Sequential Nonparametric Algorithm for Detecting Time Series Breakdown

Chervova, Almira; Filaretov, Gennadiy F.; Zineddine, Bouchaala

doi:10.1134/s0005117921120110

Cited by 2 publications

(4 citation statements)

References 3 publications

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“…Thus, adaptive minimax estimators over the Sobolev ellipsoids have been developed, which are asymptotically minimax on Θ(β, R) for every β and R simultaneously. Among them, here we focus on the blockwise James-Stein estimator [15,42].…”

Section: Blockwise James-stein Estimatormentioning

confidence: 99%

“…The blockwise James-Stein estimator with the weakly geometric blocks attains adaptive minimaxity as follows [15,42].…”

Section: Blockwise James-stein Estimatormentioning

confidence: 99%

“…Consider estimation of θ = (θ i ) in the multivariate Gaussian sequence model (14) under the quadratic loss L Q in (15). The risk function of an estimator θ is…”

Section: Asymptotically Minimax Estimator Onmentioning

confidence: 99%

“…Thus, estimators that are adaptive minimax over the Sobolev ellipsoids have been developed. Among them, the blockwise Jame-Stein estimator [15] partitions the observations into blocks and apply the James-Stein shrinkage to each block. Adaptive minimaxity of this estimator is proved by using an oracle inequality for the James-Stein estimator, which indicates that the James-Stein estimator attains almost the same risk with the best linear estimator.…”

Section: Introductionmentioning

confidence: 99%

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Adapting to General Quadratic Loss via Singular Value Shrinkage

Matsuda

2024

IEEE Trans. Inform. Theory

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The Gaussian sequence model is a canonical model in nonparametric estimation. In this study, we introduce a multivariate version of the Gaussian sequence model and investigate adaptive estimation over the multivariate Sobolev ellipsoids, where adaptation is not only to unknown smoothness but also to arbitrary quadratic loss. First, we derive an oracle inequality for the singular value shrinkage estimator by Efron and Morris, which is a matrix generalization of the James-Stein estimator. Next, we develop an asymptotically minimax estimator on the multivariate Sobolev ellipsoid for each quadratic loss, which can be viewed as a generalization of Pinsker's theorem. Then, we show that the blockwise Efron-Morris estimator is exactly adaptive minimax over the multivariate Sobolev ellipsoids under the corresponding quadratic loss. It attains sharp adaptive estimation of any linear combination of the mean sequences simultaneously.

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Section: Blockwise James-stein Estimatormentioning

confidence: 99%

“…The blockwise James-Stein estimator with the weakly geometric blocks attains adaptive minimaxity as follows [15,42].…”

Section: Blockwise James-stein Estimatormentioning

confidence: 99%

“…Consider estimation of θ = (θ i ) in the multivariate Gaussian sequence model (14) under the quadratic loss L Q in (15). The risk function of an estimator θ is…”

Section: Asymptotically Minimax Estimator Onmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Adapting to General Quadratic Loss via Singular Value Shrinkage

Matsuda

2024

IEEE Trans. Inform. Theory

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Electric Vehicle Battery Power Estimation and Remote Monitoring Method Based on Optimization Algorithm

Wang¹,

Ye²,

Xu³

et al. 2022

J. Phys.: Conf. Ser.

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With the leap-forward development of China’s economy and society, people have put forward higher demands for travel convenience. As a daily means of transportation, automobiles have gradually approached thousands of households. Therefore, the automobile industry has also developed rapidly in recent years. The construction of digital factories for automobile manufacturing and production has rapidly promoted the progress of the new energy automobile industry. The main purpose of this paper is to study the battery power estimation and remote monitoring program of electric vehicles based on the optimization algorithm. This paper mainly selects the battery model, compares and analyzes different algorithms, and completes the database design and the construction of the relevant development environment according to the functional requirements and combined with the actual situation. Experiments show that low temperature has a great influence on the discharge capacity of the battery. The battery can release 74% of the rated capacity at -5°C, while the battery can release only 56% of the rated capacity at -20°C.

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Sequential Nonparametric Algorithm for Detecting Time Series Breakdown

Cited by 2 publications

References 3 publications

Adapting to General Quadratic Loss via Singular Value Shrinkage

Adapting to General Quadratic Loss via Singular Value Shrinkage

Electric Vehicle Battery Power Estimation and Remote Monitoring Method Based on Optimization Algorithm

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