Related Experiment Video
Updated: Aug 10, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
Published on: December 9, 2015
A computer program to estimate the parameters of covariate dependent higher order Markov model
Rafiqul Islam Chowdhury1, M Ataharul Islam, Makhdoom Ali Shah
1Department of Health Information Administration, Kuwait University, P.O. Box 31470, Sulaibekhat 90805, Kuwait. rafiq@hsc.edu.kw
This study introduces a new S-plus program for analyzing higher-order Markov chains with multiple covariates. It estimates parameters and performs statistical tests for longitudinal data, aiding in understanding complex health trends.
Area of Science:
- Biostatistics
- Epidemiology
- Computer Science
Background:
- Longitudinal data analysis is crucial for understanding health trends over time.
- Higher-order Markov chains offer a flexible framework for modeling sequential data.
- Covariate-dependent models are essential for identifying factors influencing health outcomes.
Purpose of the Study:
- To present a novel computer program for estimating parameters of covariate-dependent higher-order Markov chains.
- To provide tools for related statistical tests, including likelihood ratio and model chi-square tests.
- To demonstrate the program's utility with a real-world dataset on maternal morbidity.
Main Methods:
- Development of an S-plus program to estimate parameters for higher-order Markov chains.
- Implementation of maximum likelihood estimation for model parameters.
- Inclusion of statistical tests for model assessment and covariate effects.
Main Results:
- The program successfully estimates parameters and provides standard errors, t-values, and significance levels.
- Likelihood ratio and model chi-square test results are generated.
- The application to maternal morbidity data demonstrates the program's capability in analyzing longitudinal health outcomes.
Conclusions:
- The developed S-plus program is a valuable tool for analyzing complex longitudinal data using higher-order Markov chains.
- The program facilitates the identification of significant covariates influencing health outcomes.
- This methodology can be applied to various fields requiring the analysis of sequential, covariate-dependent data.
More Related Videos
04:35Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
08:51Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
Published on: September 20, 2024
Related Concept Videos
Distributions to Estimate Population Parameter
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation
On...
Mechanistic Models: Compartment Models in Individual and Population Analysis
Friedman Two-way Analysis of Variance by Ranks
Statistical Methods for Analyzing Epidemiological Data
Parametric Survival Analysis: Weibull and Exponential Methods
Weibull Distribution
The Weibull distribution is a flexible model used in parametric survival analysis. It can handle both increasing and decreasing hazard rates, depending on its shape parameter...