Searcharxiv⌕ Search

arXiv subjects

Masayuki Uchida

Publications and source records attributed to Masayuki Uchida.

At least 19 recordsLinked to original sources
arXiv2026

Small noise asymptotics for linear parabolic SPDEs in two space dimensions with unknown damping factors

We study parametric estimation for second order linear parabolic stochastic partial differential equations in two space dimensions with a small volatility parameter driven by a $Q$-Wiener process with an unknown damping parameter using high frequency spatio-temporal data. We first provide an estimator for the damping parameter of the $Q$-Wiener process utilizing realized quadratic variations based on spatial and temporal increments. We next propose minimum contrast estimators of the diffusive and advective parameters in the SPDE using a contrast function with the proposed estimator of the damping parameter. We then construct a quasi-maximum likelihood estimator of the reaction parameter in the SPDE using the approximate coordinate process derived from the estimators of the diffusive and advective parameters. We also provide simulation results of the proposed estimators.

math.ST↗
arXiv2026

QBIC of SEM for jump-diffusion processes based on high-frequency data

Structural equation modeling (SEM) is a statistical method for analyzing relationships among latent variables. Since SEM is a confirmatory method, the model needs to be specified in advance. In practice, however, statisticians have several candidate models and aim to select the most appropriate one among them. In this paper, we consider model selection in SEM for jump-diffusion processes. We propose a quasi-Bayesian information criterion (QBIC) for the SEM and show that the proposed criterion has model-selection consistency.

math.ST↗
arXiv2025

Volatility change point detection for linear parabolic SPDEs

We consider change point detection for the volatility in second order linear parabolic stochastic partial differential equations based on high frequency spatio-temporal data. We give a test statistic to detect changes in the volatility based on change point analysis for diffusion processes and derive the asymptotic null distribution of the test statistic. We also show that the test is consistent. Moreover, we provide some examples and then perform numerical simulations of the proposed test statistic.

math.ST↗
arXiv2025

Akaike-type information criterion of SEM for jump-diffusion processes based on high-frequency data

Structural equation modeling (SEM) is a statistical method used to investigate relationships among latent variables. In SEM, the model must be specified in advance. However, in practice, statisticians often have several candidate models and need to select the most appropriate one. Consequently, model selection is a key issue in SEM, and information criteria are commonly used to address this issue. In this study, we develop an Akaike-type information criterion of SEM for jump-diffusion processes, which enables model selection for SEM based on high-frequency data with jumps. Simulation studies are conducted to illustrate the finite-sample performance of the proposed method.

math.ST↗
arXiv2025

Statistical inference in SEM for diffusion processes with jumps based on high-frequency data

We study structural equation modeling (SEM) for diffusion processes with jumps. Based on high-frequency data, we consider the parameter estimation and the goodness-of-fit test in the SEM. Using a threshold method, we propose the quasi-likelihood of the SEM and prove that the quasi-maximum likelihood estimator has consistency and asymptotic normality. To examine whether a specified parametric model is correct or not, we also construct the quasi-likelihood ratio test statistics and investigate the asymptotic properties. Furthermore, numerical simulations are conducted.

math.ST↗
arXiv2025

Estimation for linear parabolic SPDEs in two space dimensions with unknown damping parameters

We study parametric estimation for second order linear parabolic stochastic partial differential equations (SPDEs) in two space dimensions driven by two types of $Q$-Wiener processes based on high frequency spatio-temporal data. First, we give estimators for damping parameters of the $Q$-Wiener processes of the SPDE using realized quadratic variations based on temporal and spatial increments. We next propose minimum contrast estimators of four coefficient parameters in the SPDE and obtain estimators of the rest of unknown parameters in the SPDE using an approximate coordinate process. We also examine numerical simulations of the proposed estimators.

math.ST↗
arXiv2025

Quasi-likelihood ratio test for jump-diffusion processes based on adaptive maximum likelihood inference

In this paper, we consider parameter estimation and quasi-likelihood ratio tests for multidimensional jump-diffusion processes defined by stochastic differential equations. In general, simultaneous estimation faces challenges such as an increase of computational time for optimization and instability of estimation accuracy as the dimensionality of parameters grows. To address these issues, we propose an adaptive quasi-log likelihood function based on the joint quasi-log likelihood function introduced by Shimizu and Yoshida (2003, 2006) and Ogihara and Yoshida (2011). We then show that the resulting adaptive estimators possess consistency and asymptotic normality. Furthermore, we extend the joint quasi-log likelihood function proposed by Shimizu and Yoshida (2003, 2006) and Ogihara and Yoshida (2011) and construct a test statistic using the proposed adaptive estimators. We prove that the proposed test statistic converges in distribution to a $χ^2$-distribution under the null hypothesis and that the associated test is consistent. Finally, we conduct numerical simulations using a specific jump-diffusion process model to examine the asymptotic behavior of the proposed adaptive estimators and test statistics.

math.ST↗
arXiv2024

Small dispersion asymptotics for an SPDE in two space dimensions using triple increments

