This paper proposes two ratio-type statistics to sequentially detect mean and variance change-points in the long-memory time series.The limiting distributions of monitoring statistics under the no change-point null hy...This paper proposes two ratio-type statistics to sequentially detect mean and variance change-points in the long-memory time series.The limiting distributions of monitoring statistics under the no change-point null hypothesis,alternative hypothesis as well as change-point misspecified hypothesis are proved.In particular,a sieve bootstrap approximation method is proposed to determine the critical values.Simulations indicate that the new monitoring procedures have better finite sample performance than the available off-line tests when the change-point nears to the beginning time of monitoring,and can discriminate between mean and variance change-point.Finally,the authors illustrate their procedures via two real data sets:A set of annual volume of discharge data of the Nile river,and a set of monthly temperature data of northern hemisphere.The authors find a new variance change-point in the latter data.展开更多
Multiple change-points estimation for functional time series is studied in this paper.The change-point problem is first transformed into a high-dimensional sparse estimation problem via basis functions.Group least abs...Multiple change-points estimation for functional time series is studied in this paper.The change-point problem is first transformed into a high-dimensional sparse estimation problem via basis functions.Group least absolute shrinkage and selection operator(LASSO)is then applied to estimate the number and the locations of possible change points.However,the group LASSO(GLASSO)always overestimate the true points.To circumvent this problem,a further Information Criterion(IC)is applied to eliminate the redundant estimated points.It is shown that the proposed two-step procedure estimates the number and the locations of the change-points consistently.Simulations and two temperature data examples are also provided to illustrate the finite sample performance of the proposed method.展开更多
风电机组运行过程中,一些故障导致设备状态发生改变,状态的改变发生在一个持续的时间序列中,找到变化点的时间对于故障回溯及根本原因分析具有重要价值。该文研究风电信号及状态时序变化的特点,引入统计学中的Change-Point算法,通过划...风电机组运行过程中,一些故障导致设备状态发生改变,状态的改变发生在一个持续的时间序列中,找到变化点的时间对于故障回溯及根本原因分析具有重要价值。该文研究风电信号及状态时序变化的特点,引入统计学中的Change-Point算法,通过划分不同置信区间求取置信度方法解决奇异变点的不确定度问题。通过实验对算法进行验证,得出以下结论:Change-Point算法能够有效挖掘到历史数据中的一维及二维模型数据的变化,并给出变点;Change-Point算法思想是挖掘出数据本身的规律性,不受其他条件限制,因此可广泛应用于风电机组数据采集与监视控制(supervisory control and data acquisition,SCADA)系统变量数据挖掘中的问题回溯,快速定位SCADA数据状态变化点。展开更多
Electrostatic monitoring technology of particle charging information can facilitate online monitoring of aero-engine,which effectively enhances engine fault diagnosis and health managements.Unlike traditional engine s...Electrostatic monitoring technology of particle charging information can facilitate online monitoring of aero-engine,which effectively enhances engine fault diagnosis and health managements.Unlike traditional engine state monitoring technologies,aircraft engine monitoring by gas path electrostatic monitoring not only covers the predicted information source itself,but also detects the information that can provide an early warnings for initial fault states through gas path charging levels.This paper establishes a non-stationary time sequence change-point model for anomaly recognition of electrostatic signals based on change-point theory combined with difference method of non-stationary time series.Finally,electrostatic induction data were utilized by the engine life test for a particular aircraft to validate the proposed algorithm.The results indicate that the activity level and the event rate were0.5—0.8(nc)and 50%,respectively,which were far greater than 4—12(pc)and 0—4% under normal working conditions of the engine.展开更多
This paper presents software reliability growth models(SRGMs) with change-point based on the stochastic differential equation(SDE).Although SRGMs based on SDE have been developed in a large scale software system,consi...This paper presents software reliability growth models(SRGMs) with change-point based on the stochastic differential equation(SDE).Although SRGMs based on SDE have been developed in a large scale software system,considering the variation of failure distribution in the existing models during testing time is limited.These SDE SRGMs assume that failures have the same distribution.However,in practice,the fault detection rate can be affected by some factors and may be changed at certain point as time proceeds.With respect to this issue,in this paper,SDE SRGMs with changepoint are proposed to precisely reflect the variations of the failure distribution.A real data set is used to evaluate the new models.The experimental results show that the proposed models have a fairly accurate prediction capability.展开更多
