On the asymptotic properties of parameter estimates in a regression model with non-normally distributed errors

As pointed out in a recent paper by Amirkhalkhali and Rao (1986) (henceforth referred to as A&R), the usual assumption of normality for the error terms of a regression model isoften untenable. However, when this assumption is dropped, it may be difficult to characterize parameter estimates for the model. For example, A&R (p. 189) state that “if the regression errors are non-normal, we are not even sure of their [e.g., the generalized least squares parameter estimates1] asymptotic properties.” A partial answer, however, is given by Spall and Wall (1984), which presents an asymptotic distribution theory for Kalman filter estimates for cases where the random terms of the state space model are not necessarily Gaussian. Certain of these asymptotic distribution results are also discussed in Spall (1985) in the context of model validation (diagnostic checking)     

Short run spc based upon the second order dynamic linear model for trend detection

Recently, several new applications of control chart procedures for short production runs have been introduced. Bothe (1989) and Burr (1989) proposed the use of control chart statistics which are obtained by scaling the quality characteristic by target values or process estimates of a location and scale parameter. The performance of these control charts can be significantly affected by the use of incorrect scaling parameters, resulting in either an excessive "false alarm rate," or insensitivity to the detection of moderate shifts in the process. To correct for these deficiencies, Quesenberry (1990, 1991) has developed the Q-Chart which is formed from running process estimates of the sample mean and variance. For the case where both the process mean and variance are unknown, the Q-chaxt statistic is formed from the standard inverse Z-transformation of a t-statistic. Q-charts do not perform correctly, however, in the presence of special cause disturbances at process startup. This has recently been supported by results published by Del Castillo and Montgomery (1992), who recommend the use of an alternative control chart procedure which is based upon a first-order adaptive Kalman filter model Consistent with the recommendations by Castillo and Montgomery, we propose an alternative short run control chart procedure which is based upon the second order dynamic linear model (DLM). The control chart is shown to be useful for the early detection of unwanted process trends. Model and control chart parameters are updated sequentially in a Bayesian estimation framework, providing the greatest degree of flexibility in the level of prior information which is incorporated into the model. The result is a weighted moving average control chart statistic which can be used to provide running estimates of process capability. The average run length performance of the control chart is compared to the optimal performance of the exponentially weighted moving average (EWMA) chart, as reported by Gan (1991). Using a simulation approach, the second order DLM control chart is shown to provide better overall performance than the EWMA for short production run applications     

Modelling the travel time of transit vehicles in real‐time through a GTFS‐based road network using GPS vehicle locations

Predicting the arrival time of a transit vehicle involves not only knowledge of its current position and schedule adherence, but also traffic conditions along the remainder of the route. Road networks are dynamic and can quickly change from free‐flowing to highly congested, which impacts the arrival time of transit vehicles, particularly buses which often share the road with other vehicles, so reliable predictions need to account for real‐time and future traffic conditions. The first step in this process is to construct a framework with which road state (traffic conditions) can be estimated using real‐time transit vehicle position data. Our proposed framework implements a vehicle model using a particle filter to estimate road travel times, which are used in a second model to estimate real‐time traffic conditions. Although development and testing took place in Auckland, New Zealand, we generalised each component to make the framework compatible with other public transport systems around the world. We demonstrate the real‐time feasibility and performance of our approach in real‐time, where a combination of R and C++ was used to obtain the necessary performance results. Future work will use these estimated traffic conditions in combination with historical data to obtain reliable arrival time predictions of transit vehicles.     

Distribution of Exposure Concentrations and Doses for Constituents of Environmental Tobacco Smoke

The ultimate goal of the research reported in this series of three articles is to derive distributions of doses of selected environmental tobacco smoke (ETS)-related chemicals for nonsmoking workers. This analysis uses data from the 16-City Study collected with personal monitors over the course of one workday in workplaces where smoking occurred. In this article, we describe distributions of ETS chemical concentrations and the characteristics of those distributions (e.g., whether the distribution was log normal for a given constituent) for the workplace exposure. Next, we present population parameters relevant for estimating dose distributions and the methods used for estimating those dose distributions. Finally, we derive distributions of doses of selected ETS-related constituents obtained in the workplace for people in smoking work environments. Estimating dose distributions provided information beyond the usual point estimate of dose and showed that the preponderance of individuals exposed to ETS in the workplace were exposed at the low end of the dose distribution curve. The results of this analysis include estimations of hourly maxima and time-weighted average (TWA) doses of nicotine from workplace exposures to ETS (extrapolated from 1 day to 1 week) and doses derived from modeled lung burdens of ultraviolet-absorbing particulate matter (UVPM) and solanesol resulting from workplace exposures to ETS (extrapolated from 1 day to 1 year).     

