arXiv subjects

Shogo Kato

Publications and source records attributed to Shogo Kato.

At least 19 recordsLinked to original sources

arXiv2026

M\"obius transport on spheres

The M\"obius transformation that generates the spherical Cauchy distribution from the uniform is the tangent-normal lift of a one-dimensional optimal transport: the monotone rearrangement of the cosine about the location axis. This identifies the probabilistic nature of the M\"obius transformation and suggests a generalization: replacing the Cauchy target by any rotationally symmetric law, for instance the Poisson kernel or spherical cardioid, yields a generalized M\"obius transformation. M\"obius transport of a von Mises-Fisher base gives tractable anisotropic distributions on the sphere, with closed-form densities that inherit the base normalizing constant and allow immediate simulation. The M\"obius-von Mises-Fisher and isotropic scaled von Mises-Fisher distributions, the latter also arising from a M\"obius transport, are illustrated on paleomagnetic directions and short-period comet orbits, where they outperform classical and recently proposed alternatives.

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arXiv2026

A bivariate cure copula model with zero-inflated gamma frailty: dependence in both cure fractions and survival times

In biomedical studies, paired survival data arise naturally when two event times are observed within the same subject. Existing statistical models seldom accommodate both cure fractions and complex dependence structures. In this paper, we propose a novel bivariate cure frailty-copula model for paired survival data with a cure fraction. By incorporating a zero-inflated gamma frailty, the proposed framework simultaneously accommodates a cure fraction and continuous unobserved heterogeneity among uncured subjects. Dependence between cure statuses is modeled naturally via an odds-ratio parameter, while dependence between survival times conditional on frailty is captured through a copula. We show that the proposed model includes existing bivariate cure models as special cases. Population-level rank correlation coefficients are derived for the proposed model, namely tie-corrected versions of Kendall's tau and Spearman's rho. For suitable choices of marginal distributions and copula, the joint survival function admits a closed-form expression, enabling maximum likelihood estimation and likelihood ratio testing. Simulation studies and a real data application demonstrate the practical utility of the proposed approach. An R package, curecopula, implementing the proposed methods is publicly available on GitHub.

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arXiv2025

Modelling toroidal and cylindrical data via the trivariate wrapped Cauchy copula with non-uniform marginals

In this paper, we propose a new flexible family of distributions for data that consist of three angles, two angles and one linear component, or one angle and two linear components. To achieve this, we equip the recently proposed trivariate wrapped Cauchy copula with non-uniform marginals and develop a parameter estimation procedure. We compare our model to its main competitors for analyzing trivariate data and provide some evidence of its advantages. We illustrate our new model using toroidal data from protein bioinformatics of conformational angles, and cylindrical data from climate science related to buoy in the Adriatic Sea. The paper is motivated by these real trivariate datasets.

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arXiv2025

Regression for spherical responses with linear and spherical covariates using a scaled link function

We propose a regression model in which the responses are spherical variables and the covariates include linear and/or spherical variables. A novel link function is introduced by extending the M\"obius transformation on the sphere. This link function is an anisotropic mapping that enables scale control along each axis of the spherical covariates and for each linear covariate. It generalizes several well-known link functions for circular or linear covariates. Each parameter of the link function is clearly interpretable. For the error distribution, we consider a general class of elliptically symmetric distributions, which includes the Kent distribution, the elliptically symmetric angular Gaussian distribution, and the scaled von Mises-Fisher distribution. Axes of symmetry of the error distribution are determined using a method involving parallel transport. Maximum likelihood estimation is feasible via reparameterization of the proposed model. Moreover, the parameters of the link function and the shape/scale parameters of the error distribution are orthogonal in the sense of the Fisher information matrix. The proposed regression model is illustrated using two real datasets. An R software package accompanies this article.

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arXiv2025

An interpretable family of projected normal distributions and a related copula model for Bayesian analysis of hypertoroidal data

This paper introduces two families of probability distributions for Bayesian analysis of hypertoroidal data. The first family consists of symmetric distributions derived from the projection of multivariate normal distributions under specific parameter constraints. This family is closed under marginalization and hence any marginal distribution belongs to a lower-dimensional case of the same family. In particular the univariate marginal of the family is the unimodal case of the projected normal distribution on the circle. The second family is a flexible extension of the copula case of the first family, which can accommodate any univariate marginal distributions. Unlike existing models derived via projection, both families have the common advantage that their parameters possess a clear and intuitive interpretation. The use of latent variables simplifies Bayesian estimation using Markov chain Monte Carlo algorithms. The usefulness of the proposed families is demonstrated through the analysis of a meteorological dataset.

