Maximum likelihood estimation of semiparametric mixture component models for competing risks data

Biometrics ◽  
2014 ◽  
Vol 70 (3) ◽  
pp. 588-598 ◽  
Author(s):  
Sangbum Choi ◽  
Xuelin Huang
Keyword(s):  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Competing Risks ◽  
Likelihood Estimation ◽  
Mixture Component ◽  
Competing Risks Data ◽  
Semiparametric Mixture ◽  
Component Models

scholarly journals Maximum likelihood estimation based on type-i hybrid progressive censored competing risks data

2016 ◽  
Vol 4 (1) ◽  
pp. 20
Author(s):  
Samir Ashour ◽  
Wael Abu El Azm
Keyword(s):  
Maximum Likelihood ◽  
Competing Risks ◽  
Information Matrix ◽  
Likelihood Estimation ◽  
Real Data ◽  
Maximum Likelihood Estimates ◽  
Type I ◽  
Data Set ◽  
Special Cases ◽  
Competing Risks Data

<p>This paper is concerned with the estimators problems of the generalized Weibull distribution based on Type-I hybrid progressive censoring scheme (Type-I PHCS) in the presence of competing risks when the cause of failure of each item is known. Maximum likelihood estimates and the corresponding Fisher information matrix are obtained. We generalized Kundu and Joarder [7] results in the case of the exponential distribution while, the corresponding results in the case of the generalized exponential and Weibull distributions may be obtained as a special cases. A real data set is used to illustrate the theoretical results.</p>

Statistical Analysis of Thermal Shock Data

1984 ◽  
Vol 106 (4) ◽  
pp. 325-330
Author(s):  
C. W. Merten ◽  
W. R. Steinhurst
Keyword(s):  
Statistical Analysis ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Thermal Shock ◽  
Competing Risks ◽  
Likelihood Estimation ◽  
Single Cycle ◽  
Weibull Parameters ◽  
The Third ◽  
Failure Models

Hazard plotting and maximum likelihood estimation (MLE) are used to determine Weibull parameters for single cycle thermal shock tests performed on WC-Co specimens. Data for each sample fall into three classes: tests below a threshold prequench temperature for which failure never occurs; tests above some higher prequench temperature for which failure is universal; and tests at intermediate prequench temperatures for which both failures and nonfailures are interspersed. Hazard plots in the region of the third class show a good Weibull fit. For some samples, the shape of the hazard plots indicates competing risks. MLE of the Weibull parameters for each sample is performed and the estimates are compared. Failure models which might account for the shape of the hazard plots are proposed.

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