In this paper, the Holt’s exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) models were used to forecast inflation rate of Zambia using the monthly consumer price index (CPI) data from May 2...In this paper, the Holt’s exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) models were used to forecast inflation rate of Zambia using the monthly consumer price index (CPI) data from May 2010 to May 2014. Results show that the ARIMA ((12), 1, 0) is an adequate model which best fits the CPI time series data and is therefore suitable for forecasting CPI and subsequently the inflation rate. However, the choice of the Holt’s exponential smoothing is as good as an ARIMA model considering the smaller deviations in the mean absolute percentage error and mean square error. Moreover, the Holt’s exponential smoothing model is less complicated since you do not require specialised software to implement it as is the case for ARIMA models. The forecasted inflation rate for April and May, 2015 is 7.0 and 6.6 respectively.展开更多
In the investigation of disease dynamics, the effect of covariates on the hazard function is a major topic. Some recent smoothed estimation methods have been proposed, both frequentist and Bayesian, based on the relat...In the investigation of disease dynamics, the effect of covariates on the hazard function is a major topic. Some recent smoothed estimation methods have been proposed, both frequentist and Bayesian, based on the relationship between penalized splines and mixed models theory. These approaches are also motivated by the possibility of using automatic procedures for determining the optimal amount of smoothing. However, estimation algorithms involve an analytically intractable hazard function, and thus require ad-hoc software routines. We propose a more user-friendly alternative, consisting in regularized estimation of piecewise exponential models by Bayesian P-splines. A further facilitation is that widespread Bayesian software, such as WinBUGS, can be used. The aim is assessing the robustness of this approach with respect to different prior functions and penalties. A large dataset from breast cancer patients, where results from validated clinical studies are available, is used as a benchmark to evaluate the reliability of the estimates. A second dataset from a small case series of sarcoma patients is used for evaluating the performances of the PE model as a tool for exploratory analysis. Concerning breast cancer data, the estimates are robust with respect to priors and penalties, and consistent with clinical knowledge. Concerning soft tissue sarcoma data, the estimates of the hazard function are sensitive with respect to the prior for the smoothing parameter, whereas the estimates of regression coefficients are robust. In conclusion, Gibbs sampling results an efficient computational strategy. The issue of the sensitivity with respect to the priors concerns only the estimates of the hazard function, and seems more likely to occur when non-large case series are investigated, calling for tailored solutions.展开更多
Background The World Health Organization End TB Strategy meant that compared with 2015 baseline,the reduction in pulmonary tuberculosis(PTB)incidence should be 20 and 50%in 2020 and 2025,respectively.The case number o...Background The World Health Organization End TB Strategy meant that compared with 2015 baseline,the reduction in pulmonary tuberculosis(PTB)incidence should be 20 and 50%in 2020 and 2025,respectively.The case number of PTB in China accounted for 9%of the global total in 2018,which ranked the second high in the world.From 2007 to 2019,854672 active PTB cases were registered and treated in Henan Province,China.This study was to assess whether the WHO milestones could be achieved in Henan Province.Methods The active PTB numbers in Henan Province from 2007 to 2019,registered in Chinese Tuberculosis Information Management System were analyzed to predict the active PTB registration rates in 2020 and 2025,which is conductive to early response measures to ensure the achievement of the WHO milestones.The time series model was created by monthly active PTB registration rates from 2007 to 2016,and the optimal model was verified by data from 2017 to 2019.The Ljung-Box Q statistic was used to evaluate the model.The statistically significant level isα=0.05.Monthly active PTB registration rates and 95%confidence interval(CI)from 2020 to 2025 were predicted.Results High active PTB registration rates in March,April,May and June showed the seasonal variations.The exponential smoothing winter’s multiplication model was selected as the best-fitting model.The predicted values were approximately consistent with the observed ones from 2017 to 2019.The annual active PTB registration rates were predicted as 49.1(95%CI:36.2–62.0)per 100000 population and 34.4(95%CI:18.6–50.2)per 100000 population in 2020 and 2025,respectively.Compared with the active PTB registration rate in 2015,the reduction will reach 23.7%(95%CI,3.2–44.1%)and 46.8%(95%CI,21.4–72.1%)in 2020 and 2025,respectively.Conclusions The high active PTB registration rates in spring and early summer indicate that high risk of tuberculosis infection in late autumn and winter in Henan Province.Without regard to the CI,the first milestone of WHO End TB Strategy in 2020 will be achieved.However,the second milestone in 2025 will not be easily achieved unless there are early response measures in Henan Province,China.展开更多
基金Supported by National High Technology Research and Development Program of China （863 Program） （2007AA11Z221）, International Cooperation Project of Shanghai （08210707500）, and Natural Science Foundation of Shanghai.（08ZR1420600） . _
文摘In this paper, the Holt’s exponential smoothing and Auto-Regressive Integrated Moving Average (ARIMA) models were used to forecast inflation rate of Zambia using the monthly consumer price index (CPI) data from May 2010 to May 2014. Results show that the ARIMA ((12), 1, 0) is an adequate model which best fits the CPI time series data and is therefore suitable for forecasting CPI and subsequently the inflation rate. However, the choice of the Holt’s exponential smoothing is as good as an ARIMA model considering the smaller deviations in the mean absolute percentage error and mean square error. Moreover, the Holt’s exponential smoothing model is less complicated since you do not require specialised software to implement it as is the case for ARIMA models. The forecasted inflation rate for April and May, 2015 is 7.0 and 6.6 respectively.
