On the strong consistency of least squares estimates in stochastic regression models with associated errors

João Filipe Lita da Silva

On the strong consistency of least squares estimates in stochastic regression models with associated errors

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Strong consistency of the least-squares estimates in stochastic regression models is established considering associated errors with finite variance and assuming a moderate asymptotic condition on the stochastic regressors. Strong consistency of the ridge estimates is also obtained for some biasing parameters in the referred
set of assumptions.

Original language	English
Title of host publication	Mathematical Methods in Science and Mechanics
Editors	Nikos Mastorakis, Francesco Mainardi, Mariofanna Milanova
Place of Publication	Lisbon
Publisher	World Scientific and Engineering Academy and Society
Pages	58-64
Number of pages	7
ISBN (Print)	978-960-474-396-4
Publication status	Published - 1 Nov 2014

Publication series

Name	Mathematics and Computers in Science and Engineering Series
Publisher	WSEAS Press
Number	33
ISSN (Print)	2227-4588

Cite this

Lita da Silva, J. F. (2014). On the strong consistency of least squares estimates in stochastic regression models with associated errors. In N. Mastorakis, F. Mainardi, & M. Milanova (Eds.), Mathematical Methods in Science and Mechanics (pp. 58-64). (Mathematics and Computers in Science and Engineering Series; No. 33). World Scientific and Engineering Academy and Society.

Lita da Silva, João Filipe. / On the strong consistency of least squares estimates in stochastic regression models with associated errors. Mathematical Methods in Science and Mechanics. editor / Nikos Mastorakis ; Francesco Mainardi ; Mariofanna Milanova. Lisbon : World Scientific and Engineering Academy and Society, 2014. pp. 58-64 (Mathematics and Computers in Science and Engineering Series; 33).

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On the strong consistency of least squares estimates in stochastic regression models with associated errors. / Lita da Silva, João Filipe.
Mathematical Methods in Science and Mechanics. ed. / Nikos Mastorakis; Francesco Mainardi; Mariofanna Milanova. Lisbon: World Scientific and Engineering Academy and Society, 2014. p. 58-64 (Mathematics and Computers in Science and Engineering Series; No. 33).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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AB - Strong consistency of the least-squares estimates in stochastic regression models is established considering associated errors with finite variance and assuming a moderate asymptotic condition on the stochastic regressors. Strong consistency of the ridge estimates is also obtained for some biasing parameters in the referredset of assumptions.

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T3 - Mathematics and Computers in Science and Engineering Series

SP - 58

EP - 64

BT - Mathematical Methods in Science and Mechanics

A2 - Mastorakis, Nikos

A2 - Mainardi, Francesco

A2 - Milanova, Mariofanna

PB - World Scientific and Engineering Academy and Society

CY - Lisbon

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On the strong consistency of least squares estimates in stochastic regression models with associated errors

Abstract

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