A number of statistical tests are proposed for the purpose of change-point detection in a general nonparametric regression model under mild conditions. New proofs are given to prove the weak convergence of the underly...A number of statistical tests are proposed for the purpose of change-point detection in a general nonparametric regression model under mild conditions. New proofs are given to prove the weak convergence of the underlying processes which assume remove the stringent condition of bounded total variation of the regression function and need only second moments. Since many quantities, such as the regression function, the distribution of the covariates and the distribution of the errors, are unspecified, the results are not distribution-free. A weighted bootstrap approach is proposed to approximate the limiting distributions. Results of a simulation study for this paper show good performance for moderate samples sizes.展开更多
Testing-time when a change of a stochastic characteristic of the software failure-occurrence time or software failure-occurrence time-interval is observed is called change-point. It is said that effect of the change-p...Testing-time when a change of a stochastic characteristic of the software failure-occurrence time or software failure-occurrence time-interval is observed is called change-point. It is said that effect of the change-point on the software reliability growth process influences on accuracy for software reliability assessment based on a software reliability growth model (SRGM). We propose an SRGM with the effect of the change-point based on a bivariate SRGM, in which the software reliability growth process is assumed to depend on the testing-time and testing-effort factors simultaneously, for accurate software reliability assessment. And we discuss an optimal software release problem for deriving optimal testing-effort expenditures based on our model. Further, we show numerical examples of software reliability assessment based on our bivariate SRGM and estimation of optimal testing-effort expenditures by using actual data.展开更多
We design monitoring procedures for the common change-point in a sequential panel data model. An asymptotic method and two new bootstrap methods are proposed to obtain critical values. We establish the asymptotic vali...We design monitoring procedures for the common change-point in a sequential panel data model. An asymptotic method and two new bootstrap methods are proposed to obtain critical values. We establish the asymptotic validity of the proposed bootstrap procedures. In simulation studies the empirical test size and the empirical test power values are investigated to show that the three tests are valid and have their own applications.At the same time, the estimations of an unknown change-point are obtained by using the proposed test statistic with these three methods.展开更多
For some products,degradation mechanisms change during testing,and therefore,their degradation patterns vary at different points in time;these points are called change-points.Owing to the limitation of measurement cos...For some products,degradation mechanisms change during testing,and therefore,their degradation patterns vary at different points in time;these points are called change-points.Owing to the limitation of measurement costs,time intervals for degradation measurements are usually very long,and thus,the value of change-points cannot be determined.Conventionally,a certain degradation measurement is selected as the change-point in a two-phase degradation process.According to the tendency of the two-phase degradation process,the change-point is probably located in the interval between two neighboring degradation measurements,and it is a fuzzy variable.The imprecision of the change-point may lead to the incorrect product’s reliability evaluation results.In this paper,based on the fuzzy theory,a two-phase degradation model with a fuzzy change-point and a statistical analysis method are proposed.First,a two-phase Wiener degradation model is developed according to the membership function of the change-point.Second,the reliability evaluation is carried out using maximum likelihood estimation and a fuzzy simulation approach.Finally,the proposed methodology is verified via a case study.The results of the study show that the proposed methodology can achieve more believable reliability evaluation results compared with those of the conventional approach.展开更多
In this paper, the roughness of the model function to the basis functions and its properties have been considered. We also consider some conditions to take the limit of the roughness when the observations are i.i.d. A...In this paper, the roughness of the model function to the basis functions and its properties have been considered. We also consider some conditions to take the limit of the roughness when the observations are i.i.d. An explicit formula to calculate the power of change-point test for the two phases regression through the roughness was obtained.展开更多
