10.1007/BF02933549

M3 - Article

AN - SCOPUS:52449148473

SN - 0039-0631

VL - 22

SP - 128

EP - 134

JO - Statistische Hefte

JF - Statistische Hefte

IS - 2

ER -

Best subsets regression using L_P-norms with 1≤p<∞

Abstract

Access to Document

Other files and links

Fingerprint

Cite this