In this paper, we consider semiparametric varying coefficient partially linear models when the predictor variables of the linear part are ultra-high dimensional where the dimensionality grows exponentially with the sample size. We propose a profile forward regression (PFR) method to perform variable screening for ultra-high dimensional linear predictor variables. The proposed PFR algorithm can ...

normal 0 false false false en-us x-none ar-sa aim: ct number (hu) and linear attenuation coefficient contain useful information which  can be determined during usual ct scanning for the prediction of breast cancerous cells existence based on hemoglobin concentration. methods: this study used breast phantom containing major and minor vessels with diameters of 10 and 5 mm, respectively. the major...

Quantitative structure-activity relationships are mathematical models constructed based on the hypothesis that structure of chemical compounds is related to their biological activity. A linear regression model is often used to estimate and predict the nature of the relationships between a measured activity and some measure or calculated descriptors. Linear regression helps to answer main three ...

Random coefficient regression models have received considerable attention, especially from econometricians. Previous work has assumed that the coefficients have normal distributions. The variances of the coefficients have, in previous papers, been estimated by maximum likelihood or by least squares methodology applied to the squared residuals from a preliminary (unweighted) fit. Maximum likelih...

Fuzzy linear regression analysis with symmetric triangular fuzzy number coefficient has been introduced by Tanaka et al. In this work we propose to approximate the fuzzy nonlinear regression using Artificial Neural Networks. The working of the proposed method is illustrated by the case study with the data for temperature and evaporation for the IARI New Delhi division. MSC: 62J86

Although the literature on varying coefficient models (VCMs) is vast, we believe that there remains room to make these models more widely accessible and provide a unified and practical implementation for a variety of complex data settings. The adaptive nature and strength of P-spline VCMs allow a full range of models: from simple to additive structures, from standard to generalized linear model...