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arXiv2025

Minimum Copula Divergence for Robust Estimation

This paper introduces a robust estimation framework based solely on the copula function. We begin by introducing a family of divergence measures tailored for copulas, including the $\alpha$-, $\beta$-, and $\gamma$-copula divergences, which quantify the discrepancy between a parametric copula model and an empirical copula derived from data independently of marginal specifications. Using these divergence measures, we propose the minimum copula divergence estimator (MCDE), an estimation method that minimizes the divergence between the model and the empirical copula. The framework proves particularly effective in addressing model misspecifications and analyzing heavy-tailed data, where traditional methods such as the maximum likelihood estimator (MLE) may fail. Theoretical results show that common copula families, including Archimedean and elliptical copulas, satisfy conditions ensuring the boundedness of divergence-based estimators, thereby guaranteeing the robustness of MCDE, especially in the presence of extreme observations. Numerical examples further underscore MCDE's ability to adapt to varying dependence structures, ensuring its utility in real-world scenarios.

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arXiv2024

Directional data analysis using the spherical Cauchy and the Poisson kernel-based distribution

In 2020, two novel distributions for the analysis of directional data were introduced: the spherical Cauchy distribution and the Poisson kernel-based distribution. This paper provides a detailed exploration of both distributions within various analytical frameworks. To enhance the practical utility of these distributions, alternative parametrizations that offer advantages in numerical stability and parameter estimation are presented, such as implementation of the Newton-Raphson algorithm for parameter estimation, while facilitating a more efficient and simplified approach in the regression framework. Additionally, a two-sample location test based on the log-likelihood ratio test is introduced. This test is designed to assess whether the location parameters of two populations can be assumed equal. The maximum likelihood discriminant analysis framework is developed for classification purposes, and finally, the problem of clustering directional data is addressed, by fitting finite mixtures of Spherical Cauchy or Poisson kernel-based distributions. Empirical validation is conducted through comprehensive simulation studies and real data applications, wherein the performance of the spherical Cauchy and Poisson kernel-based distributions is systematically compared.

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arXiv2024

Measuring and testing tail equivalence

We call two copulas tail equivalent if their first-order approximations in the tail coincide. As a special case, a copula is called tail symmetric if it is tail equivalent to the associated survival copula. We propose a novel measure and statistical test for tail equivalence. The proposed measure takes the value of zero if and only if the two copulas share a pair of tail order and tail order parameter in common. Moreover, taking the nature of these tail quantities into account, we design the proposed measure so that it takes a large value when tail orders are different, and a small value when tail order parameters are non-identical. We derive asymptotic properties of the proposed measure, and then propose a novel statistical test for tail equivalence. Performance of the proposed test is demonstrated in a series of simulation studies and empirical analyses of financial stock returns in the periods of the world financial crisis and the COVID-19 recession. Our empirical analysis reveals non-identical tail behaviors in different pairs of stocks, different parts of tails, and the two periods of recessions.

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arXiv2024

Traffic Count Data Analysis Using Mixtures of Kato--Jones Distributions

We discuss the modelling of traffic count data that show the variation of traffic volume within a day. For the modelling, we apply mixtures of Kato-Jones distributions in which each component is unimodal and affords a wide range of skewness and kurtosis. We consider two methods for parameter estimation, namely, a modified method of moments and the maximum likelihood method. These methods were seen to be useful for fitting the proposed mixtures to our data. As a result, the variation in traffic volume was classified into the morning and evening traffic whose distributions have different shapes, particularly different degrees of skewness and kurtosis.

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arXiv2024

The trivariate wrapped Cauchy copula

In this paper, we propose a new flexible distribution for data on the three-dimensional torus which we call a trivariate wrapped Cauchy copula. Our trivariate copula has several attractive properties. It has a simple form of density and desirable modality properties. Its parameters allow for an adjustable degree of dependence between every pair of variables and these can be easily estimated. The conditional distributions of the model are well studied bivariate wrapped Cauchy distributions. Furthermore, the distribution can be easily simulated. Parameter estimation via maximum likelihood for the distribution is given and we highlight the simple implementation procedure to obtain these estimates. We illustrate our trivariate wrapped Cauchy copula on data from protein bioinformatics of conformational angles.

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arXiv2023

Measuring non-exchangeable tail dependence using tail copulas

Quantifying tail dependence is an important issue in insurance and risk management. The prevalent tail dependence coefficient (TDC), however, is known to underestimate the degree of tail dependence and it does not capture non-exchangeable tail dependence since it evaluates the limiting tail probability only along the main diagonal. To overcome these issues, two novel tail dependence measures called the maximal tail concordance measure (MTCM) and the average tail concordance measure (ATCM) are proposed. Both measures are constructed based on tail copulas and possess clear probabilistic interpretations in that the MTCM evaluates the largest limiting probability among all comparable rectangles in the tail, and the ATCM is a normalized average of these limiting probabilities. In contrast to the TDC, the proposed measures can capture non-exchangeable tail dependence. Analytical forms of the proposed measures are also derived for various copulas. A real data analysis reveals striking tail dependence and tail non-exchangeability of the return series of stock indices, particularly in periods of financial distress.