Against the deficiencies of component-based software(CBS) reliability modeling and analysis,for instance,importing too many assumptions,paying less attention to debugging process without considering imperfect debuggin...Against the deficiencies of component-based software(CBS) reliability modeling and analysis,for instance,importing too many assumptions,paying less attention to debugging process without considering imperfect debugging and change-point(CP) problems adequately,an approach of CBS reliability process analysis is proposed which incorporates the imperfect debugging and CP.First,perfect/imperfect debugging and CP are reviewed.Based on the queuing theory,a multi-queue multichannel and infinite server queuing model(MMISQM) is presented to sketch the integration test process of CBS.Meanwhile,considering the effects of imperfect debugging and CP,expressions for fault detection and correction are derived based on MMISQM.Numerical results demonstrate that the proposed model can sketch the integration test process of CBS with preferable performance which outperforms other models.展开更多
Effects of many medical procedures appear after a time lag, when a significant change occurs in subjects’ failure rate. This paper focuses on the detection and estimation of such changes which is important for the ev...Effects of many medical procedures appear after a time lag, when a significant change occurs in subjects’ failure rate. This paper focuses on the detection and estimation of such changes which is important for the evaluation and comparison of treatments and prediction of their effects. Unlike the classical change-point model, measurements may still be identically distributed, and the change point is a parameter of their common survival function. Some of the classical change-point detection techniques can still be used but the results are different. Contrary to the classical model, the maximum likelihood estimator of a change point appears consistent, even in presence of nuisance parameters. However, a more efficient procedure can be derived from Kaplan-Meier estimation of the survival function followed by the least-squares estimation of the change point. Strong consistency of these estimation schemes is proved. The finite-sample properties are examined by a Monte Carlo study. Proposed methods are applied to a recent clinical trial of the treatment program for strong drug dependence.展开更多
The paper aimed to better characterize how flood impacts have changed over time in Japan.It is hypothesized that different flood impact indicators vary in their sensitivity to significant changes.To test this hypothes...The paper aimed to better characterize how flood impacts have changed over time in Japan.It is hypothesized that different flood impact indicators vary in their sensitivity to significant changes.To test this hypothesis,change-point analysis was applied to various indicators,including flood-related deaths,the ratio of deaths to total flood victims,and a newly proposed composite indicator that integrates both loss of life and property damage.The analysis revealed that while the annual number of flood victims has remained statistically unchanged during the study period,the proportion of deaths among victims has increased.Similarly,although the annual number of completely damaged houses did not show a significant change,the proportion of completely damaged houses relative to the total number of flooded houses has risen.According to the newly developed composite indicator,the overall impact of flooding in Japan has shifted upward since 2004.The value of this study lies in its novel approach of combining loss of life with property damage in trend analysis,enabling policymakers and citizens to better understand the evolving risks posed by floods.These findings not only provide policymakers with a comprehensive reference for evaluating the effectiveness of flood management measures but also help promote public participation in flood mitigation efforts.展开更多
We consider some non-homogeneous Poisson models to estimate the mean number of times that a given environmental threshold of interest is surpassed by a given pollutant. Seven different rate functions for the Poisson p...We consider some non-homogeneous Poisson models to estimate the mean number of times that a given environmental threshold of interest is surpassed by a given pollutant. Seven different rate functions for the Poisson processes describing the models are taken into account. The rate functions considered are the Weibull, exponentiated-Weibull, and their generalisation the Beta-Weibull rate function. We also use the Musa-Okumoto, the Goel-Okumoto, a generalised Goel- Okumoto and the Weibull-geometric rate functions. Whenever thought justifiable, the model allowing the presence of change-points is also going to be considered. The different models are applied to the daily maximum ozone measurements data provided by the monitoring network of the Metropolitan Area of Mexico City. The aim is to compare the adjustment of different rate functions to the data. Even though, some of the rate functions have been considered before, now we are applying them to the same data set. In previous works they were used in different data sets and therefore a comparison of the adequacy of those models were not possible. The measurements considered here were obtained after a series of environmental measures were implemented in Mexico City. Hence, the data present a different behaviour from that of earlier studies.展开更多
