arXiv subjects

Hiroki Masuda

Publications and source records attributed to Hiroki Masuda.

At least 19 recordsLinked to original sources

arXiv2026

Violet: Enabling Full Virtualization for M-mode RTOS on RISC-V

In embedded systems, complex configurations may be required, such as the simultaneous execution of a real-time operating system (RTOS) and a general-purpose operating system (GPOS), or the operation of multiple RTOS instances. Embedded system hypervisors have been studied and developed to meet these requirements for architectures like ARM and x86. RISC-V is experiencing growing adoption in embedded systems and faces similar needs. However, RISC-V's virtualization support targets only U-mode (where applications run) and S-mode (where general-purpose OSs run) as virtualization levels. The M-mode, where RTOSs like FreeRTOS or Zephyr run, is excluded from virtualization. This means that, similar to architectures like ARM, running an RTOS on a Virtual Machine (VM) using methods based on virtualization support features is impossible. Therefore, this paper proposes the Violet hypervisor. Violet combines RISC-V's virtualization features with software-based emulation, enabling the execution of unmodified M-mode RTOSs. Evaluation verified the validity of the M-mode emulation functionality using RISC-V architecture tests. Furthermore, this was implemented on the SiFive HiFive Premier P550 hardware, demonstrating that existing RTOSs can run on Violet's VM and that coexistence with GPOSs like Linux is also possible. The performance evaluation also quantified the overhead introduced by M-mode emulation on M-mode CSR accesses, timer interrupt latency, and context switching.

cs.OS↗

arXiv2026

Robustified Gaussian quasi-BIC for volatility

We develop a theoretical foundation for robust model comparison in a class of non-ergodic continuous volatility regression models contaminated by finite-activity jumps. Using the density-power weighting and the H\"{o}lder(-inequality)-based normalization of the conventional Gaussian quasi-likelihood function, we propose two Schwarz-type statistics and also establish their model selection consistency with respect to the minimal true parametric volatility coefficient. Numerical experiments are conducted to illustrate our theoretical findings.

math.ST↗

arXiv2026

LAD estimation of locally stable SDE

We prove the asymptotic mixed normality of the least absolute deviation (LAD) estimator for a locally $\alpha$-stable stochastic differential equation (SDE) observed at high frequency, where $\alpha\in(0,2)$. We investigate both ergodic and non-ergodic cases, where the terminal sampling time diverges or is fixed, respectively, under different sets of assumptions. The objective function for the LAD estimator is expressed in a fully explicit form without necessitating numerical integration, offering a significant computational advantage over the existing non-Gaussian stable quasi-likelihood approach.

math.ST↗

arXiv2026

Asymptotic inference for skewed stable Ornstein-Uhlenbeck process

We consider the parametric estimation of the Ornstein-Uhlenbeck process driven by a non-Gaussian $\alpha$-stable L\'{e}vy process with the stable index $\alpha>1$ and possibly skewed jumps, based on a discrete-time sample over a fixed period. By employing a suitable non-diagonal normalizing matrix, we present the following: the parametric family satisfies the local asymptotic mixed normality with a non-degenerate Fisher information matrix; there exists a local maximum of the log-likelihood function which is asymptotically mixed-normal; the local maximum is asymptotically efficient in the sense that it has maximal concentration around the true value over symmetric convex Borel subsets. In the proof, we prove the asymptotic equivalence between the genuine likelihood and the much simpler Euler-type quasi-likelihood. Furthermore, we propose a simple moment-based method to estimate the parameters of the driving stable L\'{e}vy process, which serves as an initial estimator for numerical search of the (quasi-)likelihood, reducing the computational burden of the optimization to a large extent. We also present simulation results, which illustrate the theoretical results and highlight the advantages and disadvantages of the genuine and quasi-likelihood approaches.

math.ST↗

arXiv2025

Robustified Gaussian quasi-likelihood inference for volatility

We consider statistical inference for a class of continuous semimartingale regression models based on high-frequency observations subject to contamination by finite-activity jumps and spike noise. By employing density-power weighting and H\"{o}lder-inequality-based normalization, we propose easy-to-implement, robustified versions of the conventional Gaussian quasi-maximum-likelihood estimator that require only a single tuning parameter. We prove their asymptotic mixed normality at the standard rate of $\sqrt{n}$. It is theoretically shown that these estimators are simultaneously robust against contamination in both the covariate and response processes. Additionally, under suitable conditions on the selection of the tuning parameter, the proposed estimators achieve the same asymptotic distribution as the conventional estimator in the contamination-free case. Illustrative simulation results highlight the estimators' insensitivity to the choice of the tuning parameter.

