Comment on “Under‐reported data analysis with INAR‐hidden Markov chains”

Markov Chain Monte Carlo Analysis of Underreported Count Data With an Application to Worker Absenteeism

A new approach for modeling under reported Poisson counts is developed The parameters of the model are estimated by Markov Chain Monte Carlo simulation An application to workers absenteeism data from the German Socio Economic Panel illustrates the fruitfulness of the approach Worker absenteeism and the level of pay are unrelated but absence rates increase with rm size I had helpful discussions ...

Importance Analysis with Markov Chains

In order to maximize system dependability improvements we need criteria for placement of component redundancy. One such criterion is based on quantitative measures provided by importance theory. Importance coefficients of components in mathematical models provide numerical ranks based on the contribution of the component to a system event occurrence (i.e., the one for which the model was constr...

On sampling with Markov chains

Card-Shuffling Analysis with Markov Chains

In this essay we shall discuss mathematical models of card-shuffling. The basic question to answer is ”how many times do you need to shuffle a deck of cards for it to become sufficiently randomized?”. This obviously depends on what we mean by shuffling and sufficiently randomized so we shall dwell quite a bit on these points too. In section 2 we will name and describe some popular shuffles. The...

Empirical Bayes Estimation in Nonstationary Markov chains

Estimation procedures for nonstationary Markov chains appear to be relatively sparse. This work introduces empirical  Bayes estimators  for the transition probability  matrix of a finite nonstationary  Markov chain. The data are assumed to be of  a panel study type in which each data set consists of a sequence of observations on N>=2 independent and identically dis...