Taking the Lhasa River Basin above Lhasa hydrological station in Tibetan Plateau as a study area, the characteristics of the annual and monthly mean runoff during 1956-2003 were analyzed, based on the hydro-data of th...Taking the Lhasa River Basin above Lhasa hydrological station in Tibetan Plateau as a study area, the characteristics of the annual and monthly mean runoff during 1956-2003 were analyzed, based on the hydro-data of the two hydrological stations （Lhasa and Tanggya） and the meteorological data of the three meteorological stations （Damxung, Lhasa and Tanggya）. The trends and the change points of runoff and climate from 1956 to 2003 were detected using the nonparametric Mann-Kendall test and Pettitt-Mann-Whitney change-point statistics. The correlations between runoff and climate change were analyzed using multiple linear regression. The major results could be summarized as follows： （1） The annual mean runoff during the last 50 years is characterized by a great fluctuation and a positive trend with two change points （around 1970 and the early 1980s）, after which the runoff tended to increase and was increasing intensively in the last 20 years. Besides, the monthly mean runoff with a positive trend is centralized in winter half-year （November to April） and some other months （May, July and September）. （2） The trends of the climate change in the study area are generally consistent with the trend of the runoff, but the leading climate factors which aroused the runoff variation are distinct. Precipitation is the dominant factor influencing the annual and monthly mean runoff in summer half year, while temperature is the primary factor in winter season.展开更多
As the web-server based business is rapidly developed and popularized, how to evaluate and improve the reliability of web-servers has been extremely important. Although a large num- ber of software reliability growth ...As the web-server based business is rapidly developed and popularized, how to evaluate and improve the reliability of web-servers has been extremely important. Although a large num- ber of software reliability growth models （SRGMs）, including those combined with multiple change-points （CPs）, have been available, these conventional SRGMs cannot be directly applied to web soft- ware reliability analysis because of the complex web operational profile. To characterize the web operational profile precisely, it should be realized that the workload of a web server is normally non-homogeneous and often observed with the pattern of random impulsive shocks. A web software reliability model with random im- pulsive shocks and its statistical analysis method are developed. In the proposed model, the web server workload is characterized by a geometric Brownian motion process. Based on a real data set from IIS server logs of ICRMS website （www.icrms.cn）, the proposed model is demonstrated to be powerful for estimating impulsive shocks and web software reliability.展开更多
Accurate classification and prediction of future traffic conditions are essential for developing effective strategies for congestion mitigation on the highway systems. Speed distribution is one of the traffic stream p...Accurate classification and prediction of future traffic conditions are essential for developing effective strategies for congestion mitigation on the highway systems. Speed distribution is one of the traffic stream parameters, which has been used to quantify the traffic conditions. Previous studies have shown that multi-modal probability distribution of speeds gives excellent results when simultaneously evaluating congested and free-flow traffic conditions. However, most of these previous analytical studies do not incorporate the influencing factors in characterizing these conditions. This study evaluates the impact of traffic occupancy on the multi-state speed distribution using the Bayesian Dirichlet Process Mixtures of Generalized Linear Models (DPM-GLM). Further, the study estimates the speed cut-point values of traffic states, which separate them into homogeneous groups using Bayesian change-point detection (BCD) technique. The study used 2015 archived one-year traffic data collected on Florida’s Interstate 295 freeway corridor. Information criteria results revealed three traffic states, which were identified as free-flow, transitional flow condition (congestion onset/offset), and the congested condition. The findings of the DPM-GLM indicated that in all estimated states, the traffic speed decreases when traffic occupancy increases. Comparison of the influence of traffic occupancy between traffic states showed that traffic occupancy has more impact on the free-flow and the congested state than on the transitional flow condition. With respect to estimating the threshold speed value, the results of the BCD model revealed promising findings in characterizing levels of traffic congestion.展开更多
