Strongly consistent model selection for general causal time series

@article{Kengne2020StronglyCM,
  title={Strongly consistent model selection for general causal time series},
  author={William Kengne},
  journal={arXiv: Statistics Theory},
  year={2020}
}
  • William Kengne
  • Published 20 August 2020
  • Mathematics, Computer Science
  • arXiv: Statistics Theory
Efficient and Consistent Data-Driven Model Selection for Time Series
TLDR
This paper proves that consistent model selection criteria outperform classical AIC criterion in terms of efficiency and derives from a Bayesian approach the usual BIC criterion, by keeping all the second order terms of the Laplace approximation, a data-driven criterion denoted KC’.
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>2LK(j)E(n -j), Y,0?K(j)zi = k(z) = gh, where the K(j) decrease to zero at a geometric rate, and that the E(n) are the linear innovations. It has been assumed that &{x(n)} = 0 but this is immaterial
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