Dear Editor,This letter deals with state estimation issues of discrete-time nonlinear systems subject to denial-of-service(DoS)attacks under the try-once-discard(TOD)protocol.More specifically,to reduce the communicat...Dear Editor,This letter deals with state estimation issues of discrete-time nonlinear systems subject to denial-of-service(DoS)attacks under the try-once-discard(TOD)protocol.More specifically,to reduce the communication burden,a TOD protocol with novel update rules on protocol weights is designed for scheduling measurement outputs.In addition,unknown nonlinear functions vulnerable to DoS attacks are considered due to the openness and vulnerability of the network.展开更多
本文在Stein恒等式(Stein’s identity)的框架下,给出了一种适用于有限样本场合的全新的修正Akaike信息准则(corrected Akaike information criterion),所提出的新准则适用于非常一般的协方差结构.在一定的正则性条件下,本文建立了所提...本文在Stein恒等式(Stein’s identity)的框架下,给出了一种适用于有限样本场合的全新的修正Akaike信息准则(corrected Akaike information criterion),所提出的新准则适用于非常一般的协方差结构.在一定的正则性条件下,本文建立了所提出准则的渐近有效性.应用带有自回归误差的空间回归模型进行模拟,结果表明,在备选模型与真实的数据生成过程之间的差异较小时,本文所提出方法的表现是令人满意的.当这种差异变大时,本文所提出的方法与其他已有方法相比也非常有竞争力.所提出的方法也被用于一组实际数据(社区犯罪数据)的分析中,所得到的结果更进一步支持了我们的方法在实际数据分析中的应用.展开更多
A rapidly growing body of literature has documented improvements in forecasting financial return volatility measurement using various heterogeneous autoregression(HAR)type models.Most HAR-type models use a fixed lag i...A rapidly growing body of literature has documented improvements in forecasting financial return volatility measurement using various heterogeneous autoregression(HAR)type models.Most HAR-type models use a fixed lag index of(1,5,22)to mirror the daily,weekly,and monthly components of the volatility process,but they ignore model specification uncertainty.In this paper,we propose applying the least squares model averaging approach to HAR-type models with signed realized semivariance to account for model uncertainty and to allow for a more flexible lag structure.We denote this approach as MARS and prove that the MARS estimator is asymptotically optimal in the sense of achieving the lowest possible mean squared forecast error.Selected by the data-driven model averaging method,the lag combination in the MARS method changes with various data series and different forecast horizons.Employing high frequency data from the NASDAQ 100 index and its 104 constituents,our empirical results demonstrate that acknowledging model uncertainty under the HAR framework and solving with the model averaging method can significantly improve the accuracy of financial return volatility forecasting.展开更多
This paper gives a theoretical analysis for the algorithms to compute functional decomposition for multivariate polynomials based on differentiation and homogenization which were proposed by Ye, Dai, and Lam （1999） ...This paper gives a theoretical analysis for the algorithms to compute functional decomposition for multivariate polynomials based on differentiation and homogenization which were proposed by Ye, Dai, and Lam （1999） and were developed by Faugere, Perret （2006, 2008, 2009）. The authors show that a degree proper functional decomposition for a set of randomly decomposable quartic homoge- nous polynomials can be computed using the algorithm with high probability. This solves a conjecture proposed by Ye, Dal, and Lam （1999）. The authors also propose a conjecture which asserts that the decomposition for a set of polynomials can be computed from that of its homogenization and show that the conjecture is valid with high probability for quartic polynomials. Finally, the authors prove that the right decomposition factors for a set of polynomials can be computed from its right decomposition factor space.展开更多
The multimodel inference makes statistical inferences from a set of plausible models rather than from a single model.In this paper,we focus on the multimodel inference based on smoothed information criteria proposed b...The multimodel inference makes statistical inferences from a set of plausible models rather than from a single model.In this paper,we focus on the multimodel inference based on smoothed information criteria proposed by seminal monographs(see Buckland et al.(1997)and Burnham and Anderson(2003)),which are termed as smoothed Akaike information criterion(SAIC)and smoothed Bayesian information criterion(SBIC)methods.Due to their simplicity and applicability,these methods are very widely used in many fields.By using an illustrative example and deriving limiting properties for the weights in the linear regression,we find that the existing variance estimation for SAIC is not applicable because of a restrictive condition,but for SBIC it is applicable.Especially,we propose a simulation-based inference for SAIC based on the limiting properties.Both the simulation study and the real data example show the promising performance of the proposed simulationbased inference.展开更多
基金supported in part by the Shandong Provincial Natural Science Foundation(ZR2021QF057)Taishan Scholars Program(tsqn202211203)+3 种基金Shandong Provincial Higher Education Youth Innovation Team Development Project(2022KJ 290)“20 New Universities”Project of Jinan City(202228077)QLU/SDAS Computer Science and Technology Fundamental Research Enhancement Program(2021JC02023)QLU/SDAS Pilot Project for Integrated Innovation of Science,Education,and Industry(2022JBZ01-01).