In this paper, we propose two monitoring schemes to monitor change in the mean vector of independent multivariate process after a period of size m. The first procedure is based on the CUSUM of residuals, and the secon...In this paper, we propose two monitoring schemes to monitor change in the mean vector of independent multivariate process after a period of size m. The first procedure is based on the CUSUM of residuals, and the second procedure employs the CUSUM of recursive residuals. The corresponding asymptotic distributions of the statistics are derived. Simula- tions show that the proposed monitoring procedures perform well. The empirical application illustrates the practicability and effectiveness of the procedures.展开更多
基金supported by the National Natural Science Foundation of China under Grant Nos.11661067,11801438,71661028,61966030the Natural Science Foundation of Qinghai Province under Grant No.2019-ZJ-920。
文摘This paper proposes two ratio-type statistics to sequentially detect mean and variance change-points in the long-memory time series.The limiting distributions of monitoring statistics under the no change-point null hypothesis,alternative hypothesis as well as change-point misspecified hypothesis are proved.In particular,a sieve bootstrap approximation method is proposed to determine the critical values.Simulations indicate that the new monitoring procedures have better finite sample performance than the available off-line tests when the change-point nears to the beginning time of monitoring,and can discriminate between mean and variance change-point.Finally,the authors illustrate their procedures via two real data sets:A set of annual volume of discharge data of the Nile river,and a set of monthly temperature data of northern hemisphere.The authors find a new variance change-point in the latter data.
基金NSFC(Grant No.12171427/U21A20426/11771390)Zhejiang Provincial Natural Science Foundation(Grant No.LZ21A010002)the Fundamental Research Funds for the Central Universities(Grant No.2021XZZX002)。
文摘Multiple change-points estimation for functional time series is studied in this paper.The change-point problem is first transformed into a high-dimensional sparse estimation problem via basis functions.Group least absolute shrinkage and selection operator(LASSO)is then applied to estimate the number and the locations of possible change points.However,the group LASSO(GLASSO)always overestimate the true points.To circumvent this problem,a further Information Criterion(IC)is applied to eliminate the redundant estimated points.It is shown that the proposed two-step procedure estimates the number and the locations of the change-points consistently.Simulations and two temperature data examples are also provided to illustrate the finite sample performance of the proposed method.
文摘风电机组运行过程中,一些故障导致设备状态发生改变,状态的改变发生在一个持续的时间序列中,找到变化点的时间对于故障回溯及根本原因分析具有重要价值。该文研究风电信号及状态时序变化的特点,引入统计学中的Change-Point算法,通过划分不同置信区间求取置信度方法解决奇异变点的不确定度问题。通过实验对算法进行验证,得出以下结论:Change-Point算法能够有效挖掘到历史数据中的一维及二维模型数据的变化,并给出变点;Change-Point算法思想是挖掘出数据本身的规律性,不受其他条件限制,因此可广泛应用于风电机组数据采集与监视控制(supervisory control and data acquisition,SCADA)系统变量数据挖掘中的问题回溯,快速定位SCADA数据状态变化点。
基金supported by the Initial Scientific Research Fund (No.2015QD02S)the Foundation Research Funds for the Central Universities (No.3122016A004, 3122017027)
文摘Electrostatic monitoring technology of particle charging information can facilitate online monitoring of aero-engine,which effectively enhances engine fault diagnosis and health managements.Unlike traditional engine state monitoring technologies,aircraft engine monitoring by gas path electrostatic monitoring not only covers the predicted information source itself,but also detects the information that can provide an early warnings for initial fault states through gas path charging levels.This paper establishes a non-stationary time sequence change-point model for anomaly recognition of electrostatic signals based on change-point theory combined with difference method of non-stationary time series.Finally,electrostatic induction data were utilized by the engine life test for a particular aircraft to validate the proposed algorithm.The results indicate that the activity level and the event rate were0.5—0.8(nc)and 50%,respectively,which were far greater than 4—12(pc)and 0—4% under normal working conditions of the engine.