math.ST↗

arXiv2025

Quasi-likelihood inference for SDE with mixed-effects observed at high frequency

We consider statistical inference for a class of dynamic mixed-effect models described by stochastic differential equations whose drift and diffusion coefficients simultaneously depend on fixed- and random-effect parameters. Assuming that each process is observed at high frequency and the number of individuals goes to infinity, we propose a stepwise inference procedure and prove its theoretical properties. The methodology is based on suitable quasi-likelihood functions by profiling the random effect in the diffusion coefficient at the first stage, and then integrating out the Gaussian random effect in the drift coefficient to obtain the marginal distribution in the second stage, resulting in a fully explicit and computationally convenient method. It is also the strength of the proposed approach that the proposed method allows a wide variety of distributions for the random effects in the diffusion coefficient.

math.ST↗

arXiv2025

Clustering-based aggregate value regression

In various practical situations, forecasting of aggregate values rather than individual ones is often our main focus. For instance, electricity companies are interested in forecasting the total electricity demand in a specific region to ensure reliable grid operation and resource allocation. However, to our knowledge, statistical learning specifically for forecasting aggregate values has not yet been well-established. In particular, the relationship between forecast error and the number of clusters has not been well studied, as clustering is usually treated as unsupervised learning. This study introduces a novel forecasting method specifically focused on the aggregate values in the linear regression model. We call it the Aggregate Value Regression (AVR), and it is constructed by combining all regression models into a single model. With the AVR, we must estimate a huge number of parameters when the number of regression models to be combined is large, resulting in overparameterization. To address the overparameterization issue, we introduce a hierarchical clustering technique, referred to as AVR-C (C stands for clustering). In this approach, several clusters of regression models are constructed, and the AVR is performed within each cluster. The AVR-C introduces a novel bias-variance trade-off theory under the assumption of a misspecified model. In this framework, the number of clusters characterizes model complexity. Monte Carlo simulation is conducted to investigate the behavior of training and test errors of our proposed clustering technique. The bias-variance trade-off theory is also demonstrated through the analysis of electricity demand forecasting.

stat.ME↗

arXiv2025

Efficient estimation of stable Levy process with symmetric jumps

Efficient estimation of a non-Gaussian stable Levy process with drift and symmetric jumps observed at high frequency is considered. For this statistical experiment, the local asymptotic normality of the likelihood is proved with a non-singular Fisher information matrix through the use of a non-diagonal norming matrix. The asymptotic normality and efficiency of a sequence of roots of the associated likelihood equation are shown as well. Moreover, we show that a simple preliminary method of moments can be used as an initial estimator of a scoring procedure, thereby conveniently enabling us to bypass numerically demanding likelihood optimization. Our simulation results show that the one-step estimator can exhibit quite similar finite-sample performance as the maximum likelihood estimator.

math.ST↗

arXiv2025

On estimation of skewed stable linear regression

We study the parameter estimation method for linear regression models with possibly skewed stable distributed errors. Our estimation procedure consists of two stages: first, for the regression coefficients, the Cauchy quasi-maximum likelihood estimator (CQMLE) is considered after taking the differences to remove the skewness of noise, and we prove its asymptotic normality and tail-probability estimate; second, as for stable-distribution parameters, we consider the moment estimators based on the symmetrized and centered residuals and prove their $\sqrt{n}$-consistency. To derive the $\sqrt{n}$-consistency, we essentially used the tail-probability estimate of the CQMLE. The proposed estimation procedure has a very low computational load and is much less time-consuming compared with the maximum-likelihood estimator. Further, our estimator can be effectively used as an initial value of the numerical optimization of the log-likelihood.

math.ST↗

arXiv2025

Statistical inference for ergodic diffusion with Markovian switching

This study explores a Gaussian quasi-likelihood approach for estimating parameters of diffusion processes with Markovian regime switching. Assuming the ergodicity under high-frequency sampling, we will show the asymptotic normality of the unknown parameters contained in the drift and diffusion coefficients and present a consistent explicit estimator for the generator of the Markov chain. Simulation experiments are conducted to illustrate the theoretical results obtained.

math.ST↗

arXiv2025

Adaptive Ridge Approach to Heteroscedastic Regression

We propose an adaptive ridge (AR) estimation scheme for a heteroscedastic linear regression model with log-linear noise in data. We simultaneously estimate the mean and variance parameters, demonstrating new asymptotic distributional and tightness properties in a sparse setting. We also show that estimates for zero parameters shrink with more iterations under suitable assumptions for tuning parameters. Aspects of application and possible generalizations are presented through simulations and real data examples.

math.ST↗

arXiv2024

Quasi-likelihood-based EM algorithm for regime-switching SDE

This paper considers estimating the parameters in a regime-switching stochastic differential equation(SDE) driven by Normal Inverse Gaussian(NIG) noise. The model under consideration incorporates a continuous-time finite state Markov chain to capture regime changes, enabling a more realistic representation of evolving market conditions or environmental factors. Although the continuous dynamics are typically observable, the hidden nature of the Markov chain introduces significant complexity, rendering standard likelihood-based methods less effective. To address these challenges, we propose an estimation algorithm designed for discrete, high-frequency observations, even when the Markov chain is not directly observed. Our approach integrates the Expectation-Maximization (EM) algorithm, which iteratively refines parameter estimates in the presence of latent variables, with a quasi-likelihood method adapted to NIG noise. Notably, this method can simultaneously estimate parameters within both the SDE coefficients and the driving noise. Simulation results are provided to evaluate the performance of the algorithm. These experiments demonstrate that the proposed method provides reasonable estimation under challenging conditions.

