We propose approaching prediction from a framework grounded in maximizing correct prediction rates. While this is intuitively obvious, not nearly enough attention has been paid to the creation of a clear theoretical framework for prediction; motivated by the needs of current genome-wise association studies (GWAS) we provide a discussion on such a framework. We lay out an objective function for ...

Abstract Distribution of tree diameters in a stand is characterized using models that predict diameter moments and/or percentiles conjunction with mathematical system to recover the parameters an assumed statistical distribution. Studies have compared Weibull distribution recovery systems but arrived at different conclusions regarding best approach for recovering stand’s from predicted stand-le...

A new impulse noise detection technique from gray-scale and multichannel images is presented. It is based on block correlation of the noncausal linear prediction. The linear prediction coefficients are calculated using a block of the image at one instant. These coefficients are valid within that block only and are used to predict a pixel in a sliding window within the block. The sample is predi...

We address the problem of prediction in the classical spatial autoregressive LAG model for areal data. In contrast with the spatial econometrics literature, the geostatistical literature has devoted much attention to prediction using the Best Linear Unbiased Prediction approach. From the methodological point of view, we explore the limits of the extension of BLUP formulas in the context of the ...

When analyzing default predictions in real estate companies, the number of non-defaulted cases always greatly exceeds the defaulted ones, which creates the twoclass imbalance problem. This lowers the ability of prediction models to distinguish the default sample. In order to avoid this sample selection bias and to improve the prediction model, this paper applies a minority sample generation app...