[Todos] Seminario de Probabilidad y Estadística Matemática

[Pablo Groisman]

Seminario de Probabilidad y Estadística Matemática.

ATENCIÓN: El próximo miércoles tenemos un encuentro con dos charlas y
almuerzo en el medio. Están todos invitados.

PROXIMO ENCUENTRO: Miércoles 1 de julio,
LUGAR: Instituto de Cálculo, 2do piso, Pabellón 2.

PRIMERA CHARLA: 12:00hs.
EXPOSITORA: Efstathia Bura, Universidad de Washington.
TITULO: Dimension Estimation in Sufficient Dimension Reduction: A
Unifying Approach.

SEGUNDA CHARLA: 13.30hs.
EXPOSITOR: Martin Evans, University of Edinburgh
TITULO: Diffusion  with  stochastic resetting


RESUMEN EFSTAHIA: Sufficient Dimension Reduction (SDR) in regression
comprises of estimation of the dimension of the smallest (central)
dimension
reduction subspace and its basis elements. For SDR methods based on a
kernel matrix, such as SIR and SAVE, the dimension estimation is
equivalent to the estimation of the rank of a random matrix which is
the sample based estimate of the kernel. A test for the rank of a
random matrix amounts to testing how many of its eigen- or singular
values are equal to zero. We propose two tests based on the smallest
eigen- or singular values of the estimated matrix: an asymptotic
weighted chi-square test and a Wald-type asymptotic chi-square test.
We also provide an asymptotic chi-square test for assessing whether
elements of the left singular vectors of the random matrix are zero.
These methods together constitute a unified approach for all SDR
methods based on a kernel matrix that covers estimation of the central
subspace and its dimension, as well as assessment of variable
contribution to the lower dimensional predictor projections with
variable selection a special case. A small power simulation study
shows that the proposed and existing tests, specific to each SDR
method, perform similarly with respect to power and achievement of the
nominal level. Also, the importance of choice of number of slices as a
tuning parameter is further exhibited.


RESUMEN MARTIN:  Stochastic resetting is a rather common process in
everyday life. Consider searching for some target such as, for
example, a face in a crowd or one's misplaced keys at
home. A natural tendency is, on having searched unsuccessfully for a
while, to return to the starting point and recommence the search. In
this talk I explore the consequences of such resetting on the
diffusion of a single or a multiparticle system. We show that
resetting has a rather rich and dramatic effect on mean first passage
times and survival probabilities in the presence of a trap.


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Para ver el calendario del seminario
http://www.google.com/calendar/embed?src=987brtcpho5tt3ch2ud0oobkds%40group.calendar.google.com&ctz=America/Argentina/Buenos_Aires

Para mas información sobre el seminario
http://mate.dm.uba.ar/~drodrig/seminario/

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