The University of Tokyo · 수학
Fumiya Akashi 교수의 연구실은 체계적이고 강력한 통계적 방법을 활용해 중도 및 비정상 시계열 데이터, 특히 무한 분산을 가진 무거운 尾 분포를 가진 오차 구조를 가진 모형에 대한 추론 문제를 중심으로 연구를 전개하고 있습니다. 특히, 경험적 우도법, 자기정규화된 서브샘플링, 국소 근사 기반 추정 기법을 활용해 기존 가정에 의존하지 않는 강건한 통계적 분석 기법을 개발하고 있습니다. 연구는 금융, 금융공학, 환경 과학 등 무거운 꼬리 데이터가 풍부한 분야에 응용 가능하며, 비정상성과 장기 의존성에 대한 고려를 통해 현실 세계의 복잡한 데이터를 효과적으로 분석하는 데 기여하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
In this paper we consider the problem of detecting a change in the parameters of an autoregressive process where the moments of the innovation process do not necessarily exist. An empirical likelihood ratio test for the existence of a change point is proposed and its asymptotic properties are studied. In contrast to other works on change‐point tests using empirical likelihood, we do not assume knowledge of the location of the change point. In particular, we prove that the maximizer of the empiri
Empirical likelihood approach is one of non-parametric statistical methods, which is applied to the hypothesis testing or construction of confidence regions for pivotal unknown quantities. This method has been applied to the case of independent identically distributed random variables and second order stationary processes. In recent years, we observe heavy-tailed data in many fields. To model such data suitably, we consider symmetric scalar and multivariate $\alpha$-stable linear processes gener
This article extends the self‐normalized subsampling method of Bai et al. (2016) to the M‐estimation of linear regression models, where the covariate and the noise are stationary time series which may have long‐range dependence or heavy tails. The method yields an asymptotic confidence region for the unknown coefficients of the linear regression. The determination of these regions does not involve unknown parameters such as the intensity of the dependence or the heaviness of the distributional t
We consider the problem of inference for non-stationary time series with heavy-tailed error distribution. Under a time-varying linear process framework we show that there exists a suitable local approximation by a stationary process with heavy-tails. This enable us to introduce a local approximation-based estimator which estimates consistently time-varying parameters of the model at hand. To develop a robust method, we also suggest a self-weighing scheme which is shown to recover the asymptotic
In this paper we consider the problem of detecting a change in the parameters of an autoregressive process, where the moments of the innovation process do not necessarily exist. An empirical likelihood ratio test for the existence of a change point is proposed and its asymptotic properties are studied. In contrast to other work on change point tests using empirical likelihood, we do not assume knowledge of the location of the change point. In particular, we prove that the maximizer of the empiri