文摘In the investigation of disease dynamics, the effect of covariates on the hazard function is a major topic. Some recent smoothed estimation methods have been proposed, both frequentist and Bayesian, based on the relationship between penalized splines and mixed models theory. These approaches are also motivated by the possibility of using automatic procedures for determining the optimal amount of smoothing. However, estimation algorithms involve an analytically intractable hazard function, and thus require ad-hoc software routines. We propose a more user-friendly alternative, consisting in regularized estimation of piecewise exponential models by Bayesian P-splines. A further facilitation is that widespread Bayesian software, such as WinBUGS, can be used. The aim is assessing the robustness of this approach with respect to different prior functions and penalties. A large dataset from breast cancer patients, where results from validated clinical studies are available, is used as a benchmark to evaluate the reliability of the estimates. A second dataset from a small case series of sarcoma patients is used for evaluating the performances of the PE model as a tool for exploratory analysis. Concerning breast cancer data, the estimates are robust with respect to priors and penalties, and consistent with clinical knowledge. Concerning soft tissue sarcoma data, the estimates of the hazard function are sensitive with respect to the prior for the smoothing parameter, whereas the estimates of regression coefficients are robust. In conclusion, Gibbs sampling results an efficient computational strategy. The issue of the sensitivity with respect to the priors concerns only the estimates of the hazard function, and seems more likely to occur when non-large case series are investigated, calling for tailored solutions.
文摘Background The World Health Organization End TB Strategy meant that compared with 2015 baseline,the reduction in pulmonary tuberculosis(PTB)incidence should be 20 and 50%in 2020 and 2025,respectively.The case number of PTB in China accounted for 9%of the global total in 2018,which ranked the second high in the world.From 2007 to 2019,854672 active PTB cases were registered and treated in Henan Province,China.This study was to assess whether the WHO milestones could be achieved in Henan Province.Methods The active PTB numbers in Henan Province from 2007 to 2019,registered in Chinese Tuberculosis Information Management System were analyzed to predict the active PTB registration rates in 2020 and 2025,which is conductive to early response measures to ensure the achievement of the WHO milestones.The time series model was created by monthly active PTB registration rates from 2007 to 2016,and the optimal model was verified by data from 2017 to 2019.The Ljung-Box Q statistic was used to evaluate the model.The statistically significant level isα=0.05.Monthly active PTB registration rates and 95%confidence interval(CI)from 2020 to 2025 were predicted.Results High active PTB registration rates in March,April,May and June showed the seasonal variations.The exponential smoothing winter’s multiplication model was selected as the best-fitting model.The predicted values were approximately consistent with the observed ones from 2017 to 2019.The annual active PTB registration rates were predicted as 49.1(95%CI:36.2–62.0)per 100000 population and 34.4(95%CI:18.6–50.2)per 100000 population in 2020 and 2025,respectively.Compared with the active PTB registration rate in 2015,the reduction will reach 23.7%(95%CI,3.2–44.1%)and 46.8%(95%CI,21.4–72.1%)in 2020 and 2025,respectively.Conclusions The high active PTB registration rates in spring and early summer indicate that high risk of tuberculosis infection in late autumn and winter in Henan Province.Without regard to the CI,the first milestone of WHO End TB Strategy in 2020 will be achieved.However,the second milestone in 2025 will not be easily achieved unless there are early response measures in Henan Province,China.
文摘针对多基地水下小目标分类识别问题,本文提出了一种基于核空间联合稀疏表示和指数平滑的多基地水下小目标识别方法 .对水下目标多角度散射信号提取6种典型的具有信息互补性和关联性的特征,提出一种随机森林(Random Forest,RF)和最小冗余最大相关(minimum Redundancy and Maximum Relevance,mRMR)相结合的特征选择方法(RF-mRMR),得出综合的特征重要性排序结果 .通过实验得出分类模型所需的最优特征子集,达到降低数据处理复杂度和提高目标分类结果的目的 .为了捕捉到数据中的高阶结构,在联合稀疏表示模型的基础上,使用核函数将线性不可分的特征数据映射到高维核特征空间.为了充分挖掘稀疏重构后包含在残差波段中的有用信息,使用指数平滑公式对具有一定意义的残差信息进行再利用,最后由核特征空间下的最小误差准则判定目标的类别.应用本文提出的方法对4类目标的海试数据进行识别,结果表明,相较于其他7种对比算法,本文提出的改进方法具有更好的分类性能,而且大多数情况下,本文提出的算法在双基地声呐模式下具有比单基地声呐更高的识别准确率和更低的虚警率.
