In this paper, we not only construct the confidence region for parameters in a mixed integer-valued autoregressive process using the empirical likelihood method, but also establish the empirical log-likelihood ratio s...In this paper, we not only construct the confidence region for parameters in a mixed integer-valued autoregressive process using the empirical likelihood method, but also establish the empirical log-likelihood ratio statistic and obtain its limiting distribution. And then, via simulation studies we give coverage probabilities for the parameters of interest. The results show that the empirical likelihood method performs very well.展开更多
针对混频数据的建模问题,提出自回归U-MIDAS(unrestricted mixed data sampling)分位回归模型.首先,结合嵌套Lasso惩罚方法及spike-and-slab先验进行Bayes参数估计和变量选择;其次,通过数值模拟证明该方法的优越性;最后,将该方法用于美...针对混频数据的建模问题,提出自回归U-MIDAS(unrestricted mixed data sampling)分位回归模型.首先,结合嵌套Lasso惩罚方法及spike-and-slab先验进行Bayes参数估计和变量选择;其次,通过数值模拟证明该方法的优越性;最后,将该方法用于美国名义国内生产总值(GDP)年化季度增长率的预测,结果表明,该方法预测精度较好.展开更多
Using Monte Carlo methods we generate time series with the following features： a） series with distributions that are the mix of two normal distributions with different variances, b） series that satisfy volatility m...Using Monte Carlo methods we generate time series with the following features： a） series with distributions that are the mix of two normal distributions with different variances, b） series that satisfy volatility models, c） series that satisfy an AR（1） model but with contaminated errors that follow the same distribution as the mixes given in a） and d） series that follow the same distribution as the mixes given in a） but with conditional heterocedasticity. From the analysis we see that it is difficult to identify in practical situations the real generating process of the series. In fact, the processes that come from distribution mixes have many similar characteristics to the ones that satisfy the volatility scheme. We use the corresponding theoretical considerations and also the usual tools in the identifying process of any time series; that is, series graphs, histograms, the corresponding sampling distributions, correlograms and partial correlograms.展开更多
Climate change is one of the critical determinants affecting life cycles and transmission of most infectious agents,including malaria,cholera,dengue fever,hand,foot,and mouth disease(HFMD),and the recent Corona-virus ...Climate change is one of the critical determinants affecting life cycles and transmission of most infectious agents,including malaria,cholera,dengue fever,hand,foot,and mouth disease(HFMD),and the recent Corona-virus pandemic.HFMD has been associated with a growing number of outbreaks resulting in fatal complications since the late 1990s.The outbreaks may result from a combination of rapid population growth,climate change,socioeconomic changes,and other lifestyle changes.However,the modeling of climate variability and HFMD remains unclear,particularly in statistical theory development.The statistical relationship between HFMD and climate factors has been widely studied using generalized linear and additive modeling.When dealing with time-series data with clustered variables such as HFMD with clustered states,the independence principle of both modeling approaches may be violated.Thus,a Generalized Additive Mixed Model(GAMM)is used to investigate the relationship between HFMD and climate factors in Malaysia.The model is improved by using a first-order autoregressive term and treating all Malaysian states as a random effect.This method is preferred as it allows states to be modeled as random effects and accounts for time series data autocorrelation.The findings indicate that climate variables such as rainfall and wind speed affect HFMD cases in Malaysia.The risk of HFMD increased in the subsequent two weeks with rainfall below 60 mm and decreased with rainfall exceeding 60 mm.Besides,a two-week lag in wind speeds between 2 and 5 m/s reduced HFMD's chances.The results also show that HFMD cases rose in Malaysia during the inter-monsoon and southwest monsoon seasons but fell during the northeast monsoon.The study's outcomes can be used by public health officials and the general public to raise awareness,and thus,implement effective preventive measures.展开更多
提出了一类用于非线性时间序列建模的混合自回归滑动平均模型(MARMA).该模型是由K个平稳或非平稳的ARMA分量经过混合得到的.讨论了MARMA模型的平稳性条件和自相关函数.给出了MARMA模型参数估计的期望极大化(expectation maximization)算...提出了一类用于非线性时间序列建模的混合自回归滑动平均模型(MARMA).该模型是由K个平稳或非平稳的ARMA分量经过混合得到的.讨论了MARMA模型的平稳性条件和自相关函数.给出了MARMA模型参数估计的期望极大化(expectation maximization)算法.运用贝叶斯信息准则(Bayes information criterion)来选择该模型.MARMA模型分布形式富于变化的特征使得它能够对具有多峰分布以及条件异方差的序列进行建模.通过两个实例验证了该模型,并和其他模型进行比较,结果表明MARMA模型能够更好地描述这些数据的特征.展开更多
混合性(Mixing)在时间序列和空间计量经济学研究中起着重要的作用,许多时间序列文献都假设其模型中的变量服从混合过程.然而,目前尚无关于判定空间计量经济模型所生成数据是否满足混合性质的准则.基于Doukhan(1994)的思想,基于若干常见...混合性(Mixing)在时间序列和空间计量经济学研究中起着重要的作用,许多时间序列文献都假设其模型中的变量服从混合过程.然而,目前尚无关于判定空间计量经济模型所生成数据是否满足混合性质的准则.基于Doukhan(1994)的思想,基于若干常见假设,我们建立了一系列准则,用于判定不规则格点上的线性空间过程是否满足α-混合性.我们将这些准则应用于建立由空间自回归模型、空间误差模型、矩阵指数空间模型以及基于潜在被解释变量的空间计量经济模型(例如空间样本选择模型)所生成被解释变量的α-混合性质.利用α-混合性质,我们建立了Flores-Lagunes et al.(2012)提出的空间样本选择模型的估计量的大样本性质.展开更多
基金Supported by National Natural Science Foundation of China(11731015,11571051,J1310022,11501241)Natural Science Foundation of Jilin Province(20150520053JH,20170101057JC,20180101216JC)+2 种基金Program for Changbaishan Scholars of Jilin Province(2015010)Science and Technology Program of Jilin Educational Department during the "13th Five-Year" Plan Period(2016-399)Science and Technology Research Program of Education Department in Jilin Province for the 13th Five-Year Plan(2016213)
文摘In this paper, we not only construct the confidence region for parameters in a mixed integer-valued autoregressive process using the empirical likelihood method, but also establish the empirical log-likelihood ratio statistic and obtain its limiting distribution. And then, via simulation studies we give coverage probabilities for the parameters of interest. The results show that the empirical likelihood method performs very well.
