arXiv subjects

Takuma Yoshida

Publications and source records attributed to Takuma Yoshida.

12 recordsLinked to original sources

arXiv2026

Janus-induced atomic reconstruction amplifies twist-angle modulation of interlayer thermal transport in moir\'e bilayers

In two-dimensional moir\'e bilayers, atomic reconstruction, the spontaneous structural relaxation toward energy-minimizing stacking registries in the near-commensurate regime, can strongly modify local stacking and interlayer coupling, providing a possibility to significantly control phonon-mediated properties. Here we show that the twist-angle dependence of interlayer thermal conductance can be modified by introducing Janus-induced mirror-symmetry breaking into bilayer MoS2. The intrinsic out-of-plane dipole in MoSSe/MoS2 bilayers leads to frictionless interface, and reduces the lattice deformation energy, thereby promoting atomic-reconstruction into locally distorted aperiodic moir\'e patterns. These features weaken interlayer coupling and suppress phonon transmission across the interface, leading to an anomalously strong twist-angle dependence of thermal conductance, with a pronounced minimum at small twist angles and a reduction rate nearly one order of magnitude larger than that of twisted bilayer MoS2. Our results demonstrate that interlayer thermal transport is modified by atomic reconstruction, highlighting Janus-induced mirror-symmetry breaking as an effective way to promoting phonon engineering in two-dimensional moir\'e structures.

cond-mat.mes-hall↗

arXiv2026

Structural grouping of extreme value models via graph fused lasso

The generalized Pareto distribution (GPD) is a fundamental model for analyzing the tail behavior of a distribution. In particular, the shape parameter of the GPD characterizes the extremal properties of the distribution. As described in this paper, we propose a method for grouping shape parameters in the GPD for clustered data via graph fused lasso. The proposed method simultaneously estimates the model parameters and identifies which clusters can be grouped together. We establish the asymptotic theory of the proposed estimator and demonstrate that its variance is lower than that of the cluster-wise estimator. This variance reduction not only enhances estimation stability but also provides a principled basis for identifying homogeneity and heterogeneity among clusters in terms of their tail behavior. We assess the performance of the proposed estimator through Monte Carlo simulations. As an illustrative example, our method is applied to rainfall data from 996 clustered sites across Japan.

stat.ME↗

arXiv2026

Small area estimation of dependent extreme value indices

In extreme value analysis, tail behavior of a heavy-tailed data distribution is modeled by a Pareto-type distribution in which the so-called extreme value index (EVI) controls the tail behavior. For heavy-tailed data obtained from multiple population subgroups, or areas, this study efficiently predicts the EVIs of all areas using information among areas. For this purpose, we propose a mixed effects model, which is a useful approach in small area estimation. In this model, we represent differences among areas in the EVIs by latent variables called random effects. Using correlated random effects across areas, we incorporate the relations among areas into the model. The obtained model achieves simultaneous prediction of EVIs of all areas. Herein, we describe parameter estimation and random effect prediction in the model, and clarify theoretical properties of the estimator. Additionally, numerical experiments are presented to demonstrate the effectiveness of the proposed method. As an application of our model, we provide a risk assessment of heavy rainfall in Japan.

stat.ME↗

arXiv2025

Asymptotic theory for extreme value generalized additive models

The classical approach to analyzing extreme value data is the generalized Pareto distribution (GPD). When the GPD is used to explain a target variable with the large dimension of covariates, the shape and scale function of covariates included in GPD are sometimes modeled using the generalized additive models (GAM). In contrast to many results of application, there are no theoretical results on the hybrid technique of GAM and GPD, which motivates us to develop its asymptotic theory. We provide the rate of convergence of the estimator of shape and scale functions, as well as its local asymptotic normality.

math.ST↗

arXiv2025

Single-index models for extreme value index regression

Since the extreme value index (EVI) controls the tail behaviour of the distribution function, the estimation of EVI is a very important topic in extreme value theory. Recent developments in the estimation of EVI along with covariates have been in the context of nonparametric regression. However, for the large dimension of covariates, the fully nonparametric estimator faces the problem of the curse of dimensionality. To avoid this, we apply the single index model to EVI regression under Pareto-type tailed distribution. We study the penalized maximum likelihood estimation of the single index model. The asymptotic properties of the estimator are also developed. Numerical studies are presented to show the efficiency of the proposed model.

math.ST↗

arXiv2024

Sure independence screening for covariate-dependent extreme value index estimation

