Zobrazeno 1 - 10
of 128
pro vyhledávání: '"Sei, Tomonari"'
Autor:
Sei, Tomonari
A method of constructing Markov chains on finite state spaces is provided. The chain is specified by three constraints: stationarity, dependence and marginal distributions. The generalized Pythagorean theorem in information geometry plays a central r
Externí odkaz:
http://arxiv.org/abs/2407.17682
Autor:
Sukeda, Issey, Sei, Tomonari
For a bivariate probability distribution, local dependence around a single point on the support is often formulated as the second derivative of the logarithm of the probability density function. However, this definition lacks the invariance under mar
Externí odkaz:
http://arxiv.org/abs/2407.16948
Autor:
Sukeda, Issey, Sei, Tomonari
In dependence modeling, various copulas have been utilized. Among them, the Frank copula has been one of the most typical choices due to its simplicity. In this work, we demonstrate that the Frank copula is the minimum information copula under fixed
Externí odkaz:
http://arxiv.org/abs/2406.14814
Autor:
Sukeda, Issey, Sei, Tomonari
Copulas have gained widespread popularity as statistical models to represent dependence structures between multiple variables in various applications. The minimum information copula, given a finite number of constraints in advance, emerges as the cop
Externí odkaz:
http://arxiv.org/abs/2306.01604
The paper considers the distribution of a general linear combination of central and non-central chi-square random variables by exploring the branch cut regions that appear in the standard Laplace inversion process. Due to the original interest from t
Externí odkaz:
http://arxiv.org/abs/2305.07434
Autor:
Takazawa, Yuki, Sei, Tomonari
Phylogenetic trees are key data objects in biology, and the method of phylogenetic reconstruction has been highly developed. The space of phylogenetic trees is a nonpositively curved metric space. Recently, statistical methods to analyze the set of t
Externí odkaz:
http://arxiv.org/abs/2211.12037
Autor:
Sei, Tomonari, Yano, Keisuke
We propose a method to construct a joint statistical model for mixed-domain data to analyze their dependence. Multivariate Gaussian and log-linear models are particular examples of the proposed model. It is shown that the functional equation defining
Externí odkaz:
http://arxiv.org/abs/2206.06792
Autor:
Chen, Yici, Sei, Tomonari
Multi-dimensional distributions whose marginal distributions are uniform are called copulas. Among them, the one that satisfies given constraints on expectation and is closest to the independent distribution in the sense of Kullback-Leibler divergenc
Externí odkaz:
http://arxiv.org/abs/2204.03118
Autor:
Yanagi, Mizuho, Sei, Tomonari
In causal inference, we can consider a situation in which treatment on one unit affects others, i.e., interference exists. In the presence of interference, we cannot perform a classical randomization test directly because a null hypothesis is not sha
Externí odkaz:
http://arxiv.org/abs/2203.10469
