JSPS科研費 , の研究の一環として,
2018年10月24日 (水) から 25日 (木) まで,
-  JP18H04092 基盤研究(A) 計算代数統計の方法の性能向上と実用化の推進
-  JP16H02792 基盤研究(B) 期待オイラー標数法の深化と実用化，および関連する数理の展開 (研究代表者 栗木哲)
会場は, 彦根キャンパス本部棟3階会議室1 の予定です.
- 青木敏氏 (神戸大学)/Satoshi AOKI (Kobe Univ.)
Characterizations of indicator functions for fractional
A polynomial indicator function of designs, introduced by Fontana, Pistone
Rogantin (2000), is a basic tool to characterize fractional
factorial designs in the field of computational algebraic statistics.
For the case of two-level designs, the structure of the indicator
function is well-known. For example, the coefficients of indicator
functions have clear meanings relating to
the orthogonality for the two-level cases. The polynomial relation among the
coefficients are also derived for the two-level cases, which can be used
to classify designs with given sizes.
However, for the cases of multi-level designs with rational factors,
such relations are complicated and interpretations are difficult.
In this work, we consider the structure
of the indicator function of general designs and its applications.
- 矢澤明喜子氏 (信州大学)/Akiko YAZAWA (Shinshu Univ.)
The Hessian matrix of a graph.
Let us consider the Hessian matrix of the weighted generating function for spanning trees in a graph $\Gamma$ with $n+1$ vertices.
We assume that $\Gamma$ is simple, undirected and connected.
We say that a subgraph in $\Gamma$ is a spanning tree
if it is connected and has $n$ edges.
A partial derivative corresponds to enumerating spanning trees including some edges.
We show that the Hessian does not vanish for some graph by a combinatorial proof.
- 白井朋之氏 (九州大学)/Tomoyuki SHIRAI (Kyushu Univ.)
Limit theorems for random analytic functions and their zeros
The study of random analytic functions (power series) and
their level sets has a long history and, especially,
there have been many works on Gaussian analytic functions,
which are random analytic functions that are also Gaussian processes.
In this talk, after we survey some recent topics around Gaussian analytic
functions and related point processes, we discuss limit theorems for random analytic functions and
- 伊師英之氏 (名古屋大学・JSTさきがけ)/
Hideyuki ISHI (Nagoya Univ. ・JST PRESTO)
Missing data problem and Cholesky decomposition
We consider a missing data problem for a multivariate normal distribution
under the conditional independence assigned by a undirected graph.
If there exists a perfect DAG structure on the graph compatible missing data pattern,
we give an explicit solution of the likelihood equation, where the Cholesky decomposition plays a crucial role.
Furthermore, in order to generalize the formula, we introduce an algebraic structure of a vector space of
real symmetric matrices that admits a nice Cholesky decomposition.
- 小原敦美氏 (福井大学)/Atsumi OHARA (Fukui Univ.)
Doubly autoparallel submanifolds in the space of the probability simplex
- Their characterization and classification -
We consider information geometry on the probability simplex to study
doubly autoparallel submanifolds.
Information geometry defines on the simplex two affine connections,
respectively called the exponential and the mixture connections.
The geometry naturally introduces submanifolds simultaneously autoparallel with respect to the both connections, which implies that such submanifolds are simultaneously exponential and mixture families of discrete probability distributions.We show their characterization and classification.
This is a joint work with Prof. H. Ishi (Nagoya Univ.).
下平英寿氏 (京都大学・理研AIP)/Hidetoshi SHIMODAIRA (Kyoto Uviv.・RIKEN AIP)
Selective inference for the problem of regions via multiscale bootstrap resampling with applications to hierarchical clustering and lasso
Selective inference procedures are considered for computing approximately unbiased p-values for arbitrary shaped hypotheses which are selected after looking at the data. Our idea is to estimate the geometric quantities, namely, signed distance and mean curvature, by the multiscale bootstrap in which we change the sample size of bootstrap replicates. Our method is second-order accurate in the large sample theory of smooth boundary surfaces of the hypothesis regions, and it is also justified for regions with nonsmooth surfaces such as cones. This is joint work with Yoshikazu Terada (Osaka University / RIKEN AIP).
- ブノワ コリンズ氏 (京都大学)/
Norm convergence for random permutations
Consider a finite sequence of independent random permutations on n points. We will explain why, in probability, as n goes to infinity, these permutations viewed as operators on the (n-1) dimensional vector space orthogonal to the vector with all coordinates equal to 1, are asymptotically strongly free. Our proof relies on the development of a matrix version of the non-backtracking operator theory and a refined trace method.
As a byproduct, we show that the non-trivial eigenvalues of random n-lifts of a fixed based graphs approximately achieve the Alon-Boppana bound with high probability in the large n limit. This result generalizes a theorem by Friedman. Time allowing, we will discuss more recent developments.
This is joint work with Charles Bordenave.
竹村彰通 (滋賀大学・統計数理研究所), 栗木哲 (統計数理研究所), 沼田泰英 (信州大).
nu at math.shinshu-u.ac.jp) まで連絡を下さい.
nu at math.shinshu-u.ac.jp) まで.