Hiroki Masuda | Last modified date：2019.06.20 |

Graduate School

Homepage

##### http://www2.math.kyushu-u.ac.jp/~hiroki/hmhp.html

Presonal homepage .

Academic Degree

PhD. (Mathematical Sciences)

Country of degree conferring institution (Overseas)

No

Field of Specialization

Mathematical statistics, stochastic process model

ORCID(Open Researcher and Contributor ID)

0000-0002-5553-9201

Total Priod of education and research career in the foreign country

00years00months

Outline Activities

My researches concern asymptotic statistical inference for stochastic processes, especially, of Levy driven models with/without jumps. I have interest in: asymptotic distributional theory of various statistics; higher order approximation of statistical functionals; and development of how to compute complicated conditional expectations not admitting explicit analytical expressions. Also examined is derivation of the mixing property and its rate of stochastic processes, which is essential when considering ergodic models. Also, I am interested in application of the local non-Gaussian stable approximation to statistics. Also investigated is to implement the methodologies thus obtained in the statistical software, and further to apply them to several areas such as sciences and industry.

Research

**Research Interests**

- Development of theory of statistical inference for stochastic processes, and its implementation

keyword : Asymptotic statistics, Stochastic process, Large and high-frequency dependent data analysis

2000.04Statistical inference for stochastic processes, distributional theory for statistical functionals.

**Academic Activities**

**Reports**

**Papers**

**Presentations**

1. | Hiroki Masuda, Locally stable regression with unknown activity index, CMStatistics 2018, 2018.12, Typically, transition of large-scale dependent data, such as those sampled at ultra high-frequency, are highly non-Gaussian. One of natural ways of modeling such data would be to use continuous-time stochastic processes driven by a non-Gaussian pure-jump noise. The related existing literature is, however, still far from being well-developed. In this talk, we present tailor-made quasi-likelihood inference results that can efficiently handle such locally and highly non-Gaussian statistical models with the activity index of the driving noise process being unknown. The model setup includes not only Markovian stochastic differential equations but also a class of semimartingale regression models. Of primary interest are cases where estimation target includes not only the rapidly varying scale structure but also the slowly varying trend one.. |

2. | Alexandre Brouste, Hiroki Masuda, Efficient estimation of stable Lévy process, ASC2018, Asymptotic Statistics and Computations, 2018.02. |

3. | Hiroki Masuda, Local limit theorem in non-Gaussian quasi-likelihood inference, Asymptotic Statistics of Stochastic Processes and Applications XI, 2017.07, We consider parameter estimation of the finite-dimensional parameter in the stochastic differential equation (SDE) model driven by a highly non-Gaussian noise. We will present handy sufficient conditions for the L1-local limit theorem with convergence rate, which is the key assumption for the asymptotic mixed normality. The sufficient conditions are given only in terms of the driving Levy measure and/or the characteristic exponent of the driving noise. Specific examples satisfying them include stable, exponentially tempered $¥beta$-stable, and generalized hyperbolic Levy processes.. |

4. | Hiroki Masuda, Stable quasi-likelihood regression, EcoSta 2017, 2017.06. |

5. | Hiroki Masuda, Shoichi Eguchi, Yuma Uehara, Lévy SDE inference in Yuima package, Dynstoch meeting 2017, 2017.04. |

6. | Hiroki Masuda, Locally stable regression without ergodicity and finite moments, Hokkaido International Symposium "Recent Developments of Statistical Theory in Statistical Science", 2016.10. |

7. | Hiroki Masuda, On Asymptotics of multivariate non-Gaussian quasi-likelihood, World Congress in Probability and Statistics, 2016.07, We consider (semi-)parametric inference for a class of stochastic differential equation (SDE) driven by a locally stable Levy process, focusing on multivariate setting and some computational aspects. The process is supposed to be observed at high frequency over a fixed time domain. This setting naturally gives rise to a theoretically fascinating quasi-likelihood which brings about a novel unified estimation strategy for targeting a broad spectrum of driving Levy processes. The limit experiment is mixed normal with a clean-cut random information structure, based on which it is straightforward to make several conventional asymptotic statistical decisions. The infill-asymptotics adopted here makes the popular Gaussian quasi-likelihood useless, while instead enabling us not only to incorporate any exogenous and/or observable endogenous data into the trend and/or scale coefficients without essential difficulty, but also to sidestep most crucial assumptions on the long-term stability such as ergodicity and moment boundedness. The proposed quasi-likelihood estimator is asymptotically efficient in some special cases.. |