基金Supported by the International Science&Technology Cooperation Program of China(No.2010DFA14400)the National Natural Science Foundation of China(No.60503015)the National High Technology Research and Development Programme of China(No.2008AA01A201)
文摘This paper presents software reliability growth models(SRGMs) with change-point based on the stochastic differential equation(SDE).Although SRGMs based on SDE have been developed in a large scale software system,considering the variation of failure distribution in the existing models during testing time is limited.These SDE SRGMs assume that failures have the same distribution.However,in practice,the fault detection rate can be affected by some factors and may be changed at certain point as time proceeds.With respect to this issue,in this paper,SDE SRGMs with changepoint are proposed to precisely reflect the variations of the failure distribution.A real data set is used to evaluate the new models.The experimental results show that the proposed models have a fairly accurate prediction capability.
文摘A number of statistical tests are proposed for the purpose of change-point detection in a general nonparametric regression model under mild conditions. New proofs are given to prove the weak convergence of the underlying processes which assume remove the stringent condition of bounded total variation of the regression function and need only second moments. Since many quantities, such as the regression function, the distribution of the covariates and the distribution of the errors, are unspecified, the results are not distribution-free. A weighted bootstrap approach is proposed to approximate the limiting distributions. Results of a simulation study for this paper show good performance for moderate samples sizes.
文摘Testing-time when a change of a stochastic characteristic of the software failure-occurrence time or software failure-occurrence time-interval is observed is called change-point. It is said that effect of the change-point on the software reliability growth process influences on accuracy for software reliability assessment based on a software reliability growth model (SRGM). We propose an SRGM with the effect of the change-point based on a bivariate SRGM, in which the software reliability growth process is assumed to depend on the testing-time and testing-effort factors simultaneously, for accurate software reliability assessment. And we discuss an optimal software release problem for deriving optimal testing-effort expenditures based on our model. Further, we show numerical examples of software reliability assessment based on our bivariate SRGM and estimation of optimal testing-effort expenditures by using actual data.
基金Supported by Doctoral Fund of Huaqiao University(600005-Z17Y0078,605-50Y17078)Promotion Program for Young and Middle-Aged Teacher in Science and Technology Research of Huaqiao University(ZQN-YX401)
文摘We design monitoring procedures for the common change-point in a sequential panel data model. An asymptotic method and two new bootstrap methods are proposed to obtain critical values. We establish the asymptotic validity of the proposed bootstrap procedures. In simulation studies the empirical test size and the empirical test power values are investigated to show that the three tests are valid and have their own applications.At the same time, the estimations of an unknown change-point are obtained by using the proposed test statistic with these three methods.
基金the National Natural Science Foundation of China(No.61703391)。
文摘For some products,degradation mechanisms change during testing,and therefore,their degradation patterns vary at different points in time;these points are called change-points.Owing to the limitation of measurement costs,time intervals for degradation measurements are usually very long,and thus,the value of change-points cannot be determined.Conventionally,a certain degradation measurement is selected as the change-point in a two-phase degradation process.According to the tendency of the two-phase degradation process,the change-point is probably located in the interval between two neighboring degradation measurements,and it is a fuzzy variable.The imprecision of the change-point may lead to the incorrect product’s reliability evaluation results.In this paper,based on the fuzzy theory,a two-phase degradation model with a fuzzy change-point and a statistical analysis method are proposed.First,a two-phase Wiener degradation model is developed according to the membership function of the change-point.Second,the reliability evaluation is carried out using maximum likelihood estimation and a fuzzy simulation approach.Finally,the proposed methodology is verified via a case study.The results of the study show that the proposed methodology can achieve more believable reliability evaluation results compared with those of the conventional approach.
文摘In this paper, the roughness of the model function to the basis functions and its properties have been considered. We also consider some conditions to take the limit of the roughness when the observations are i.i.d. An explicit formula to calculate the power of change-point test for the two phases regression through the roughness was obtained.
