abstract soil temperature is one of the key parameters affecting most hydrologic and agricultural processes. therefore, its measurement and prediction is very crucial. so far, the statistical regression methods have been used for estimation of soil temperature for specific location encountering with lack or shortage of data. in this work, soil temperature data are estimated at six different dep...

three artificial neural networks (ann) models; general regression neural network (grnn), redial basis function (rbf) and three layer multiple perceptron network were carried out to evaluate the prediction of the apparent metabolizable energy (ame) of wheat and corn from its chemical composition in broiler. input variables included: gross energy (ge), crude protein (cp), crude fiber (cf), ether ...

predictive quantitative structure–activity relationship was performed on the novel 4-oxo-1,4-dihydroquinoline and 4-oxo-4h-pyrido[1,2-a]pyrimidine derivatives to explore relationship between the structure of synthesized compounds and their anti-hiv-1 activities. in this way, the suitable set of the molecular descriptors was calculated and the important descriptors using the variable selections ...

In this paper we consider the problem of determining the number of structural changes in multiple linear regression models via group fused Lasso (least absolute shrinkage and selection operator). We show that with probability tending to one our method can correctly determine the unknown number of breaks and the estimated break dates are sufficiently close to the true break dates. We obtain esti...

Multiple sclerosis (MS) is a demyelinating disease of the central nervous system whose cause is still unknown. The disease course shows great interand intra-individual variability and this results in insecurity of diagnosis and prognosis. A well-founded knowledge of the natural history of MS, however, is an important prerequisite for developing adequate strategies for therapy and research. In o...

This paper proposes multiple regression models as a method for quantitative evaluation of the accuracy in steganalysis with respect to various moderating factors, such as parameter choice of the detector and properties of the carrier object. The case for multivariate statistical inference in steganalysis is particularly relevant: recent findings suggest that type and characteristics of carrier ...

The forecast of high energy electron fluxes in the radiation belts is important because the exposure of modern spacecraft to high energy particles can result in significant damage to onboard systems. A comprehensive physical model of processes related to electron energisation that can be used for such a forecast has not yet been developed. In the present paper a systems identification approach ...

Fully nonparametric methods for regression from functional data have poor accuracy from a statistical viewpoint, reflecting the fact that their convergence rates are slower than nonparametric rates for the estimation of high-dimensional functions. This difficulty has led to an emphasis on the so-called functional linear model, which is much more flexible than common linear models in finite dime...

This paper considers multiple regression procedures for analyzing the relationship between a response variable and a vector of d covariates in a nonparametric setting where both tuning parameters and the number of covariates need to be selected. We introduce an approach which handles the dilemma that with high dimensional data the sparsity of data in regions of the sample space makes estimation...