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Penalized quasi likelihood estimation for variable selection

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arxiv 1910.12871 v1 pith:LL7YQUOE submitted 2019-10-28 math.ST stat.TH

Yoshiki Kinoshita , Nakahiro Yoshida This is my paper · ORCID
classification math.STstat.TH
keywords likelihoodquasiassociateddeviationinequalitylargepenalizedpolynomial
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Penalized methods are applied to quasi likelihood analysis for stochastic differential equation models. In this paper, we treat the quasi likelihood function and the associated statistical random field for which a polynomial type large deviation inequality holds. Then penalty terms do not disturb a polynomial type large deviation inequality. This property ensures the convergence of moments of the associated estimator which plays an important role to evaluate the upper bound of the probability that model selection is incorrect.

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Cited by 2 Pith papers

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