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Georeferencing is a mandatory issue at many applications where the satellite images are used. Georeferencing is based on a transformation between images and objects coordinate systems, and parametric, semi parametric and non-parametric mathematical models are preferred for this issue. A semi-parametric model, i.e. sensor-based RFM (Rational Functional Model) suggested by the OGC (Open Geospatial Consortium) is subjected in this paper. Some theoretical background about the general equation, the estimation of coefficients, the distortions carried and their removing, the simplification of RFMs thanks to the special characteristics of coefficients and also the various adjustment models will be presented at first. Then, the georeferencing accuracies of a mono IKONOS panchromatic image covering Zonguldak test field having an undulating and mountainous topography will be presented using 22 GCPs via a computation package called GeoFigcon derived in Matlab environment by the author. If a bias compensation is applied, the accuracies are mx = ±0.4 pixel, my = ±0.5 pixel when 1st degree RFM is preferred. [ABSTRACT FROM AUTHOR]

The amount of global solar radiation and its temporal distribution are the primary variables for designing solar energy systems, such as flat-plate collectors, photovoltaic systems and other solar energy collecting devices. Knowledge of these parameters is essential to architects and engineers for energy-efficient building designs and solar energy applications. However, direct measuring is not available in many cases, so numerical technique becomes an effective alternative to estimate global radiation through observed meteorological data. In this study, some empirical models in the literature for determining the monthly average daily global solar radiation on a horizontal surface for Isparta were investigated. The models were compared on the basis of statistical error tests using the relative percentage error (e), coefficient of determination (R2), mean percentage error (MPE), mean absolute percentage error (MAPE), the sum of the square of relative error (SSRE), the relative standard error (RSE), mean bias error (MBE), root mean square error (RMSE) and t-statistic (t-stat) method. According to the results, Model 18c showed the best estimation of the global solar radiation on a horizontal surface of Isparta by means of the e (1.77%), R2(95.86%), MPE (0.127%), MAPE (8.95%), SSRE (5.22%), RSE (0.007%), MBE (0.092%), RMSE (1.39%) and t-stat (0.812%). The most suitable model for estimating global solar radiation coming on horizontal surface of Isparta is obtained with Model 18c. [ABSTRACT FROM AUTHOR]

In regression analysis, if there are some kind of relation (multicollinearity) between independent variables, the Least Squares estimation method may lead to the use of wrong models and hence to wrong findings out of the model. Various methods have been devised in order to carry out regression analysis with such independent variables which exhibit dependence on each other. One of such methods is the biased estimation methods. In this study, it has been studied on the necessary and sufficient conditions of biased estimators based on Ridge estimator to be at least efficient as much as the Least Squares estimator. [ABSTRACT FROM AUTHOR]

In this paper, different regression models have been discussed for investigation of the relationships among house rents and trait variables in Eskişehir. According to statistical analysis, it is concluded that some of the explanatory variables have had linear and some of them have had nonparametric effect on house rents. Thus, the model has obtained as semiparametric adittive regression model that contain a few nonparametric components in additon to parametric linear components. It is observed that suitable semiparametric adittive regression model is significant, and given better results than parametric linear and semiparmetric models. [ABSTRACT FROM AUTHOR]