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Updated: Aug 6, 2025
04:35
Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
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Estimating the parameters of a dependent model and applying it to environmental data set.
V Mohtashami-Borzadaran1, M Amini1, J Ahmadi1
1Department of Statistics, Ferdowsi University of Mashhad, Mashhad, Iran.
Journal of Applied Statistics
|March 17, 2023
Summary
A new dependent model for series-parallel systems is introduced, offering insights into component dependence. This research aids in analyzing bivariate data, particularly for extreme events, using advanced estimation techniques.
Area of Science:
- Reliability Engineering
- Statistical Modeling
Background:
- Series-parallel systems are crucial in reliability analysis.
- Understanding component dependence is vital for accurate system assessment.
- Existing models may not fully capture complex dependence structures.
Purpose of the Study:
- Introduce a novel dependent model for series-parallel systems.
- Investigate the dependence properties of the proposed model.
- Provide methods for parameter estimation and model validation.
Main Methods:
- Developed a new dependent model based on series-parallel system structures.
- Applied moment and maximum likelihood estimation methods.
- Utilized Monte Carlo simulations for performance evaluation.
Main Results:
- The proposed model effectively captures dependence in complex systems.
- Estimation methods provide reliable parameter estimates.
- Simulation results validate the performance of the estimators.
Conclusions:
- The new dependent model is a valuable tool for reliability analysis.
- The study offers practical methods for analyzing bivariate data, especially for extreme events.
- Findings benefit researchers and practitioners in reliability and statistics.
Keywords:
Maximum likelihood methodPrimary 60E05Secondary 62H20copuladistortion functionmeasures of non-exchangeabilitymethod of momentsMore Related Videos
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