We consider parametric estimation for a second order linear parabolic stochastic partial differential equation (SPDE) in two space dimensions driven by a $Q$-Wiener process with a small noise based on high frequency spatio-temporal data. We first provide estimators of the diffusive and advective parameters in the SPDE using temporal and spatial increments. We then construct an estimator of the reaction parameter in the SPDE based on an approximate coordinate process. We also give simulation results of the proposed estimators.

math.ST↗
arXiv2024

Estimation for the damping factor of the driving process of an SPDE in two space dimensions

We study parametric estimation for a second order linear parabolic stochastic partial differential equation (SPDE) in two space dimensions driven by a $Q$-Wiener process based on high frequency spatio-temporal data. We give an estimator of the damping parameter of the $Q$-Wiener process of the SPDE based on quadratic variations with temporal and spatial increments. We also provide simulation results of the proposed estimator.

math.ST↗
arXiv2024

QBIC of SEM for diffusion processes from discrete observations

We deal with a model selection problem for structural equation modeling (SEM) with latent variables for diffusion processes. Based on the asymptotic expansion of the marginal quasi-log likelihood, we propose two types of quasi-Bayesian information criteria of the SEM. It is shown that the information criteria have model selection consistency. Furthermore, we examine the finite-sample performance of the proposed information criteria by numerical experiments.

math.ST↗
arXiv2024

Small diffusivity asymptotics for a linear parabolic SPDE in two space dimensions

We consider parameter estimation of the reaction term for a second order linear parabolic stochastic partial differential equation in two space dimensions driven by a $Q$-Wiener process under small diffusivity. We first construct an estimator of the reaction parameter based on continuous spatio-temporal data, and then derive an estimator of the reaction parameter based on high frequency spatio-temporal data by discretizing the estimator based on the continuous data. We show that the estimators have consistency and asymptotic normality. Furthermore, we give simulation results of the estimator based on high frequency data.

math.ST↗
arXiv2024

Quasi-Akaike information criterion of structural equation modeling with latent variables for diffusion processes

We consider a model selection problem for structural equation modeling (SEM) with latent variables for diffusion processes based on high-frequency data. First, we propose the quasi-Akaike information criterion of the SEM and study the asymptotic properties. Next, we consider the situation where the set of competing models includes some misspecified parametric models. It is shown that the probability of choosing the misspecified models converges to zero. Furthermore, examples and simulation results are given.

math.ST↗
arXiv2023

Structural equation modeling with latent variables for diffusion processes and its application to sparse estimation

We consider structural equation modeling (SEM) with latent variables for diffusion processes based on high-frequency data. The quasi-likelihood estimators for parameters in the SEM are proposed. The goodness-of-fit test is derived from the quasi-likelihood ratio. We also treat sparse estimation in the SEM. The goodness-of-fit test for the sparse estimation in the SEM is developed. Furthermore, the asymptotic properties of our proposed estimators are examined.

math.ST↗
arXiv2023

Parametric estimation for linear parabolic SPDEs in two space dimensions based on temporal and spatial increments

We deal with parameter estimation for a linear parabolic second-order stochastic partial differential equation in two space dimensions driven by two types of $Q$-Wiener processes based on high frequency data with respect to time and space. We propose minimum contrast estimators of the coefficient parameters based on temporal and spatial squared increments, and provide adaptive estimators of the coefficient parameters based on an approximate coordinate process. We also give an example and simulation results of the proposed estimators.

math.ST↗
arXiv2022

Parameter estimation for a linear parabolic SPDE model in two space dimensions with a small noise

We study parameter estimation for a linear parabolic second-order stochastic partial differential equation (SPDE) in two space dimensions with a small dispersion parameter using high frequency data with respect to time and space. We set two types of $Q$-Wiener processes as a driving noise. We provide minimum contrast estimators of the coefficient parameters of the SPDE appearing in the coordinate process of the SPDE based on the thinned data in space, and approximate the coordinate process based on the thinned data in time. Moreover, we propose an estimator of the drift parameter using the fact that the coordinate process is the Ornstein-Uhlenbeck process and statistical inference for diffusion processes with a small noise.

math.ST↗
arXiv2022

Statistical inference in factor analysis for diffusion processes from discrete observations

We consider statistical inference in factor analysis for ergodic and non-ergodic diffusion processes from discrete observations. Factor model based on high frequency time series data has been mainly discussed in the field of high dimensional covariance matrix estimation. In this field, the method based on principal component analysis has been mainly used. However, this method is effective only for high dimensional model. On the other hand, there is a method based on the quasi-likelihood. However, since the factor is assumed to be observable, we cannot use this method when the factor is latent. Thus, the existing methods are not effective when the factor is latent and the dimension of the observable variable is not so high. Therefore, we propose an effective method in the situation.

math.ST↗
arXiv2022

Parameter estimation for linear parabolic SPDEs in two space dimensions based on high frequency data

We consider parameter estimation for a linear parabolic second-order stochastic partial differential equation (SPDE) in two space dimensions driven by two types $Q$-Wiener processes based on high frequency data in time and space. We first estimate the parameters which appear in the coordinate process of the SPDE using the minimum contrast estimator based on the thinned data with respect to space, and then construct an approximate coordinate process of the SPDE. Furthermore, we propose estimators of the coefficient parameters of the SPDE utilizing the approximate coordinate process based on the thinned data with respect to time. We also give some simulation results.

math.ST↗