Necessary and su cient conditions for the equality of ordinary least squares and generalized least squares estimators in the linear regression model with rst order spatial error processes are given

This article introduces the use of regression models based on the Poisson distribution as a tool for resolving common problems in analyzing aggregate crime rates. When the population size of an aggregate unit is small relative to the offense rate, crime rates must be computed from a small number of offenses. Such data are ill-suited to least-squares analysis. Poisson-based regression models of ...

Letting ) (i S be the i-th largest city in a country, it is often observed that i S i log log 1 0 ) ( α α + ≈ for some 0 0 > α and 0 1 < α . It is called rank size rule when 1 1 − = α . This relationship has been examined by means of ordinary least squares estimation and t test in the literature. However, since ) (i S is heteroskedastic and autocorrelated, t statistics do not have standard dist...

Alternative Estimation Procedures to OLS We have seen that OLS does a nice job in model estimation when the CLRM assumptions are met. We have also seen that violations of the CLRM lead to inefficiency and estimators that are not BLUE. We have also seen that the model y=Xβ+ε can be transformed to produce estimators that are BLUE. We now consider additional estimation techniques to least squares....

Abstract. Regression analysis, especially the ordinary least squares method which assumes that errors are confined to the dependent variable, has seen a fair share of its applications in aerosol science. The ordinary least squares approach, however, could be problematic due to the fact that atmospheric data often does not lend itself to calling one variable independent and the other dependent. ...

The problem of surface generating on the basis of experimental data is presented in this lecture. Special attention is given to the implementation of moving ordinary least squares and moving total least squares. Some results done in the Institute for Applied Mathematics in Osijek are mentioned which were published in the last several years.

Classical least squares regression consists of minimizing the sum of the squared residuals. Many authors have produced more robust versions of this estimator by replacing the square by something else, such as the absolute value. In this article a different approach is introduced in which the sum is replaced by the median of the squared residuals. The resulting estimator can resist the effect of...