Estimating the parameters of a dependent model and applying it to environmental data set

• Published in: Journal of applied statistics Vol. 50; no. 4; pp. 984 - 1016
• Main Authors: Mohtashami-Borzadaran, V., Amini, M., Ahmadi, J.
• Format: Journal Article
• Language: English
• Published: England Taylor & Francis 12.03.2023; Taylor & Francis Ltd
• Subjects: Bivariate analysis; copula; Datasets; distortion function; Estimation; Mathematical models; Maximum likelihood method; measures of non-exchangeability; method of moments; Parameters; Primary 60E05; Random numbers; Secondary 62H20; Statistical methods; Primary 60E05; Secondary 62H20; copula; distortion function; method of moments; Maximum likelihood method; measures of non-exchangeability
• ISSN: 0266-4763; 1360-0532
• DOI: 10.1080/02664763.2021.2006613

In this paper, a new dependent model is introduced. The model is motivated using the structure of series-parallel systems consisting of two series-parallel systems with a random number of parallel sub-systems that have fixed components connected in series. The dependence properties of the proposed model are studied. Two estimation methods, namely the moment method, and the maximum likelihood method are applied to estimate the parameters of the distributions of the components based on observing the system's lifetime data. A Monte Carlo simulation study is used to evaluate the performance of the estimators. Two real data sets are used to illustrate the proposed method. The results are useful for researchers and practitioners interested in analyzing bivariate data related to extreme events.