The Annals of Statistics

Strong Consistency of Least Squares Estimates in Normal Linear Regression

T. W. Anderson and John B. Taylor

Full-text: Open access

Abstract

In the usual linear regression model the sample regression coefficients converge with probability one to the population regression coefficients when the dependent variables are normally distributed and the inverse of the second-order moment matrix of the independent variables converges to the zero matrix.

Article information

Source
Ann. Statist., Volume 4, Number 4 (1976), 788-790.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176343552

Digital Object Identifier
doi:10.1214/aos/1176343552

Mathematical Reviews number (MathSciNet)
MR415899

Zentralblatt MATH identifier
0339.62039

JSTOR
links.jstor.org

Subjects
Primary: 62J05: Linear regression
Secondary: 60F15: Strong theorems

Keywords
Least squares estimates linear regression strong consistency

Citation

Anderson, T. W.; Taylor, John B. Strong Consistency of Least Squares Estimates in Normal Linear Regression. Ann. Statist. 4 (1976), no. 4, 788--790. doi:10.1214/aos/1176343552. https://projecteuclid.org/euclid.aos/1176343552


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