从人力资源开发视角探讨教育对国家经济发展的作用

知识经济是当今世界经济发展的最明显特征,为适应知识经济时代的要求,充分发挥教育的主导作用,本文从人力资源是第一资源、保证培养人才的数量、质量以及人力资本投资等方面探讨了教育在国家经济发展中的重要性及作用。     

Increased risk of emergency department presentations for bronchiolitis in infants exposed to air pollution

Air pollution has been linked to an increased risk of several respiratory diseases in children, especially respiratory tract infections. The present study aims to evaluate the association between pediatric emergency department (PED) presentations for bronchiolitis and air pollution. PED presentations due to bronchiolitis in children aged less than 1 year were retrospectively collected from 2007 to 2018 in Padova, Italy, together with daily environmental data. A conditional logistic regression based on a time-stratified case-crossover design was performed to evaluate the association between PED presentations and exposure to NO₂, PM2.5, and PM10. Models were adjusted for temperature, relative humidity, atmospheric pressure, and public holidays. Delayed effects in time were evaluated using distributed lag non-linear models. Odds ratio for lagged exposure from 0 to 14 days were obtained. Overall, 2251 children presented to the PED for bronchiolitis. Infants’ exposure to higher concentrations of PM10 and PM2.5 in the 5 days before the presentation to the PED increased the risk of accessing the PED by more than 10%, whereas high concentrations of NO₂ between 2 and 12 days before the PED presentation were associated with an increased risk of up to 30%. The association between pollutants and infants who required hospitalization was even greater. A cumulative effect of NO₂ among the 2 weeks preceding the presentation was also observed. In summary, PM and NO₂ concentrations are associated with PED presentations and hospitalizations for bronchiolitis. Exposure of infants to air pollution could damage the respiratory tract mucosa, facilitating viral infections and exacerbating symptoms.     

基于MS-AR模型的中国能源消费周期路径演化识别

文章基于改革开放以来的煤炭、石油、天然气、一次电力及其他能源消费增长率年度数据,运用HP滤波和MS-AR模型,透析能源消费周期多阶段的区制属性,刻画我国能源消费增速的时间路径变化特征。结果表明:(1)除一次电力及其他能源消费增长率外,各能源消费增长率均处于窄幅震荡状态,21世纪初呈现明显的下行态势;煤炭、石油增长率波动程度明显强于天然气、一次电力及其他能源消费增长率波动程度。(2)各能源消费增长率存在低速增长区制、中速增长区制、高速增长区制的三区制特征;以煤炭、石油为代表的传统能源消费增长率在低速和高速增长区制间动态跃迁,仅在相关政策、重大改革方针出台后跃入高速增长区制,在中速增长区制具有持续性;天然气消费增长率在低速增长区制具有惰性;21世纪以来,一次电力及其他能源消费增长率维持在高增长区制。(3)区制特征与波动风险具有联动效应,高(低)增长区制具有高(低)波动风险。     

基于住宅过滤理论对广州城市廉租房建设模式的思考

受到政府资金供给不足影响,广州廉租房建设出现了政策覆盖面窄、廉租社区配套不足等问题。由住宅过滤理论对城市住房保障问题的启示,笔者认为广州不应再采用规模兴建新住宅的方式,而应该引导、促进市场的住房过滤,将市场过滤出的低质量住宅作为向低收入家庭提供廉租住宅的主要渠道。     

基于辅助粒子滤波的DSGE模型识别方法及应用

动态随机一般均衡模型中涵盖无法直接观测的变量,同时跨方程约束涉及复杂的非线性关系使方程的解析估计难以实现。在贝叶斯框架下识别动态随机一般均衡模型,基于状态空间方法建立度量方程和状态转移方程,采用辅助粒子滤波预测条件后验分布,建立贝叶斯误差带描述宏观经济变量脉冲响应函数的动态特征。实际数据分析验证了贝叶斯识别方法的有效性。     

On testes for threshold–type nonlinearity in irregulaly spaced time series

Formulating the model first in continuous time, we have developed a state space approach to the problem of testing for threshold-type nonlinearity when the data are irregularly spaced.