文摘针对混频数据的建模问题,提出自回归U-MIDAS(unrestricted mixed data sampling)分位回归模型.首先,结合嵌套Lasso惩罚方法及spike-and-slab先验进行Bayes参数估计和变量选择;其次,通过数值模拟证明该方法的优越性;最后,将该方法用于美国名义国内生产总值(GDP)年化季度增长率的预测,结果表明,该方法预测精度较好.
文摘Using Monte Carlo methods we generate time series with the following features： a） series with distributions that are the mix of two normal distributions with different variances, b） series that satisfy volatility models, c） series that satisfy an AR（1） model but with contaminated errors that follow the same distribution as the mixes given in a） and d） series that follow the same distribution as the mixes given in a） but with conditional heterocedasticity. From the analysis we see that it is difficult to identify in practical situations the real generating process of the series. In fact, the processes that come from distribution mixes have many similar characteristics to the ones that satisfy the volatility scheme. We use the corresponding theoretical considerations and also the usual tools in the identifying process of any time series; that is, series graphs, histograms, the corresponding sampling distributions, correlograms and partial correlograms.
基金This work was supported by the Ministry of Higher Education,Malaysia under the Fundamental Research Grant Scheme FRGS/1/2020/STG06/UTM/02/3(5F311)Research University Grant with vote no:QJ130000.3854.19J58Zamalah UTM Scholarship under Universiti Teknologi Malaysia.
文摘Climate change is one of the critical determinants affecting life cycles and transmission of most infectious agents,including malaria,cholera,dengue fever,hand,foot,and mouth disease(HFMD),and the recent Corona-virus pandemic.HFMD has been associated with a growing number of outbreaks resulting in fatal complications since the late 1990s.The outbreaks may result from a combination of rapid population growth,climate change,socioeconomic changes,and other lifestyle changes.However,the modeling of climate variability and HFMD remains unclear,particularly in statistical theory development.The statistical relationship between HFMD and climate factors has been widely studied using generalized linear and additive modeling.When dealing with time-series data with clustered variables such as HFMD with clustered states,the independence principle of both modeling approaches may be violated.Thus,a Generalized Additive Mixed Model(GAMM)is used to investigate the relationship between HFMD and climate factors in Malaysia.The model is improved by using a first-order autoregressive term and treating all Malaysian states as a random effect.This method is preferred as it allows states to be modeled as random effects and accounts for time series data autocorrelation.The findings indicate that climate variables such as rainfall and wind speed affect HFMD cases in Malaysia.The risk of HFMD increased in the subsequent two weeks with rainfall below 60 mm and decreased with rainfall exceeding 60 mm.Besides,a two-week lag in wind speeds between 2 and 5 m/s reduced HFMD's chances.The results also show that HFMD cases rose in Malaysia during the inter-monsoon and southwest monsoon seasons but fell during the northeast monsoon.The study's outcomes can be used by public health officials and the general public to raise awareness,and thus,implement effective preventive measures.
文摘提出了一类用于非线性时间序列建模的混合自回归滑动平均模型(MARMA).该模型是由K个平稳或非平稳的ARMA分量经过混合得到的.讨论了MARMA模型的平稳性条件和自相关函数.给出了MARMA模型参数估计的期望极大化(expectation maximization)算法.运用贝叶斯信息准则(Bayes information criterion)来选择该模型.MARMA模型分布形式富于变化的特征使得它能够对具有多峰分布以及条件异方差的序列进行建模.通过两个实例验证了该模型,并和其他模型进行比较,结果表明MARMA模型能够更好地描述这些数据的特征.
文摘混合性(Mixing)在时间序列和空间计量经济学研究中起着重要的作用,许多时间序列文献都假设其模型中的变量服从混合过程.然而,目前尚无关于判定空间计量经济模型所生成数据是否满足混合性质的准则.基于Doukhan(1994)的思想,基于若干常见假设,我们建立了一系列准则,用于判定不规则格点上的线性空间过程是否满足α-混合性.我们将这些准则应用于建立由空间自回归模型、空间误差模型、矩阵指数空间模型以及基于潜在被解释变量的空间计量经济模型(例如空间样本选择模型)所生成被解释变量的α-混合性质.利用α-混合性质,我们建立了Flores-Lagunes et al.(2012)提出的空间样本选择模型的估计量的大样本性质.
