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B0974
Title: Penalized methods for stochastic processes Authors:  Nakahiro Yoshida - University of Tokyo (Japan) [presenting]
Abstract: Quasi likelihood analysis (QLA) is a general framework for statistics for stochastic processes. The QLA established asymptotic (mixed) normality and convergence of moments of the quasi maximum likelihood estimator and the quasi Bayesian estimator for various dependency models such as diffusion processes, even under discrete sampling, and point processes. Recently applications of the QLA have been extending to regularization methods for sparse estimation. The $L_q$ penalized quasi likelihood function methods and the least squares approximation methods are discussed as well as their applications to volatility estimation for diffusion processes and a regression analysis for point processes.