A higher order Markov model for analyzing covariate dependence

Higher Order Markov Networks for Model Estimation

The problem we address in this paper is to label datapoints when the information about them is provided primarily in terms of their subsets or groups. The knowledge we have for a group is a numerical weight or likelihood value for each group member to belong to same class. These likelihood values are computed given a class specific model, either explicit or implicit, of the pattern we wish to l...

A Simplified Higher-Order Markov Chain Model

In this paper, we present a simplified higher-order Markov chain model for multiple categorical data sequences also called as simplified higher-order multivariate Markov chain model. The number of the parameters of the new model is only ) ) (( 2 sm s n O + which is less than ) (( 2 m ns O in the higher-order multivariate Markov chain model. Numerical experiments illustrate the benefits of our n...

Covariate order tests for covariate effect.

A new approach for constructing tests for association between a random right censored life time variable and a covariate is proposed. The basic idea is to first arrange the observations in increasing order of the covariate and then base the test on a certain point process defined by the observation times. Tests constructed by this approach are robust against outliers in the covariate values or ...

A Stochastic Model for Indirect Condition Monitoring Using Proportional Covariate Model

This paper introduces a model to make decision on the maintenance of a mechanical component subject to condition monitoring. A stochastic model is used to determine what maintenance action should be taken at a monitoring check and the follow up inspection times. The condition of component has a stochastic relation with measurements. A new state space model is developed and used, to predict the ...

A general model for higher order neurons