support vector regression (svr) solves regression problems based on the concept of support vector machine (svm). in this paper, a new model of svr with probabilistic constraints is proposed that any of output data and bias are considered the random variables with uniform probability functions. using the new proposed method, the optimal hyperplane regression can be obtained by solving a quadrati...

This paper shows how the probability, for a random confounding factor to reverse the estimate of ordinary regression, decreases exponentially with the sample size.

Estimation of genetic trends is necessary to monitor and evaluate selection programs. The objective of this study was to estimate the genetic trends for milk yield in Iranian Holsteins cows using random regression test day model. Data set was consisted of 743205 test-day records from 1991 to 2008, which were collected by the Animal Breeding Centre of Iran. Breeding, environmental and phenotypic...

A weighted linear regression model with impercise response and p-real explanatory variables is analyzed. The LR fuzzy random variable is introduced and a metric is suggested for coping with this kind of variables. A least square solution for estimating the parameters of the model is derived. The result are illustrated by the means of some case studies.

the aim of the present study was to estimate the genetic parameters and genetic trends of somatic cell score (scs) in the first 3 lactations of iranian holstein cows by random regression (rr) animal model by using restricted maximum likelihood (reml) method. the data set used in this analysis included observations of 340318 test-day records of 41526 cows in 288 herds; 89969 test-day of 11750 co...

in this study test-day records of milk (kg), fat (g), and protein (g) yields, somatic cell score (scs, cells/ml) collected by animal breeding center of iran during 2007 and 2009 were used to estimate genetic parameters using random regression model. models with different order of legendre polynomials were compared using bayesian information criterion (bic).for milk, fat yield and scs genetic an...

In this paper we address the problem of clustering sets of curve or trajectory data generated by groups of objects or individuals. The focus is to model curve data directly using a set of model-based curve clustering algorithms referred to as mixtures of regressions or regression mixtures. The proposed methodology is based on extension to regression mixtures that we call random effects regressi...