Stochastic optimization of a mixed moving average process for controlling non-Markovian streamflow environments

Statistical evaluation of a long‐memory process using the generalized entropic value‐at‐risk

“…Other than the temporal discretization, the full implementation of the gradient descent requires to numerically evaluate the integrals with respect to the reversion measure $$$ \pi $$$ as they will not be available in closed forms. We mainly use the quantile discretization method of Yoshioka et al (2023) to discretize the reversion measure of the gamma type (2) and analyze its convergence computationally. Here, we revise their method for the upper‐TsVaR because it turns out to be inefficient.…”

“…The supOU process is viewed as a large‐population limit of mutually‐independent OU processes whose reversion speeds are generated according to $$$ \pi $$$ (Fasen & Klüppelberg, 2007). Indeed, the large‐population limit is justified in the sense of distributions (Yoshioka et al, 2023).…”

“…Stochastic process models are widely used for the modeling and risk analysis of dynamical systems such as streamflow environments. Many studies have considered streamflow discharge from the viewpoint of jump‐driven stochastic processes at both the local (Wang & Guo, 2019; Yoshioka et al, 2023) and regional scales (Bertassello, Durighetto, et al, 2022; Bertassello, Jawitz, et al, 2022; Durighetto et al, 2022). Accelerated erosion due to changes in land use requires estimation of highly stochastic turbidity dynamics in a streamflow environment as a principal water quality index (Steffy & Shank, 2018).…”

“…Hurst (1956) found the long memory in the data of Nile River. The hourly data of the streamflow discharge in mountainous rivers have a long memory (Yoshioka, 2021; Yoshioka et al, 2023). The long memory of a streamflow discharge has been explained as the presence of watershed runoff processes with multiple time scales, which emerge as a superposition of exponential correlations with different relaxation rates (Mudelsee, 2007; Ranjram & Craig, 2022).…”

Optimal harvesting policy for biological resources with uncertain heterogeneity for application in fisheries management

“…For the model application, we used growth data of P. altivelis that were collected in the Hii River, which is one of the largest rivers in the San-in area, Shimane Prefecture, Japan, with the help of the Hii River Fishery Cooperative (HRFC) which authorizes inland fishery resources in the mid-to-upstream reaches of this river. The authors have been communicating with the HRFC since 2015, and previously investigated the environment and fisheries of the Hii River (e.g., see Yoshioka et al, 2021Yoshioka et al, , 2023, and the references therein). The fish P. altivelis is a major inland fishery resource that contributes to the regional environment, ecosystem, culture, and recreation.…”