Abstract
We present information criteria for statistical model evaluation problems for stochastic processes. The emphasis is put on the use of the asymptotic expansion of the distribution of an estimator based on the conditional Kullback–Leibler divergence for stochastic processes. Asymptotic properties of information criteria and their improvement are discussed. An application to a diffusion process is presented.
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Uchida, M., Yoshida, N. Information Criteria in Model Selection for Mixing Processes. Statistical Inference for Stochastic Processes 4, 73–98 (2001). https://doi.org/10.1023/A:1017535913009
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DOI: https://doi.org/10.1023/A:1017535913009