stat.CO↗

arXiv2024

Gaussian quasi-likelihood analysis for non-Gaussian linear mixed-effects model with system noise

We consider statistical inference for a class of mixed-effects models with system noise described by a non-Gaussian integrated Ornstein-Uhlenbeck process. Under the asymptotics where the number of individuals goes to infinity with possibly unbalanced sampling frequency across individuals, we prove some theoretical properties of the Gaussian quasi-likelihood function, followed by the asymptotic normality and the tail-probability estimate of the associated estimator. In addition to the joint inference, we propose and investigate the three-stage inference strategy, revealing that they are first-order equivalent while quantitatively different in the second-order terms. Numerical experiments are given to illustrate the theoretical results.

math.ST↗

arXiv2024

Quasi-Likelihood Analysis for Student-Lévy Regression

We consider the quasi-likelihood analysis for a linear regression model driven by a Student-t Lévy process with constant scale and arbitrary degrees of freedom. The model is observed at high frequency over an extending period, under which we can quantify how the sampling frequency affects estimation accuracy. In that setting, joint estimation of trend, scale, and degrees of freedom is a non-trivial problem. The bottleneck is that the Student-t distribution is not closed under convolution, making it difficult to estimate all the parameters fully based on the high-frequency time scale. To efficiently deal with the intricate nature from both theoretical and computational points of view, we propose a two-step quasi-likelihood analysis: first, we make use of the Cauchy quasi-likelihood for estimating the regression-coefficient vector and the scale parameter; then, we construct the sequence of the unit-period cumulative residuals to estimate the remaining degrees of freedom. In particular, using full data in the first step causes a problem stemming from the small-time Cauchy approximation, showing the need for data thinning.

math.ST↗

arXiv2024

Student t-Lévy regression model in YUIMA

The aim of this paper is to discuss an estimation and a simulation method in the \textsf{R} package YUIMA for a linear regression model driven by a Student-$t$ Lévy process with constant scale and arbitrary degrees of freedom. This process finds applications in several fields, for example finance, physic, biology, etc. The model presents two main issues. The first is related to the simulation of a sample path at high-frequency level. Indeed, only the $t$-Lévy increments defined on an unitary time interval are Student-$t$ distributed. In YUIMA, we solve this problem by means of the inverse Fourier transform for simulating the increments of a Student-$t$ Lévy defined on a interval with any length. A second problem is due to the fact that joint estimation of trend, scale, and degrees of freedom does not seem to have been investigated as yet. In YUIMA, we develop a two-step estimation procedure that efficiently deals with this issue. Numerical examples are given in order to explain methods and classes used in the YUIMA package.

stat.CO↗

arXiv2023

On local likelihood asymptotics for Gaussian mixed-effects model with system noise

The Gaussian mixed-effects model driven by a stationary integrated Ornstein-Uhlenbeck process has been used for analyzing longitudinal data having an explicit and simple serial-correlation structure in each individual. However, the theoretical aspect of its asymptotic inference is yet to be elucidated. We prove the local asymptotics for the associated log-likelihood function, which in particular guarantees the asymptotic optimality of the suitably chosen maximum-likelihood estimator. We illustrate the obtained asymptotic normality result through some simulations for both balanced and unbalanced datasets.

math.ST↗

arXiv2023

Gaussian quasi-information criteria for ergodic Lévy driven SDE

We consider relative model comparison for the parametric coefficients of a semiparametric ergodic Lévy driven model observed at high-frequency. Our asymptotics is based on the fully explicit two-stage Gaussian quasi-likelihood function (GQLF) of the Euler-approximation type. For selections of the scale and drift coefficients, we propose explicit Gaussian quasi-AIC (GQAIC) and Gaussian quasi-BIC (GQBIC) statistics through the stepwise inference procedure. In particular, we show that the mixed-rates structure of the joint GQLF, which does not emerge for the case of diffusions, gives rise to the non-standard forms of the regularization terms in the selection of the scale coefficient, quantitatively clarifying the relation between estimation precision and sampling frequency. Numerical experiments are given to illustrate our theoretical findings.

math.ST↗

arXiv2023

Mixed-effects location-scale model based on generalized hyperbolic distribution

Motivated by better modeling of intra-individual variability in longitudinal data, we propose a class of location-scale mixed effects models, in which the data of each individual is modeled by a parameter-varying generalized hyperbolic distribution. We first study the local maximum-likelihood asymptotics and reveal the instability in the numerical optimization of the log-likelihood. Then, we construct an asymptotically efficient estimator based on the Newton-Raphson method based on the original log-likelihood function with the initial estimator being naive least-squares-type. Numerical experiments are conducted to show that the proposed one-step estimator is not only theoretically efficient but also numerically much more stable and much less time-consuming compared with the maximum-likelihood estimator.

math.ST↗