Maximum likelihood parameter estimation of superimposed chirps using Monte Carlo importance sampling

2002 ◽  
Vol 50 (2) ◽  
pp. 224-230 ◽  
Author(s):  
S. Saha ◽  
S.M. Kay
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Importance Sampling ◽  
Monte Carlo Importance Sampling

scholarly journals Parameter Estimation for Composite Dynamical Systems Based on Sequential Order Statistics from Burr Type XII Mixture Distribution

Mathematics ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 810
Author(s):  
Tzong-Ru Tsai ◽  
Yuhlong Lio ◽  
Hua Xin ◽  
Hoang Pham
Keyword(s):  
Monte Carlo ◽  
Parameter Estimation ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Survival Function ◽  
Estimation Method ◽  
Mixture Distribution ◽  
Likelihood Estimation ◽  
Model Parameters ◽  
Burr Type Xii

Considering the impact of the heterogeneous conditions of the mixture baseline distribution on the parameter estimation of a composite dynamical system (CDS), we propose an approach to infer the model parameters and baseline survival function of CDS using the maximum likelihood estimation and Bayesian estimation methods. The power-trend hazard rate function and Burr type XII mixture distribution as the baseline distribution are used to characterize the changes of the residual lifetime distribution of surviving components. The Markov chain Monte Carlo approach via using a new Metropolis–Hastings within the Gibbs sampling algorithm is proposed to overcome the computation complexity when obtaining the Bayes estimates of model parameters. A numerical example is generated from the proposed CDS to analyze the proposed procedure. Monte Carlo simulations are conducted to investigate the performance of the proposed methods, and results show that the proposed Bayesian estimation method outperforms the maximum likelihood estimation method to obtain reliable estimates of the model parameters and baseline survival function in terms of the bias and mean square error.

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