It is now widely recognized that the statistical property of long memory may be due to reasons other than the data generating process being fractionally integrated. We propose a new procedure aimed at distinguishing b...It is now widely recognized that the statistical property of long memory may be due to reasons other than the data generating process being fractionally integrated. We propose a new procedure aimed at distinguishing between a null hypothesis of unifractal fractionally integrated processes and an alternative hypothesis of other processes which display the long memory property. The procedure is based on a pair of empirical, but consistently defined, statistics namely the number of breaks reported by Atheoretical Regression Trees (ART) and the range of the Empirical Fluctuation Process (EFP) in the CUSUM test. The new procedure establishes through simulation the bivariate distribution of the number of breaks reported by ART with the CUSUM range for simulated fractionally integrated series. This bivariate distribution is then used to empirically construct a test which rejects the null hypothesis for a candidate series if its pair of statistics lies on the periphery of the bivariate distribution determined from simulation under the null. We apply these methods to the realized volatility series of 16 stocks in the Dow Jones Industrial Average and show that the rejection rate of the null is higher than if either statistic was used as a univariate test.展开更多
Single-pixel imaging(SPI)faces significant challenges in reconstructing high-quality images under complex real-world degradation conditions.This paper presents an innovative degradation model for the physical processe...Single-pixel imaging(SPI)faces significant challenges in reconstructing high-quality images under complex real-world degradation conditions.This paper presents an innovative degradation model for the physical processes in SPI,providing the first comprehensive and quantitative analysis of various SPI noise sources encountered in real-world applications.Especially,pattern-dependent global noise propagation and object jitter modelling methods for SPI are proposed.Subsequently,a deep-blind neural network is developed to remove the necessity of obtaining parameters of all the degradation factors in real-world image compensation.Our method can operate without degradation parameters and significantly improve the resolution and fidelity of SPI image reconstruction.The deep-blind network training is guided by the proposed comprehensive SPI degradation model that describes real-world SPI impairments,enabling the network to generalize across a wide range of degradation combinations.The experiment validates its advanced performance in real-world SPI imaging at ultra-low sampling rates.The proposed method holds great potential for applications in remote sensing,biomedical imaging,and privacy-preserving surveillance.展开更多
文摘It is now widely recognized that the statistical property of long memory may be due to reasons other than the data generating process being fractionally integrated. We propose a new procedure aimed at distinguishing between a null hypothesis of unifractal fractionally integrated processes and an alternative hypothesis of other processes which display the long memory property. The procedure is based on a pair of empirical, but consistently defined, statistics namely the number of breaks reported by Atheoretical Regression Trees (ART) and the range of the Empirical Fluctuation Process (EFP) in the CUSUM test. The new procedure establishes through simulation the bivariate distribution of the number of breaks reported by ART with the CUSUM range for simulated fractionally integrated series. This bivariate distribution is then used to empirically construct a test which rejects the null hypothesis for a candidate series if its pair of statistics lies on the periphery of the bivariate distribution determined from simulation under the null. We apply these methods to the realized volatility series of 16 stocks in the Dow Jones Industrial Average and show that the rejection rate of the null is higher than if either statistic was used as a univariate test.
基金National Natural Science Foundation of China(62305184)Science,Technology and Innovation Commission of Shenzhen Municipality(JCYJ20241202123919027)+1 种基金Science,Technology and Innovation Commission of Shenzhen Municipality(WDZC20220818100259004)Basic and Applied Basic Research Foundation of Guangdong Province(2023A1515012932).
文摘Single-pixel imaging(SPI)faces significant challenges in reconstructing high-quality images under complex real-world degradation conditions.This paper presents an innovative degradation model for the physical processes in SPI,providing the first comprehensive and quantitative analysis of various SPI noise sources encountered in real-world applications.Especially,pattern-dependent global noise propagation and object jitter modelling methods for SPI are proposed.Subsequently,a deep-blind neural network is developed to remove the necessity of obtaining parameters of all the degradation factors in real-world image compensation.Our method can operate without degradation parameters and significantly improve the resolution and fidelity of SPI image reconstruction.The deep-blind network training is guided by the proposed comprehensive SPI degradation model that describes real-world SPI impairments,enabling the network to generalize across a wide range of degradation combinations.The experiment validates its advanced performance in real-world SPI imaging at ultra-low sampling rates.The proposed method holds great potential for applications in remote sensing,biomedical imaging,and privacy-preserving surveillance.
