soil salinity is a limiting factor for plant growth and a serious cause of land degradation. field sampling and statistical analysis for estimating soil salinity is expensive and time consuming. estimating soil salinity by spatial statistical models and geographic information system (gis) is recommended, because it saves labor and time. this study was conducted to evaluate the performance of sp...

A robust version of the Ordinary Least Squares accommodating the idea of weighting the order statistics of the squared residuals (rather than directly the squares of residuals) is recalled and its properties are studied. The existence of solution of the corresponding extremal problem and the consistency under heteroscedasticity is proved. 1. BASIC FRAMEWORK AND WEIGHTING THE ORDER STATISTICS Le...

We show that previous results on the asymptotic e ciency of OLS versus GLS in the context of trending data carry over to regressors of the fractionally integrated type

This work gives a simultaneous analysis of both the ordinary least squares estimator and the ridge regression estimator in the random design setting under mild assumptions on the covariate/response distributions. In particular, the analysis provides sharp results on the “out-of-sample” prediction error, as opposed to the “in-sample” (fixed design) error. The analysis also reveals the effect of ...

The random design setting for linear regression concerns estimators based on a random sample of covariate/response pairs. This work gives explicit bounds on the prediction error for the ordinary least squares estimator and the ridge regression estimator under mild assumptions on the covariate/response distributions. In particular, this work provides sharp results on the “out-of-sample” predicti...