Propagation Models and Fitting Them for the Boolean Random Sets

Authors

  • Khalil Shafie Associate Professor, University of Northern Colorado, Colorado, USA.
  • mojtaba Khazaei Assistant Professor, Department of Statistics, Shahid Beheshti University G.C., Tehran, IRAN.
Abstract:

In order to study the relationship between random Boolean sets and some explanatory variables, this paper introduces a Propagation model. This model can be applied when corresponding Poisson process of the Boolean model is related to explanatory variables and the random grains are not affected by these variables. An approximation for the likelihood is used to find pseudo-maximum likelihood estimates of propagation model parameters when the grains are nonrandom circle with unknown radii.

Upgrade to premium to download articles

Sign up to access the full text

Already have an account?login

similar resources

propagation models and fitting them for the boolean random sets

in order to study the relationship between random boolean sets and some explanatory variables, this paper introduces a propagation model. this model can be applied when corresponding poisson process of the boolean model is related to explanatory variables and the random grains are not affected by these variables. an approximation for the likelihood is used to find pseudo-maximum likelihood esti...

full text

fitting growth regression model to the boolean random sets

one of the models that can be used to study the relationship between boolean random sets and explanatory variables is growth regression model which is defined by generalization of boolean model and permitting its grains distribution to be dependent on the values of explanatory variables. this model can be used in the study of behavior of boolean random sets when their coverage regions variation...

full text

Fitting Propagation Models with Random Grains, Method and Some Simulation Studies

In this paper the regression problem for random sets of the Boolean model type is developed, where the corresponding poisson process of the model is related to some explanatory variables and the random grains are not affected by these variables. A model we call propagation model, is presented and some methods for fitting this model are introduced. Propagation model is applied in a simulation st...

full text

Fitting semiparametric random effects models to large data sets.

For large data sets, it can be difficult or impossible to fit models with random effects using standard algorithms due to memory limitations or high computational burdens. In addition, it would be advantageous to use the abundant information to relax assumptions, such as normality of random effects. Motivated by data from an epidemiologic study of childhood growth, we propose a 2-stage method f...

full text

Perturbation propagation in random and evolved Boolean networks

In this paper, we investigate the propagation of perturbations in Boolean networks by evaluating the Derrida plot and its modifications. We show that even small random Boolean networks agree well with the predictions of the annealed approximation, but nonrandom networks show a very different behaviour. We focus on networks that were evolved for high dynamical robustness. The most important conc...

full text

Robustness and Information Propagation in Attractors of Random Boolean Networks

Attractors represent the long-term behaviors of Random Boolean Networks. We study how the amount of information propagated between the nodes when on an attractor, as quantified by the average pairwise mutual information (I(A)), relates to the robustness of the attractor to perturbations (R(A)). We find that the dynamical regime of the network affects the relationship between I(A) and R(A). In t...

full text

My Resources

Save resource for easier access later

Save to my library Already added to my library

{@ msg_add @}


Journal title

volume Volume 2  issue Issue 3

pages  45- 48

publication date 2010-02-07

By following a journal you will be notified via email when a new issue of this journal is published.

Hosted on Doprax cloud platform doprax.com

copyright © 2015-2023