Add to ORKGIn the normal linear regression the least square estimation of the coefficients has a series of nice properties. In addition the needed numerical calculations may be done a fairly efficient way. For the case when the application of this widely used least square method is not justified some further methods are suggested. But experience about their statistical and numerical properties can hardly be found. This paper intends to help the practitioner to become familiar with a type of non-least-square regression methods
Limitations of the least squares estimators; a teaching perspective.The standard linear regression m...
Algorithm for the exact solution of the problem of estimating the parameters of linear regression mo...
Multiple regression provides the capability of using non-linear functions to fit various curvilinear...
In the present thesis we deal with the linear regression models based on least squares. These method...
This paper is a survey on traditional linear regression techniques using the lñ-, l2-, and lâÂÂ-n...
Much of the data analysed by least squares regression methods violates the assumption that independe...
This thesis considers the regression analysis problem in which the estimators of the parameters are ...
Regression analysis is one of the most extensively used statistical tools applied across different f...
Least squares estimation, when used appropriately, is a powerful research tool. A deeper understandi...
The asymptotic properties of the least squares estimator are derived for a non regular nonlinear mod...
The present study investigates parameter estimation under the simple linear regression model for sit...
Regression models are the statistical methods that widely used in many fields. The models allow rela...
Classical least squares regression consists of minimizing the sum of the squared residuals. Many aut...
The percentage error, or error relative to the observed value is usually felt to be more meaningful ...
In the standard classical regression model the most commonly used procedures for estimation are base...
Limitations of the least squares estimators; a teaching perspective.The standard linear regression m...
Algorithm for the exact solution of the problem of estimating the parameters of linear regression mo...
Multiple regression provides the capability of using non-linear functions to fit various curvilinear...
In the present thesis we deal with the linear regression models based on least squares. These method...
This paper is a survey on traditional linear regression techniques using the lñ-, l2-, and lâÂÂ-n...
Much of the data analysed by least squares regression methods violates the assumption that independe...
This thesis considers the regression analysis problem in which the estimators of the parameters are ...
Regression analysis is one of the most extensively used statistical tools applied across different f...
Least squares estimation, when used appropriately, is a powerful research tool. A deeper understandi...
The asymptotic properties of the least squares estimator are derived for a non regular nonlinear mod...
The present study investigates parameter estimation under the simple linear regression model for sit...
Regression models are the statistical methods that widely used in many fields. The models allow rela...
Classical least squares regression consists of minimizing the sum of the squared residuals. Many aut...
The percentage error, or error relative to the observed value is usually felt to be more meaningful ...
In the standard classical regression model the most commonly used procedures for estimation are base...
Limitations of the least squares estimators; a teaching perspective.The standard linear regression m...
Algorithm for the exact solution of the problem of estimating the parameters of linear regression mo...
Multiple regression provides the capability of using non-linear functions to fit various curvilinear...