Least squares with inequality restrictions: a symmetric positive-definite linear complementarity problem algorithm

When finding the least squares estimate in a full rank regression subject to inequality constraints, a symmetric positive-definite linear complementarity problem is encountered if the set of linear constraints is full rank. This particular problem is analyzed here, and an algorithm is proposed. A co...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Veröffentlicht in:	Journal of statistical computation and simulation 1987-08, Vol.28 (2), p.127-143
Hauptverfasser:	Quintana†, José M., Federico, O'Reilly J., Gómez, Susana
Format:	Artikel
Sprache:	eng
Schlagworte:	Exact sciences and technology Least Squares Linear Complementarity Markov processes Mathematics Probability and statistics Probability theory and stochastic processes Sciences and techniques of general use
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

Beschreibung
Zusammenfassung:	When finding the least squares estimate in a full rank regression subject to inequality constraints, a symmetric positive-definite linear complementarity problem is encountered if the set of linear constraints is full rank. This particular problem is analyzed here, and an algorithm is proposed. A comparison is done between this algorithm and Lemke's algorithm. An Apple II microcomputer is used for the comparison.
ISSN:	0094-9655 1563-5163
DOI:	10.1080/00949658708811021