AbstractThe rate of convergence of the least squares estimator in a non-linear regression model with errors forming either a φ-mixing or strong mixing process is obtained. Strong consistency of the least squares estimator is obtained as a corollary
The strong convergence rates in nonparametric regression estimation have been mostly discussed when ...
This paper considers the linear regression model with multiple stochastic regressors, intercept, and...
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
The least squares estimator for the linear regression model is shown to converge to the true paramet...
The asymptotic properties of the least squares estimator are derived for a non regular nonlinear mod...
The paper uses empirical process techniques to study the asymptotics of the least-squares estimator ...
AbstractA recent theorem of T. L. Hai, H. Robbins, and C. Z. Wei (J. Multivariate Anal. 9 (1979), 34...
AbstractThe paper uses empirical process techniques to study the asymptotics of the least-squares es...
Abstract We consider the moving least-squares (MLS) method by the regression learning framework unde...
. A nonlinear regression model with correlated, normally distributed errors is investigated. The bia...
Abstract. General conditions for convergence rates of nonparametric or-thogonal series estimators of...
This paper looks at the strong consistency of the ordinary least squares (OLS) estimator in linear r...
In this paper we study the rate of convergence to the normal approximation of the least squares esti...
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
AbstractThe effect of dependent errors in fixed-design, nonparametric regression is investigated. It...
The strong convergence rates in nonparametric regression estimation have been mostly discussed when ...
This paper considers the linear regression model with multiple stochastic regressors, intercept, and...
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
The least squares estimator for the linear regression model is shown to converge to the true paramet...
The asymptotic properties of the least squares estimator are derived for a non regular nonlinear mod...
The paper uses empirical process techniques to study the asymptotics of the least-squares estimator ...
AbstractA recent theorem of T. L. Hai, H. Robbins, and C. Z. Wei (J. Multivariate Anal. 9 (1979), 34...
AbstractThe paper uses empirical process techniques to study the asymptotics of the least-squares es...
Abstract We consider the moving least-squares (MLS) method by the regression learning framework unde...
. A nonlinear regression model with correlated, normally distributed errors is investigated. The bia...
Abstract. General conditions for convergence rates of nonparametric or-thogonal series estimators of...
This paper looks at the strong consistency of the ordinary least squares (OLS) estimator in linear r...
In this paper we study the rate of convergence to the normal approximation of the least squares esti...
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
AbstractThe effect of dependent errors in fixed-design, nonparametric regression is investigated. It...
The strong convergence rates in nonparametric regression estimation have been mostly discussed when ...
This paper considers the linear regression model with multiple stochastic regressors, intercept, and...
Sufficient conditions are given to ensure the existence of a sequence of strongly consistent estimat...
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