Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1154198 | Statistics & Probability Letters | 2016 | 7 Pages |
Abstract
This paper is concerned with inference for renewal processes on the real line that are observed in a broken interval. For such processes, the classic history-based approach cannot be used. Instead, we adapt tools from sequential spatial point process theory to propose a Monte Carlo maximum likelihood estimator that takes into account the missing data. Its efficacy is assessed by means of a simulation study and the missing data reconstruction is illustrated on real data.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
M.N.M. van Lieshout,