Sparse estimation for generalized exponential marked Hawkes process
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We have established a sparse estimation method for the generalized exponential marked Hawkes process by the penalized method to the ordinary method (P-O) estimator. Furthermore, we evaluated the probability of correct variable selection. In order to achieve this, we established a framework for a likelihood analysis and the P-O estimation when there might be nuisance parameters and the true value of the parameter could be realized at the boundary of the parameter space. Numerical simulations are given for several important examples.
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