8. | Hiroki Masuda, On Asymptotics of multivariate non-Gaussian quasi-likelihood, The 4th Institute of Mathematical Statistics Asia Pacific Rim Meeting, 2016.06. |

9. | Hiroki Masuda, Lévy in quasi-likelihood estimation of SDE, Statistics for Stochastic Processes and Analysis of High Frequency Data V, 2016.03, We try to give a clear whole picture about the local stable approximation in estimating a L\'{e}vy driven SDE under infill asymptotics without ergodicity. Our finding here is that the completely analogous strategy as in the local Gauss approximation in estimating a diffusion does a good job, when the activity degree is equal to or greater than 1 (the Cauchy-like case). The proposed estimator is indeed asymptotically efficient in some instances.. |

10. | Hiroki Masuda, Computational aspects of estimating Lévy driven models, The 9th IASC-ARS conference, 2015.12, We consider estimation problem concerning stochastic differential equations driven by a Levy process with jumps. The model is supposed to be observed at high-frequency, allowing us to incorporate a small-time approximation of the underlying likelihood. An overview of some existing theories based on the Gaussian and non-Gaussian quasi-likelihoods is presented, together with their computational aspects. Also to be demonstrated is how to implement the theory in the YUIMA package: an R framework for simulation and inference of stochastic differential equations.. |

11. | Hiroki Masuda, On variants of stable quasi-likelihood for Levy driven SDE, Statistique Asymptotique des Processus Stochastiques X, 2015.03. |

12. | Hiroki Masuda, On sampling problem for pure-jump SDE , 3rd APRM, Taipei, 2014.07. |

13. | Hiroki Masuda, LAD-based estimation of locally stable Ornstein-Uhlenbeck processes, Waseda International Symposium on "Stable Process, Semimartingale, Finance & Pension Mathematics", 2014.03, [URL], The LAD type estimator for discretely observed Levy driven OU process is much more efficient than the LSE type one. We prove that the proposed estimator under a random norming is asymptotically standard-normally distributed, making construction of confidence intervals easy.. |

14. | Hiroki Masuda, Stable quasi-likelihood: Methodology and computational aspects, ERCIM 2013 London, 2013.12, [URL], We consider the semi-parametric model described by the parametric locally stable pure-jump stochastic differential equation. We wish to estimate the parametric coefficients based on a high-frequency sample over a fixed interval. In this talk, we introduce a novel, tailor-made estimator based on the stable approximation of the one-step transition distribution. Under suitable regularity conditions, it is shown that the proposed estimator is asymptotically mixed-normal. The result reveals that, in case of the stable-like driving Levy process, the proposed estimator is much more efficient than the conventional Gaussian quasi-maximum likelihood estimator, which requires the large-time asymptotics and leads to a slower rates of convergence. Nevertheless, evaluation of the proposed estimator is computationally more involved compared with the Gaussian case. Also discussed in some detail is the computational aspects of the proposed methodology. . |

15. | Osaka University, [URL]. |

16. | Hiroki Masuda, On statistical inference for Levy-driven models, The 59th World Statistics Congress (WSC), 2013.08, [URL], 保険数理分野では局所安定型確率微分方程式によるモデリングが有用である．モデルを適合させる対象期間を固定しつつ統計的分布論の理論基盤を確保できるという点において，ノイズの非正規性が如実に現れる当該分野での推測問題に新たな視点・展開を与えた．. |

