Conventional approaches to modeling classification image data can be described in terms of a standard linear model (LM). We show how the problem can be characterized as a Generalized Linear Model (GLM) with a Bernoulli distribution. We demonstrate via simulation that this approach is more accurate in estimating the underlying template in the absence of internal noise. With increasing internal n...

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,...

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...

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...