Spatial adaption for predicting random functions

The power of adaption for approximating functions with singularities

Consider approximating functions based on a finite number of their samples. We show that adaptive algorithms are much more powerful than nonadaptive ones when dealing with piecewise smooth functions. More specifically, let F 1 r be the class of scalar functions f : [0, T ] → R whose derivatives of order up to r are continuous at any point except for one unknown singular point. We provide an ada...

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 regression models for estimation of covariance functions of growth in Iranian Kurdi sheep

Body weight (BW) records (n=11,659) of 4961 Kurdi sheep from 215 sires and 2085 dams were used to estimate the additive genetic, direct and maternal permanent environmental effects on growth from 1 to 300 days of age. The data were collected from 1993 to 2015 at a breeding station in North Khorasan province; Iran. Genetic parameters for growth traits were estimated using random regression test-...

Stochastic Modelling Probability Theory of Random Functions Random Variables Random Functions Spectral Representation of Random Functions Isotropic Incompressible Random Functions Transformations of Random Functions Derivation of Stochastic Models Quasi Homogeneous Random Functions Filtering of Random Functions

Correlation Functions for Random Involutions

Our interest is in the scaled joint distribution associated with k-increasing subsequences for random involutions with a prescribed number of fixed points. We proceed by specifying in terms of correlation functions the same distribution for a Poissonized model in which both the number of symbols in the involution, and the number of fixed points, are random variables. From this, a de-Poissonizat...