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In order to estimate linear extension of the Yule process (LEYP2s) parameters from actual failure and decommissioning data, the authors have to build the likelihood function of the parameters. The log‐likelihood computation formula looks to be somewhat complicated, and this raises the question of whether an optimal value of the parameter θ can be easily found or not, by using the Nelder‐Mead optimization procedure. In order to perform estimation procedure for LEYP2s, the authors carry out the three steps on a theoretical example. The steps include generating a pseudo‐random failure and decommissioning dataset, according to a given LEYP2s parameter, checking parameter estimates with respect to theoretical values, and checking convexity of log‐likelihood function in a reasonably broad neighborhood around the estimates. The random simulation has been used to graphically assess the shape of the log‐likelihood function.
In order to estimate linear extension of the Yule process (LEYP2s) parameters from actual failure and decommissioning data, the authors have to build the likelihood function of the parameters. The log‐likelihood computation formula looks to be somewhat complicated, and this raises the question of whether an optimal value of the parameter θ can be easily found or not, by using the Nelder‐Mead optimization procedure. In order to perform estimation procedure for LEYP2s, the authors carry out the three steps on a theoretical example. The steps include generating a pseudo‐random failure and decommissioning dataset, according to a given LEYP2s parameter, checking parameter estimates with respect to theoretical values, and checking convexity of log‐likelihood function in a reasonably broad neighborhood around the estimates. The random simulation has been used to graphically assess the shape of the log‐likelihood function.
LEYP2s Likelihood and Inference
Le Gat, Yves (author)
2015-12-30
13 pages
Article/Chapter (Book)
Electronic Resource
English
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