We propose an extensive framework for additive regression models for correlated functional responses, allowing for multiple partially nested or crossed functional random effects with flexible correlation structures for, e.g., spatial, temporal, or longitudinal functional data. Additionally, our framework includes linear and nonlinear effects of functional and scalar covariates that may vary smo...

BACKGROUND Heart rate variability (HRV) has been widely used in the non-invasive evaluation of cardiovascular function. Recent studies have also attached great importance to the cardiac diastolic period variability (DPV) examination. Short-term variability measurement (e.g., 5 min) has drawn increasing attention in clinical practice, since it is able to provide almost immediate measurement resu...

nowadays, due to the competitive conditions of global market, corporations try to outsource their extraneous processes to third-party suppliers. so, selecting a proper supplier play a significant role in organization success. the supplier selection problem can be viewed as a group decision-making problem with multiple criteria. since in previous researches the inter relationship between criteri...

We present a new method for function estimation and variable selection, specifically designed for additive models fitted by cubic splines. Our method involves regularizing additive models using the l1–norm, which generalizes Tibshirani’s lasso to the nonparametric setting. As in the linear case, it shrinks coefficients, some of them reducing exactly to zero. It gives parsimonious models, select...

The paper presents some models involving a pair of actors that aim at bartering the goods from two privately owned pools of heterogeneous goods. In the models we discuss in the paper the barter can occur only once and can involve either a single good or a basket of goods from each actor/player. In the paper we examine both the basic symmetric model (one-to-one barter) as well as some other vers...

The generalized additive model is extended to handle negative binomial responses. The extension is complicated by the fact that the negative binomial distribution has two parameters and is not in the exponential family. The methodology is applied to data involving DNA adduct counts and smoking variables among ex-smokers with lung cancer. A more detailed investigation is made of the parametric r...

The aim of the present study was the estimation of (co) variance components and genetic parameters for body weight of Moghani sheep, using random regression models based on B-Splines functions. The data set included 9165 body weight records from 60 to 360 days of age from 2811 Moghani sheep, collected between 1994 to 2013 from Jafar-Abad Animal Research and Breeding Institute, Ardabil province,...

In commonly used functional regression models, the regression of a scalar or functional response on the functional predictor is assumed to be linear. This means the response is a linear function of the functional principal component scores of the predictor process. We relax the linearity assumption and propose to replace it by an additive structure. This leads to a more widely applicable and mu...

We consider the problem of sparse variable selection in nonparametric additive models, with the prior knowledge of the structure among the covariates to encourage those variables within a group to be selected jointly. Previous works either study the group sparsity in the parametric setting (e.g., group lasso), or address the problem in the nonparametric setting without exploiting the structural...

1.1 Parametric models: Linear Regression with non-linear basis functions Although the linear regression with linear basis is widely used in different areas, it is not powerful enough for lots of the real world cases as not all the models are linear in the real world. However, we can use non-linear basis functions to deal with non-linear relationships. It is just a linear combination of some fun...