An Extension of Yoshida, Imoto, Higuchi and Miyano Result

Onoghojobi B

doi:10.3844/jmssp.2009.283.286

Abstract

Problem statement: Different approaches to deal with dynamic model when it is studentized are presented. Approach: In this respect, the recursive formula for calculating state space in the canonical form. Results: The asymptotic distribution of their test under the linear system for the gene network and a studentized-dynamic linear model with Markov denoising switching for estimating time-dependent gene network structure were presented. A resultant studentized version for gene network state space model was obtained, as an improvement on the original model. Conclusion/Recommendations: The dynamic model with Markov switching for estimating time-dependent gene regulatory networks handled the problem of modeling change in an evolving time series. The studentized version incorporated the modeling change and test for heteroscedasticity.

Highlights

Xt = FtXt−1 + ηt (1)In this study we develop a new result for application of importance studentized method to state space with Markov switching for estimating gene regulatory network from time series microarray experiments
A draw back of dynamic linear model with Markov switching for estimating time seriesdependent gene network structure s or (7) is that, it is crucially dependent on the assumption that εt is normally distributed
Imoto and Higuchi[15] proposed a simple method of estimating time-dependent gene network from time series microarray data by dynamic linear models with Markov switching and the state space version was later explored by Yamaguchi and Higuchi [13] in which the state space model was represented by linear system for the gene network

Summary

Introduction

In this study we develop a new result for application of importance studentized method to state space with Markov switching for estimating gene regulatory network from time series microarray experiments. A draw back of dynamic linear model with Markov switching for estimating time seriesdependent gene network structure s ∧. Imoto and Higuchi[15] proposed a simple method of estimating time-dependent gene network from time series microarray data by dynamic linear models with Markov switching and the state space version was later explored by Yamaguchi and Higuchi [13] in which the state space model was represented by linear system for the gene network. A state space model consists of a state equation and an observation equation. A standard linear state space model can be written as: Yt = TtXt + εt (2)

Methods

Results

Conclusion