We obtain an optimal estimator for a domain (small area) total using a linear least-squares prediction approach under a design-based model. The optimal estimator is the same as that obtained under a super-population model approach, and reproduces the classical form of the synthetic estimator in an extreme case. Using the concept of M-optimality, we generalize a well known theorem (Royall, 1976)...

In the frame of the ISPRS-ISRO Cartosat-1 Scientific Assessment Programme (C-SAP) the orientation of 3 stereo scenes has been computed by bias corrected RPC-solution. The generation of digital elevation models (DEMs) followed by least squares matching. An analysis of the DEMs against reference DEMs showed sub-pixel accuracy of the height values as x-parallax.

Ordinary least square regression is one of the most widely used statistical methods. However, it is a parametric model and relies on assumptions that are often not met. Alternative methods of regression for continuous dependent variables relax these assumptions in various ways. This paper will explore PROCS such as QUANTREG, ADAPTIVEREG and TRANSREG for these data.

We investigate in this paper a variety of equalities for the ordinary least-squares estimators and the best linear unbiased estimators under the general linear (Gauss-Markov) model {y, Xβ, σΣ} and the restrained model {y, Xβ |Aβ = b, σΣ}.

Pedomodels have become a popular topic in soil science and environmentalresearch. They are predictive functions of certain soil properties based on other easily orcheaply measured properties. The common method for fitting pedomodels is to use classicalregression analysis, based on the assumptions of data crispness and deterministic relationsamong variables. In modeling natural systems such as s...

We examined an econometric model of counts of worker absences due to illness. The underlying theoretical model is of a sluggishly adjusting hedonic labor market. We compared results from three parametric estimators, nonlinear least squares plus Poissonand negative binomial pseudo maximum likelihood, to generalized least squares using nonparametric estimates of the conditional variance. Our data...

The Stata package krls as well as the R package KRLS implement kernel-based regularized least squares (KRLS), a machine learning method described in Hainmueller and Hazlett (2014) that allows users to tackle regression and classification problems without strong functional form assumptions or a specification search. The flexible KRLS estimator learns the functional form from the data, thereby pr...

The uniformly minimum variance unbiased (UMVU), maximum likelihood, percentile (PC), least squares (LS) and weighted least squares (WLS) estimators of the probability density function (pdf) and cumulative distribution function are derived for the generalized Rayleigh distribution. This model can be used quite effectively in modelling strength data and also modeling general lifetime data. It has...

Citations are increasingly used for research evaluations. It is therefore important to identify factors affecting citation scores that are unrelated to scholarly quality or usefulness so that these can be taken into account. Regression is the most powerful statistical technique to identify these factors and hence it is important to identify the best regression strategy for citation data. Citati...

In this paper, we extend Bai and Perron’s (1998, Econometrica, p.47-78) framework for multiple break testing to linear models estimated via Two Stage Least Squares (2SLS). Within our framework, the break points are estimated simultaneously with the regression parameters via minimization of the residual sum of squares on the second step of the 2SLS estimation. We establish the consistency of the...