基金Supported by the National High Technology Research and Development Program of China(No.2008AA01A201)the National Natural ScienceFoundation of China(No.60503015)+1 种基金the National Key R&D Program of China(No.2013BA17F02)the Shandong Province Science andTechnology Program of China(No.2011GGX10108,2010GGX10104)
文摘Against the deficiencies of component-based software(CBS) reliability modeling and analysis,for instance,importing too many assumptions,paying less attention to debugging process without considering imperfect debugging and change-point(CP) problems adequately,an approach of CBS reliability process analysis is proposed which incorporates the imperfect debugging and CP.First,perfect/imperfect debugging and CP are reviewed.Based on the queuing theory,a multi-queue multichannel and infinite server queuing model(MMISQM) is presented to sketch the integration test process of CBS.Meanwhile,considering the effects of imperfect debugging and CP,expressions for fault detection and correction are derived based on MMISQM.Numerical results demonstrate that the proposed model can sketch the integration test process of CBS with preferable performance which outperforms other models.
文摘Effects of many medical procedures appear after a time lag, when a significant change occurs in subjects’ failure rate. This paper focuses on the detection and estimation of such changes which is important for the evaluation and comparison of treatments and prediction of their effects. Unlike the classical change-point model, measurements may still be identically distributed, and the change point is a parameter of their common survival function. Some of the classical change-point detection techniques can still be used but the results are different. Contrary to the classical model, the maximum likelihood estimator of a change point appears consistent, even in presence of nuisance parameters. However, a more efficient procedure can be derived from Kaplan-Meier estimation of the survival function followed by the least-squares estimation of the change point. Strong consistency of these estimation schemes is proved. The finite-sample properties are examined by a Monte Carlo study. Proposed methods are applied to a recent clinical trial of the treatment program for strong drug dependence.
文摘The paper aimed to better characterize how flood impacts have changed over time in Japan.It is hypothesized that different flood impact indicators vary in their sensitivity to significant changes.To test this hypothesis,change-point analysis was applied to various indicators,including flood-related deaths,the ratio of deaths to total flood victims,and a newly proposed composite indicator that integrates both loss of life and property damage.The analysis revealed that while the annual number of flood victims has remained statistically unchanged during the study period,the proportion of deaths among victims has increased.Similarly,although the annual number of completely damaged houses did not show a significant change,the proportion of completely damaged houses relative to the total number of flooded houses has risen.According to the newly developed composite indicator,the overall impact of flooding in Japan has shifted upward since 2004.The value of this study lies in its novel approach of combining loss of life with property damage in trend analysis,enabling policymakers and citizens to better understand the evolving risks posed by floods.These findings not only provide policymakers with a comprehensive reference for evaluating the effectiveness of flood management measures but also help promote public participation in flood mitigation efforts.
基金financially supported by the project PAPIIT number IN104110-3 of the Direccion General de Apoyo al Personal Academico of the Universidad Nacional Autonoma de Mexico,Mexico,and is part of JMB’s Ph.D.partially funded by the Consejo Nacional de Ciencias y Tecnologia,Mexico,through the Ph.D.Scholarship number 210347JAA was partially funded by the Conselho Nacional de Pesquisa,Brazil,grant number 300235/2005-4.
文摘We consider some non-homogeneous Poisson models to estimate the mean number of times that a given environmental threshold of interest is surpassed by a given pollutant. Seven different rate functions for the Poisson processes describing the models are taken into account. The rate functions considered are the Weibull, exponentiated-Weibull, and their generalisation the Beta-Weibull rate function. We also use the Musa-Okumoto, the Goel-Okumoto, a generalised Goel- Okumoto and the Weibull-geometric rate functions. Whenever thought justifiable, the model allowing the presence of change-points is also going to be considered. The different models are applied to the daily maximum ozone measurements data provided by the monitoring network of the Metropolitan Area of Mexico City. The aim is to compare the adjustment of different rate functions to the data. Even though, some of the rate functions have been considered before, now we are applying them to the same data set. In previous works they were used in different data sets and therefore a comparison of the adequacy of those models were not possible. The measurements considered here were obtained after a series of environmental measures were implemented in Mexico City. Hence, the data present a different behaviour from that of earlier studies.