17. | Hiroki Masuda, Estimation of stable-like stochastic differential equations, 29th European Meeting of Statisticians, 2013.07, [URL], We consider the stochastic differential equation of pure-jumps type with parametric coefficients. We wish to estimate the unknown parameters based on a discrete-time but high-frequency sample. A naive way would be to use the Gaussian quasi likelihood. However, although the Gaussian quasi likelihood is known to be well-suited for the case of diffusions, it leads to asymptotically suboptimal estimator in the pure-jump case; in particular, the Gaussian quasi-maximum likelihood estimation inevitably needs a large-time asymptotics. In this talk, we will introduce another kind of quasi-maximum likelihood estimator based on the local-stable approximation of the one-step transition distribution; the proposed estimation procedure is a pure-jump counterpart to the Gaussian quasi-maximum likelihood estimation. Under some regularity conditions, we will show the asymptotic mixed normality of the proposed estimator, revealing that the proposed estimator is asymptotically much more efficient than the Gaussian quasi-maximum likelihood estimator.. |

18. | Hiroki Masuda, On optimal estimation of stable Ornstein-Uhlenbeck processes, Dynstoch meeting 2013, 2013.04, [URL], Ornstein-Uhlenbeck (OU) processes driven by a Levy process form a particular tractable class of Markovian stochastic differential equations with jumps. Among them, the non-Gaussian stable driven ones, the study of which dates back to Doob's work in 1942, are known to have a pretty inherent character. Especially, a special property of stable integrals allows us to exactly generate the discrete-time sample from the process, and more importantly, to study in a transparent way the likelihood ratio associated with discrete-time sampling. We are concerned with optimal estimation of the stable OU processes observed at high-frequency. We clarify that, due to the infinite-variance character of the model, the likelihood ratio exhibits entirely different asymptotic behaviors according to whether or not the terminal sampling time tends to infinity. When the terminal time is a fixed time, we present the LAMN (Local Asymptotic Mixed Normality) structure of the statistical model, entailing the notion of asymptotic efficiency of a regular estimator. Also presented is how to construct some simple rate-efficient estimators having asymptotic mixed normality, together with numerical experiments.. |

19. | Hiroki Masuda, Non-Gaussian quasi-likelihoods for estimating jump SDE, 8th World Congress in Probability and Statistics, 2012.07, We consider a stochastic differential equation driven by a stable-like Levy process, which is observed at high frequency. In this talk, we will introduce a quasi-maximum likelihood estimator based on the local-stable approximation of the transition laws. This is a pure-jump counterpart to the local-Gauss contrast function, well-suited for the case of diffusions. Under some regularity conditions, we will present asymptotic distribution results, which is entirely different from the Gaussian quasi-likelihood case and much more efficient. In particular, the rate of convergence of the estimator obtained is much better and they are jointly asymptotically normal and mixed-normal according as the terminal sampling tends to infinity or not. . |

20. | Hiroki Masuda, Non-Gaussian quasi likelihood in estimating jump SDE, 2nd Asian Pacific Rim Meeting, 2012.07, 非正規安定レヴィ過程で微小時間近似できる確率微分方程式モデルの推定問題を考察した．当該モデルでは従来の正規型擬似最尤推定は効率が悪いことが知られており，新たな推定手法が要求される．筆者は，データ増分の非正規安定近似を介した新しい擬似尤度推定法を考案し，その漸近挙動を導出した．特に，ドリフト推定量の有界時間区間上での漸近混合正規性，および推定量の収束率の改善など，正規型では決して得られない（好ましい）現象が明らかとなった．. |

21. | Hiroki Masuda, Local-stable contrast function, Dynstoch meeting 2012, 2012.06, We consider a stochastic differential equation driven by a stable-like Levy process, which is observed at high frequency. In this talk, we will introduce a quasi-maximum likelihood estimator based on the local-stable approximation of the transition laws. This is a pure-jump counterpart to the local-Gauss contrast function, well-suited for the case of diffusions. Under some regularity conditions, we will present asymptotic distribution results, which is entirely different from the Gaussian quasi-likelihood case and much more efficient. In particular, the rate of convergence of the estimator obtained is much better and they are jointly asymptotically normal and mixed-normal according as the terminal sampling tends to infinity or not. . |

**Awards**

- Studies in statistical inference for stochastic processes with jumps and their implementation
- Simple estimators for parametric Markovian trend of ergodic processes based on sampled data

Educational

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