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arXiv2020

Copula-based measures of asymmetry between the lower and upper tail probabilities

We propose a copula-based measure of asymmetry between the lower and upper tail probabilities of bivariate distributions. The proposed measure has a simple form and possesses some desirable properties as a measure of asymmetry. The limit of the proposed measure as the index goes to the boundary of its domain can be expressed in a simple form under certain conditions on copulas. A sample analogue of the proposed measure for a sample from a copula is presented and its weak convergence to a Gaussian process is shown. Another sample analogue of the presented measure, which is based on a sample from a distribution on $\mathbb{R}^2$, is given. Simple methods for interval estimation and nonparametric testing based on the two sample analogues are presented. As an example, the presented measure is applied to daily returns of S&P500 and Nikkei225.

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arXiv2020

Flexible random-effects distribution models for meta-analysis

In meta-analysis, the random-effects models are standard tools to address between-study heterogeneity in evidence synthesis analyses. For the random-effects distribution models, the normal distribution model has been adopted in most systematic reviews due to its computational and conceptual simplicity. However, the restrictive model assumption might have serious influences on the overall conclusions in practices. In this article, we first provide two examples of real-world evidence that clearly show that the normal distribution assumption is unsuitable. To address the model restriction problem, we propose alternative flexible random-effects models that can flexibly regulate skewness, kurtosis and tailweight: skew normal distribution, skew t-distribution, asymmetric Subbotin distribution, Jones-Faddy distribution, and sinh-arcsinh distribution. We also developed a R package, flexmeta, that can easily perform these methods. Using the flexible random-effects distribution models, the results of the two meta-analyses were markedly altered, potentially influencing the overall conclusions of these systematic reviews. The flexible methods and computational tools can provide more precise evidence, and these methods would be recommended at least as sensitivity analysis tools to assess the influence of the normal distribution assumption of the random-effects model.

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arXiv2018

Möbius transformation and a Cauchy family on the sphere

We present some properties of a Cauchy family of distributions on the sphere, which is a spherical extension of the wrapped Cauchy family on the circle. The spherical Cauchy family is closed under the Möbius transformation on the sphere and there is a similar induced transformation on the parameter space. Stereographic projection transforms the the spherical Cauchy family into a multivariate $t$-family with a certain degree of freedom on Euclidean space. Many tractable properties of the spherical Cauchy are derived using the Möbius transformation and stereographic projection. A method of moments estimator and an asymptotically efficient estimator are expressed in closed form. The maximum likelihood estimation is also straightforward.

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arXiv2015

A flexible family of distributions on the cylinder

We propose a flexible family of distributions, generalized $t$-distributions, on the cylinder which is obtained as a conditional distribution of a trivariate $t$ distribution. The new distribution has unimodality or bimodality, symmetry or asymmetry, depending on the values of parameters and flexibly fits the cylindrical data. The circular marginal of this distribution is distributed as a generalized $t$-distribution on the circle. Some other properties are also investigated. The proposed distribution is applied to the real cylindrical data.

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arXiv2014

A characterization of a Cauchy family on the complex space

It is shown that a family of distributions on the complex space is characterized as the only family such that the orbit of one distribution under a certain group of transformations on the complex space is the same as that under the group of affine transformations. The resulting family is compared with some existing families.

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arXiv2013

An extended family of circular distributions related to wrapped Cauchy distributions via Brownian motion

We introduce a four-parameter extended family of distributions related to the wrapped Cauchy distribution on the circle. The proposed family can be derived by altering the settings of a problem in Brownian motion which generates the wrapped Cauchy. The densities of this family have a closed form and can be symmetric or asymmetric depending on the choice of the parameters. Trigonometric moments are available, and they are shown to have a simple form. Further tractable properties of the model are obtained, many by utilizing the trigonometric moments. Other topics related to the model, including alternative derivations and Möbius transformation, are considered. Discussion of the symmetric submodels is given. Finally, generalization to a family of distributions on the sphere is briefly made.

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arXiv2012

Robust estimation of location and concentration parameters for the von Mises-Fisher distribution

Robust estimation of location and concentration parameters for the von Mises-Fisher distribution is discussed. A key reparametrisation is achieved by expressing the two parameters as one vector on the Euclidean space. With this representation, we first show that maximum likelihood estimator for the von Mises-Fisher distribution is not robust in some situations. Then we propose two families of robust estimators which can be derived as minimisers of two density power divergences. The presented families enable us to estimate both location and concentration parameters simultaneously. Some properties of the estimators are explored. Simple iterative algorithms are suggested to find the estimates numerically. A comparison with the existing robust estimators is given as well as discussion on difference and similarity between the two proposed estimators. A simulation study is made to evaluate finite sample performance of the estimators. We consider a sea star dataset and discuss the selection of the tuning parameters and outlier detection.

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