Random products and product auto-regression

Support vector regression with random output variable and probabilistic constraints

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...

Spatial Beta Regression Model with Random Effect

 Abstract: In many applications we have to encountered with bounded dependent variables. Beta regression model can be used to deal with these kinds of response variables. In this paper we aim to study spatially correlated responses in the unit interval. Initially we introduce spatial beta generalized linear mixed model in which the spatial correlation is captured through a random effect. T...

\random" Random Matrix Products

Regression and Random Confounding

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

Random walks, random configurations, and horocyclic products