Asymptotic Statistics and Related Topics:
Theories and Methodologies
2-4 September 2013,Bernoulli Society Satellite Meeting to the ISI World Statistics Congress 2013
Sanjo Conference Hall, The University of Tokyo, Tokyo, Japan
Recent developments in statistics require stronger tie-ups and integration
of various research areas such as asymptotic decision theory, stochastic
analysis, asymptotic distribution theory, computational statistics, stochastic
numerical analysis, statistical software, machine learning, and applied
fields. Topics in asymptotic statistics and related fields will be discussed
comprehensively in this meeting.
Topics in asymptotic statistics and related fields will be discussed comprehensively
in this conference:
- Stochastic analysis and limit theorems
Advanced knowledge of stochastic analysis and limit theorem is indispensable
for statistical inference for stochastic-process models, which include
diffusions with jumps, Lévy processes, and fractional processes,
to mention just a few. Limit theorems for covariance estimators and power
variations are now hot spots in high-frequency financial data analysis.
Interaction between limit theorems for stochastic processes with the Malliavin
calculus gives a new perspective to asymptotic distribution theory.
- Theory of asymptotic inference and its applications
Statistical inference for sampled stochastic processes requires new developments
in asymptotic theory. Quasi likelihood analysis serves as guiding principles.
Change point problems suggest importance of a framework of the asymptotic
decision theory. Nonlinear time series analysis gives many problems in
construction of estimating function and computations. Statistical inference
for jump processes is producing various asymptotic results that are essentially
different from the classical asymptotic theory. We invite talks on computational
methods such as bootstrap and MCMC. We also encourage topics in statistical
- Stochastic numerical analysis and computational statistics
Discretization and error estimation are of importance in applications of
stochastic differential equations. These are deeply related with the sampling
problem in statistics for stochastic processes. Asymptotic expansion methods
and their half-analytic variations are nowadays used in real financial
world. Implementation is a question we should answer. Topics in computational
statistics and applications to data analysis are also welcome in the session.
- Insurance mathematics and risk theory
Analysis of financial and insurance risks are extremely important in recent
economics. Those risks have been well studied in a long history of actuarial
science. After progresses in probability and mathematical statistics, insurance
risk theory is dramatically changing. We will discuss recent developments
from mathematical and statistical point of view.
- Statistical learning theory
Machine learning is one of scientific fields to which theoretical statistics
makes fundamental contributions. Some of topics, e.g., reinforcement learning,
ensemble learning, random forest, support vector machine, sparse coding,
will be discussed. Nonparametric and semiparametric methods play an essential
role. Talks in linear/nonlinear multivariate analysis, density estimation
or model selection enter any one of sessions, depending on the theme.
Kuriki, Satoshi (Institute of Statistical Mathematics) Website: http://www.sigmath.es.osaka-u.ac.jp/~tokyo2013/
Lee, Sang-Yeol (Seoul National University)
Masuda, Hiroki (Kyushu University)
Shimizu, Yasutaka (Osaka University)
Suzuki, Taiji (The University of Tokyo)
Uchida, Masayuki (Osaka University)
Yoshida, Nakahiro (The University of Tokyo)