文摘Dear Editor,This letter deals with state estimation issues of discrete-time nonlinear systems subject to denial-of-service(DoS)attacks under the try-once-discard(TOD)protocol.More specifically,to reduce the communication burden,a TOD protocol with novel update rules on protocol weights is designed for scheduling measurement outputs.In addition,unknown nonlinear functions vulnerable to DoS attacks are considered due to the openness and vulnerability of the network.
文摘本文在Stein恒等式(Stein’s identity)的框架下,给出了一种适用于有限样本场合的全新的修正Akaike信息准则(corrected Akaike information criterion),所提出的新准则适用于非常一般的协方差结构.在一定的正则性条件下,本文建立了所提出准则的渐近有效性.应用带有自回归误差的空间回归模型进行模拟,结果表明,在备选模型与真实的数据生成过程之间的差异较小时,本文所提出方法的表现是令人满意的.当这种差异变大时,本文所提出的方法与其他已有方法相比也非常有竞争力.所提出的方法也被用于一组实际数据(社区犯罪数据)的分析中,所得到的结果更进一步支持了我们的方法在实际数据分析中的应用.
基金supported by the National Natural Science Foundation of China(71701175,71522004,11471324,71631008,and 71642006)the Ministry of Education of the People's Republic of China Project of Humanities and Social Sciences(17YJC790174 and 17YJC910011)+2 种基金the Natural Science Foundation of Fujian Province of China(2018J01116)the Fundamental Research Funds for the Central Universities China(20720171002,20720171076,20720181050,and 20720181004)the Educational and Scientific Research Program for Young and Middle-aged Instructors of Fujian Province(JAS170018).
文摘A rapidly growing body of literature has documented improvements in forecasting financial return volatility measurement using various heterogeneous autoregression(HAR)type models.Most HAR-type models use a fixed lag index of(1,5,22)to mirror the daily,weekly,and monthly components of the volatility process,but they ignore model specification uncertainty.In this paper,we propose applying the least squares model averaging approach to HAR-type models with signed realized semivariance to account for model uncertainty and to allow for a more flexible lag structure.We denote this approach as MARS and prove that the MARS estimator is asymptotically optimal in the sense of achieving the lowest possible mean squared forecast error.Selected by the data-driven model averaging method,the lag combination in the MARS method changes with various data series and different forecast horizons.Employing high frequency data from the NASDAQ 100 index and its 104 constituents,our empirical results demonstrate that acknowledging model uncertainty under the HAR framework and solving with the model averaging method can significantly improve the accuracy of financial return volatility forecasting.
基金partially supported by a National Key Basic Research Project of China under Grant No. 2011CB302400by a Grant from NSFC with Nos 60821002 and 10901156
文摘This paper gives a theoretical analysis for the algorithms to compute functional decomposition for multivariate polynomials based on differentiation and homogenization which were proposed by Ye, Dai, and Lam （1999） and were developed by Faugere, Perret （2006, 2008, 2009）. The authors show that a degree proper functional decomposition for a set of randomly decomposable quartic homoge- nous polynomials can be computed using the algorithm with high probability. This solves a conjecture proposed by Ye, Dal, and Lam （1999）. The authors also propose a conjecture which asserts that the decomposition for a set of polynomials can be computed from that of its homogenization and show that the conjecture is valid with high probability for quartic polynomials. Finally, the authors prove that the right decomposition factors for a set of polynomials can be computed from its right decomposition factor space.
基金supported by National Key R&D Program of China(Grant No.2020AAA 0105200)National Natural Science Foundation of China(Grant Nos.12001559,71925007,71988101 and 72042019)+3 种基金Ministry of Education of China(Grant No.17YJC910011)the Youth Innovation Promotion Association of the Chinese Academy of Sciencesthe Beijing Academy of Artificial IntelligenceAcademy for Multidisciplinary Studies,Capital Normal University。
文摘The multimodel inference makes statistical inferences from a set of plausible models rather than from a single model.In this paper,we focus on the multimodel inference based on smoothed information criteria proposed by seminal monographs(see Buckland et al.(1997)and Burnham and Anderson(2003)),which are termed as smoothed Akaike information criterion(SAIC)and smoothed Bayesian information criterion(SBIC)methods.Due to their simplicity and applicability,these methods are very widely used in many fields.By using an illustrative example and deriving limiting properties for the weights in the linear regression,we find that the existing variance estimation for SAIC is not applicable because of a restrictive condition,but for SBIC it is applicable.Especially,we propose a simulation-based inference for SAIC based on the limiting properties.Both the simulation study and the real data example show the promising performance of the proposed simulationbased inference.
