27 citations · 106 across the 18 of their papers we have counts for
12 papers · 1 filter
Holonomic gradient method for the distribution function of the largest root of a Wishart matrix
Hiroki Hashiguchi, Yasuhide Numata, Nobuki Takayama +1
We apply the holonomic gradient method introduced by Nakayama et al.(2011) to the evaluation of the exact distribution function of the largest root of a Wishart matrix, which invol…
Running Markov chain without Markov basis
Hisayuki Hara, Satoshi Aoki, Akimichi Takemura
The methodology of Markov basis initiated by Diaconis and Sturmfels(1998) stimulated active research on Markov bases for more than ten years. It also motivated improvements of algo…
Properties of semi-elementary imsets as sums of elementary imsets
Takuya Kashimura, Tomonari Sei, Akimichi Takemura +1
We study properties of semi-elementary imsets and elementary imsets introduced by Studeny (2005). The rules of the semi-graphoid axiom (decomposition, weak union and contraction) f…
Stochastic Bandit Based on Empirical Moments
Junya Honda, Akimichi Takemura
In the multiarmed bandit problem a gambler chooses an arm of a slot machine to pull considering a tradeoff between exploration and exploitation. We study the stochastic bandit prob…
Conformal geometry of statistical manifold with application to sequential estimation
Masayuki Kumon, Akimichi Takemura, Kei Takeuchi
We present a geometrical method for analyzing sequential estimating procedures. It is based on the design principle of the second-order efficient sequential estimation provided in…
A Markov basis for two-state toric homogeneous Markov chain model without initial parameters
Hisayuki Hara, Akimichi Takemura
We derive a Markov basis consisting of moves of degree at most three for two-state toric homogeneous Markov chain model of arbitrary length without parameters for initial states. O…
Hierarchical subspace models for contingency tables
Hisayuki Hara, Tomonari Sei, Akimichi Takemura
For statistical analysis of multiway contingency tables we propose modeling interaction terms in each maximal compact component of a hierarchical model. By this approach we can sea…
Markov chain Monte Carlo tests for designed experiments
Satoshi Aoki, Akimichi Takemura
We consider conditional exact tests of factor effects in designed experiments for discrete response variables. Similarly to the analysis of contingency tables, a Markov chain Monte…
Hierarchical orbital decompositions and extended decomposable distributions
Hidehiko Kamiya, Akimichi Takemura
Elliptically contoured distributions can be considered to be the distributions for which the contours of the density functions are proportional ellipsoids. Kamiya, Takemura and Kur…
Strong consistency of MLE for finite mixtures of location-scale distributions when the scale parameters are exponentially small (full version)
Kentaro Tanaka, Akimichi Takemura
In a finite mixture of location-scale distributions maximum likelihood estimator does not exist because of the unboundedness of the likelihood function when the scale parameter of…
Conditions for swappability of records in a microdata set when some marginals are fixed
Akimichi Takemura, Hisayuki Hara
We consider swapping of two records in a microdata set for the purpose of disclosure control. We give some necessary and sufficient conditions that some observations can be swapped…
Iterative proportional scaling via decomposable submodels for contingency tables
Yushi Endo, Akimichi Takemura
We propose iterative proportional scaling (IPS) via decomposable submodels for maximizing likelihood function of a hierarchical model for contingency tables. In ordinary IPS the pr…