Weak and strong consistency of Simple Least Squares Estimators in regression models and uniform strong consistency of residual sample spectral density in the error process
Year: 1978
Type: article
Abstract: Some conditions for weak and strong consistency of Simple Least Squares Estimators for regression parameters are given in the cases where a sequence of the error random variables is (i) uncorrelated, (ii) independent, (iii) independentlly identically Normally distributed and (iv) weakly stationary. A set of sufficient conditions for uniform strong consistency of a proposed estimator for the spectral density function of the strictly stationary and ergodic error process is given.
Source: Keio Engineering Reports
Author: Yasuyuki Toyooka
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Funder: Keio University
OpenAlex ID: W640636660