One of the main topics in extreme value analysis is the estimation of the extreme value index, which characterizes the tail behavior of a distribution. Although covariate dependent extreme value index estimation has been widely studied, covariate screening for high-dimensional covariates has not been fully investigated. This paper proposes a sure independence screening method for covariate-dependent extreme value index estimation. The proposed method ranks covariates by marginal utilities constructed from a kernel-based conditional Pickands estimator. Unlike ordinary local smoothing, the proposed screening procedure uses a large-bandwidth kernel regime to obtain stable marginal contrasts. We establish the sure screening property under this regime, showing that all truly active covariates are retained with probability tending to one. Simulation studies and a real-data application demonstrate the effectiveness of the proposed method.

stat.ME↗

arXiv2024

Mixed effects models for extreme value index regression

Extreme value theory (EVT) provides an elegant mathematical tool for the statistical analysis of rare events. When data are collected from multiple population subgroups, because some subgroups may have less data available for extreme value analysis, a scientific interest of many researchers would be to improve the estimates obtained directly from each subgroup. To achieve this, we incorporate the mixed effects model (MEM) into the regression technique in EVT. In small area estimation, the MEM has attracted considerable attention as a primary tool for producing reliable estimates for subgroups with small sample sizes, i.e., ``small areas.'' The key idea of MEM is to incorporate information from all subgroups into a single model and to borrow strength from all subgroups to improve estimates for each subgroup. Using this property, in extreme value analysis, the MEM may contribute to reducing the bias and variance of the direct estimates from each subgroup. This prompts us to evaluate the effectiveness of the MEM for EVT through theoretical studies and numerical experiments, including its application to the risk assessment of a number of stocks in the cryptocurrency market.

stat.ME↗

arXiv2023

Hypothesis testing for varying coefficient models in tail index regression

This study examines the varying coefficient model in tail index regression. The varying coefficient model is an efficient semiparametric model that avoids the curse of dimensionality when including large covariates in the model. In fact, the varying coefficient model is useful in mean, quantile, and other regressions. The tail index regression is not an exception. However, the varying coefficient model is flexible, but leaner and simpler models are preferred for applications. Therefore, it is important to evaluate whether the estimated coefficient function varies significantly with covariates. If the effect of the non-linearity of the model is weak, the varying coefficient structure is reduced to a simpler model, such as a constant or zero. Accordingly, the hypothesis test for model assessment in the varying coefficient model has been discussed in mean and quantile regression. However, there are no results in tail index regression. In this study, we investigate the asymptotic properties of an estimator and provide a hypothesis testing method for varying coefficient models for tail index regression.

math.ST↗

arXiv2019

Nonparametric smoothing for extremal quantile regression with heavy tailed distributions

In several different fields, there is interest in analyzing the upper or lower tail quantile of the underlying distribution rather than mean or center quantile. However, the investigation of the tail quantile is difficult because of data sparsity. In this paper, we attempt to develop nonparametric quantile regression for the extremal quantile level. In extremal quantile regression, there are two types of technical conditions of the order of convergence of the quantile level: intermediate order or extreme order. For the intermediate order quantile, the ordinary nonparametric estimator is used. On the other hand, for the extreme order quantile, we provide a new estimator by extrapolating the intermediate order quantile estimator. The performance of the estimator is guaranteed by asymptotic theory and extreme value theory. As a result, we show the asymptotic normality and the rate of convergence of the nonparametric quantile regression estimator for both intermediate and extreme order quantiles. A simulation is presented to confirm the behavior of the proposed estimator. The data application is also assessed.

math.ST↗

arXiv2012

Asymptotics for penalized spline estimators in quantile regression

Quantile regression predicts the $τ$-quantile of the conditional distribution of a response variable given the explanatory variable for $τ\in(0,1)$. The aim of this paper is to establish the asymptotic distribution of the quantile estimator obtained by penalized spline method. A simulation and an exploration of real data are performed to validate our results.

math.ST↗

arXiv2012

Asymptotics for penalized splines in generalized additive models

This paper discusses asymptotic theory for penalized spline estimators in generalized additive models. The purpose of this paper is to establish the asymptotic bias and variance as well as the asymptotic normality of the penalized spline estimators proposed by Marx and Eilers (1998). Furthermore, the asymptotics for the penalized quasi likelihood fit in mixed models are also discussed.

math.ST↗

arXiv2012

Semiparametric Penalized Spline Regression

In this paper, we propose a new semiparametric regression estimator by using a hybrid technique of a parametric approach and a nonparametric penalized spline method. The overall shape of the true regression function is captured by the parametric part, while its residual is consistently estimated by the nonparametric part. Asymptotic theory for the proposed semiparametric estimator is developed, showing that its behavior is dependent on the asymptotics for the nonparametric penalized spline estimator as well as on the discrepancy between the true regression function and the parametric part. As a naturally associated application of asymptotics, some criteria for the selection of parametric models are addressed. Numerical experiments show that the proposed estimator performs better than the existing kernel-based semiparametric estimator and the fully nonparametric estimator, and that the proposed criteria work well for choosing a reasonable parametric model.

math.ST↗