EM Algorithm for Estimating the Parameters of Weibull Competing Risk Model

Kayid, Mohamed

doi:10.1155/2021/1179856

Applied Bionics and Biomechanics

2021

DOI: 10.1155/2021/1179856

|View full text |Cite

EM Algorithm for Estimating the Parameters of Weibull Competing Risk Model

Mohamed Kayid

Abstract: One of the most commonly used models in survival analysis is the additive Weibull model and its generalizations. They are well suited for modeling bathtub-shaped hazard rates that are a natural form of the hazard rate. Although they have some advantages, the maximum likelihood and the least square estimators are biased and have poor performance when the data set contains a large number of parameters. As an alternative, the expectation-maximization (EM) algorithm was applied to estimate the parameters of the ad… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Relationship

Self Cite0

Independent3

Authors

Journals

Cited by 3 publications

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Parameter estimation of inverse Weibull distribution under competing risks based on the expectation–maximization algorithm

Alotaibi,

Rezk,

Park

2024

Quality & Reliability Eng

View full text Add to dashboard Cite

A system consisting of interconnected components in series is under consideration. This research focuses on estimating the parameters of this system for incomplete lifetime data within the framework of competing risks, employing an underlying inverse Weibull distribution. While one popular method for parameter estimation involves the Newton–Raphson (NR) technique, its sensitivity to initial value selection poses a significant drawback, often resulting in convergence failures. Therefore, this paper opts for the expectation–maximization (EM) algorithm. In competing risks scenarios, the precise cause of failure is frequently unidentified, and these issues can be further complicated by potential censoring. Thus, incompleteness may arise due to both censoring and masking. In this study, we present the EM‐type parameter estimation and demonstrate its superiority over parameter estimation based on the NR method. Two illustrative examples are provided. The proposed method is compared with the existing Weibull competing risks model, revealing the superiority of our approach. Through Monte Carlo simulations, we also examine the sensitivity of the initial value selection for both the NR‐type method and our proposed method.

show abstract

Parameter estimation of inverse Weibull distribution under competing risks based on the expectation–maximization algorithm

Alotaibi,

Rezk,

Park

2024

Quality & Reliability Eng

View full text Add to dashboard Cite

show abstract

Bound-Constrained Expectation Maximization for Weibull Competing-Risks Device Reliability

Chakraborty,

Boning,

Thompson

2024

IEEE Trans. Device Mater. Relib.

View full text Add to dashboard Cite

Parameter Estimation of Birnbaum-Saunders Distribution under Competing Risks Using the Quantile Variant of the Expectation-Maximization Algorithm

Park,

Wang

2024

Mathematics

View full text Add to dashboard Cite

Competing risks models, also known as weakest-link models, are utilized to analyze diverse strength distributions exhibiting multi-modality, often attributed to various types of defects within the material. The weakest-link theory posits that a material’s fracture is dictated by its most severe defect. However, multimodal problems can become intricate due to potential censoring, a common constraint stemming from time and cost limitations during experiments. Additionally, determining the mode of failure can be challenging due to factors like the absence of suitable diagnostic tools, costly autopsy procedures, and other obstacles, collectively referred to as the masking problem. In this paper, we investigate the distribution of strength for multimodal failures with censored data. We consider both full and partial maskings and present an EM-type parameter estimate for the Birnbaum-Saunders distribution under competing risks. We compare the results with those obtained from other distributions, such as lognormal, Weibull, and Wald (inverse-Gaussian) distributions. The effectiveness of the proposed method is demonstrated through two illustrative examples, as well as an analysis of the sensitivity of parameter estimates to variations in starting values.

show abstract

scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.

Contact Info

customersupport@researchsolutions.com

10624 S. Eastern Ave., Ste. A-614

Henderson, NV 89052, USA

This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.

Blog Terms and Conditions API Terms Privacy Policy Contact Cookie Preferences Do Not Sell or Share My Personal Information

Made with 💙 for researchers

Part of the Research Solutions Family.

EM Algorithm for Estimating the Parameters of Weibull Competing Risk Model

Cited by 3 publications

References 21 publications

Parameter estimation of inverse Weibull distribution under competing risks based on the expectation–maximization algorithm

Parameter estimation of inverse Weibull distribution under competing risks based on the expectation–maximization algorithm

Bound-Constrained Expectation Maximization for Weibull Competing-Risks Device Reliability

Parameter Estimation of Birnbaum-Saunders Distribution under Competing Risks Using the Quantile Variant of the Expectation-Maximization Algorithm

Contact Info

Product

Resources

About