Add to ORKGOrdinary Least Squares (OLS) is a method for analyzing and estimating\ud the relationship among variables. This method is done by finding a set of estimates that minimizes the sum of errors squared, or residuals.\ud Under specific assumptions, the method of OLS is expected to provide unbiased estimation with minimum variance. One of the assumptions is that the error terms are normally distributed. This paper will primarily concern this assumption on the normal distribution of the error terms. By simulating data sets that contain error terms with non-normal distribution,\ud it will explore the relationship between the distribution of error\ud terms and the distribution of resulting estimators
Many statistical tests are based around an assumption of “normality”. The reasoning for this choice...
Abstract. We analyze the asymptotic properties of estimators based on optimizing an extended least s...
In many situations, the distribution of the error terms of a linear regression model departs signifi...
The derivation of the asymptotic normality LSE's under univariate non-linear regression models is pr...
AbstractIn a linear model Y = Xβ + Z a linear functional β → γ′β is to be estimated under squared er...
In the classical normal linear regression model, ordinary least squares estimators (OLS) will be con...
This paper examines the sensitivity of the distributions of OLS and 2SLS estimators to the assumptio...
In a linear model $Y=X\beta +Z$ a linear functional $\beta \mapsto \gamma '\beta$ is to be estimated...
When estimating the parameters in a linear regression model, the method of least squares (L^-norm es...
. We consider the estimation of error distributions in least squares identification of distributed p...
When estimating the parameters in a linear regression model, the method of least squares (L^-norm es...
This note formalizes bias and inconsistency results for ordinary least squares (OLS) on the linear p...
It is proved that if $y=\theta +Z$, where the unknown parameter $\theta$ is an element of a linear s...
Probability density functions were derived for errors in the evaluation of unknowns by the least squ...
In general, the theory developed in the area of linear regression analysis assumes that the error ∊ ...
Many statistical tests are based around an assumption of “normality”. The reasoning for this choice...
Abstract. We analyze the asymptotic properties of estimators based on optimizing an extended least s...
In many situations, the distribution of the error terms of a linear regression model departs signifi...
The derivation of the asymptotic normality LSE's under univariate non-linear regression models is pr...
AbstractIn a linear model Y = Xβ + Z a linear functional β → γ′β is to be estimated under squared er...
In the classical normal linear regression model, ordinary least squares estimators (OLS) will be con...
This paper examines the sensitivity of the distributions of OLS and 2SLS estimators to the assumptio...
In a linear model $Y=X\beta +Z$ a linear functional $\beta \mapsto \gamma '\beta$ is to be estimated...
When estimating the parameters in a linear regression model, the method of least squares (L^-norm es...
. We consider the estimation of error distributions in least squares identification of distributed p...
When estimating the parameters in a linear regression model, the method of least squares (L^-norm es...
This note formalizes bias and inconsistency results for ordinary least squares (OLS) on the linear p...
It is proved that if $y=\theta +Z$, where the unknown parameter $\theta$ is an element of a linear s...
Probability density functions were derived for errors in the evaluation of unknowns by the least squ...
In general, the theory developed in the area of linear regression analysis assumes that the error ∊ ...
Many statistical tests are based around an assumption of “normality”. The reasoning for this choice...
Abstract. We analyze the asymptotic properties of estimators based on optimizing an extended least s...
In many situations, the distribution of the error terms of a linear regression model departs signifi...