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...

We present a new class of methods for high-dimensional nonparametric regression and classification called sparse additive models (SpAM). Our methods combine ideas from sparse linear modeling and additive nonparametric regression. We derive an algorithm for fitting the models that is practical and effective even when the number of covariates is larger than the sample size. SpAM is essentially a ...

We introduce the functional generalized additive model (FGAM), a novel regression model for association studies between a scalar response and a functional predictor. We model the link-transformed mean response as the integral with respect to t of F{X(t), t} where F(·,·) is an unknown regression function and X(t) is a functional covariate. Rather than having an additive model in a finite number ...

This paper describes how to fit an additive Poisson model using standard software. It is illustrated with SAS code, but can be similarly used for other software packages.

The inherent intractability of probabilistic in­ ference has hindered the application of be­ lief networks to large domains. Noisy OR­ gates [30] and probabilistic similarity net­ works [18, 17) escape the complexity of infer­ ence by restricting model expressiveness. Re­ cent work in the application of belief-network models to time-series analysis and forecasting [9, 10) has given rise to the ...