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Seminar on Probability and Statistics
Seminar information archive ～03/19｜Next seminar｜Future seminars 03/20～
| Organizer(s) | Nakahiro Yoshida, Hiroki Masuda, Teppei Ogihara, Yuta Koike |
|---|
2013/02/07
11:00-12:10 Room #006 (Graduate School of Math. Sci. Bldg.)
Stefano M. Iacus (Dipartimento di Economia, Managemente Metodi Quantitativi Universita' di Milano)
On L^p model selection for discretely observed diffusion processes (JAPANESE)
http://www.sigmath.es.osaka-u.ac.jp/~kamatani/statseminar/2012/14.html
Stefano M. Iacus (Dipartimento di Economia, Managemente Metodi Quantitativi Universita' di Milano)
On L^p model selection for discretely observed diffusion processes (JAPANESE)
[ Abstract ]
The LASSO is a widely used L^2 statistical methodology for simultaneous estimation and variable selection. In the last years, many authors analyzed this technique from a theoretical and applied point of view. In the first part of the seminar, we introduce and study the adaptive LASSO problem for discretely observed ergodic diffusion processes We prove oracle properties also deriving the asymptotic distribution of the LASSO estimator. In the second part of the seminar we present general L^p approach for stochastic differential equations with small diffusion noise. Finally, we present simulated and real data analysis to provide some evidence on the applicability of this method.
FMSP Lectures
http://faculty.ms.u-tokyo.ac.jp/~fmsp/jpn/conferences/fmsp.html
[ Reference URL ]The LASSO is a widely used L^2 statistical methodology for simultaneous estimation and variable selection. In the last years, many authors analyzed this technique from a theoretical and applied point of view. In the first part of the seminar, we introduce and study the adaptive LASSO problem for discretely observed ergodic diffusion processes We prove oracle properties also deriving the asymptotic distribution of the LASSO estimator. In the second part of the seminar we present general L^p approach for stochastic differential equations with small diffusion noise. Finally, we present simulated and real data analysis to provide some evidence on the applicability of this method.
FMSP Lectures
http://faculty.ms.u-tokyo.ac.jp/~fmsp/jpn/conferences/fmsp.html
http://www.sigmath.es.osaka-u.ac.jp/~kamatani/statseminar/2012/14.html
