Mojdeh Esmailzadeh, Farzad Eskandari, Sima Naghizadeh Ardabili,
Volume 5, Issue 2 (2-2012)
Abstract
Forecasting the future status for underlying systems or random process, is one of the most important problems. In such situations, in addition to variables, the parameters may vary during the time and hence, the independence assumption between variables and parameters is broken. For analyzing this systems, usually the dynamic generalized linear models are used based on Markov chain Monte Carlo algorithm. The purpose of this paper is applying the Bayesian dynamic generalized linear models in non-conjugate discrete structures. First, the concepts of dynamic generalized linear models are reviewed. Then, the Bayesian modeling of non-conjugated discrete structures using MCMC algorithm is studied. Finally, using the investigated model the real data set related to the economic activity condition in three provinces of Iran during the years 2006-2008 are analysed.
Mahboobeh Doosti Irani, Saeid Pooladsaz,
Volume 5, Issue 2 (2-2012)
Abstract
Consider an experimental situation where it is desired to compare more than one test treatments with a control treatment. In this paper a method is presented for achieving E-optimal incomplete block design for this situation under the assumption that the observations within each block are correlated. Then an algorithm is provided for making optimal design based on above-mentioned method. This algorithm for any correlation structure with negative non-diagonal elements is applicable.