基金National Basic Research Program of China, No.2005CB422006 National Natural Science Foundation of China, No.90202012 No.40561002
文摘Taking the Lhasa River Basin above Lhasa hydrological station in Tibetan Plateau as a study area, the characteristics of the annual and monthly mean runoff during 1956-2003 were analyzed, based on the hydro-data of the two hydrological stations （Lhasa and Tanggya） and the meteorological data of the three meteorological stations （Damxung, Lhasa and Tanggya）. The trends and the change points of runoff and climate from 1956 to 2003 were detected using the nonparametric Mann-Kendall test and Pettitt-Mann-Whitney change-point statistics. The correlations between runoff and climate change were analyzed using multiple linear regression. The major results could be summarized as follows： （1） The annual mean runoff during the last 50 years is characterized by a great fluctuation and a positive trend with two change points （around 1970 and the early 1980s）, after which the runoff tended to increase and was increasing intensively in the last 20 years. Besides, the monthly mean runoff with a positive trend is centralized in winter half-year （November to April） and some other months （May, July and September）. （2） The trends of the climate change in the study area are generally consistent with the trend of the runoff, but the leading climate factors which aroused the runoff variation are distinct. Precipitation is the dominant factor influencing the annual and monthly mean runoff in summer half year, while temperature is the primary factor in winter season.
基金supported by the International Technology Cooperation Project of Guizhou Province(QianKeHeWaiGZi[2012]7052)the National Scientific Research Project for Statistics(2012LZ054)
文摘As the web-server based business is rapidly developed and popularized, how to evaluate and improve the reliability of web-servers has been extremely important. Although a large num- ber of software reliability growth models （SRGMs）, including those combined with multiple change-points （CPs）, have been available, these conventional SRGMs cannot be directly applied to web soft- ware reliability analysis because of the complex web operational profile. To characterize the web operational profile precisely, it should be realized that the workload of a web server is normally non-homogeneous and often observed with the pattern of random impulsive shocks. A web software reliability model with random im- pulsive shocks and its statistical analysis method are developed. In the proposed model, the web server workload is characterized by a geometric Brownian motion process. Based on a real data set from IIS server logs of ICRMS website （www.icrms.cn）, the proposed model is demonstrated to be powerful for estimating impulsive shocks and web software reliability.
文摘Accurate classification and prediction of future traffic conditions are essential for developing effective strategies for congestion mitigation on the highway systems. Speed distribution is one of the traffic stream parameters, which has been used to quantify the traffic conditions. Previous studies have shown that multi-modal probability distribution of speeds gives excellent results when simultaneously evaluating congested and free-flow traffic conditions. However, most of these previous analytical studies do not incorporate the influencing factors in characterizing these conditions. This study evaluates the impact of traffic occupancy on the multi-state speed distribution using the Bayesian Dirichlet Process Mixtures of Generalized Linear Models (DPM-GLM). Further, the study estimates the speed cut-point values of traffic states, which separate them into homogeneous groups using Bayesian change-point detection (BCD) technique. The study used 2015 archived one-year traffic data collected on Florida’s Interstate 295 freeway corridor. Information criteria results revealed three traffic states, which were identified as free-flow, transitional flow condition (congestion onset/offset), and the congested condition. The findings of the DPM-GLM indicated that in all estimated states, the traffic speed decreases when traffic occupancy increases. Comparison of the influence of traffic occupancy between traffic states showed that traffic occupancy has more impact on the free-flow and the congested state than on the transitional flow condition. With respect to estimating the threshold speed value, the results of the BCD model revealed promising findings in characterizing levels of traffic congestion.
基金Supported by Humanities and Social Science Research Project Fund of Ministry of Education(Grant No.14JA790034)the National Natural Science Foundation of Tianyuan Fund(Grant No.11226217)
文摘In this paper, we propose two monitoring schemes to monitor change in the mean vector of independent multivariate process after a period of size m. The first procedure is based on the CUSUM of residuals, and the second procedure employs the CUSUM of recursive residuals. The corresponding asymptotic distributions of the statistics are derived. Simula- tions show that the proposed monitoring procedures perform well. The empirical application illustrates the practicability and effectiveness of the procedures.
