变结构面板协整检验的响应面函数研究

Panel-ρ检验是常用的异质面板数据协整检验统方法,本文通过Monte Carlo模拟分析Panel-ρ协整检验方法的小样本性质,以及该方法对于变结构面板协整检验的检验功效.结果表明,在小样本情况下Panel-ρ统计量在原假设下的渐进分布会不同于该统计量的理论渐进分布,而且面板数据中存在的结构变化也会对渐进分布产生影响,从而降低Panel-ρ检验的检验功效.为了得到更加符合实际样本情况的统计量临界值,通过Monte Carlo模拟方法得到Panel-ρ协整检验方法的响应面函数,建立了统计量的临界值与面板数据的样本容量、结构变化类型的直接函数关系.Monte Carlo模拟检验表明,响应面函数法确实能够改善Panel-ρ协整检验的检验功效,在小样本容量和具有结构变化的情况下保证了面板协整检验的有效性.     

基于两步子抽样算法的多目标抽样统计推断研究

针对海量数据,子抽样算法是当前一种流行的简化计算和降低计算成本的方法。现阶段的研究主要集中于单目标变量的估计上。多目标抽样也是现实生活中经常遇到的问题。本文提出基于广义线性模型,多目标抽样的均值两步子抽样算法。两步子抽样算法是Wang等(2018)^[1]提出的基于L-最优和A-最优的思想,确定每个抽样单元的入样概率。本文在此基础上,定义多目标抽样的各单元的入样概率,并推导模型参数估计量的渐近性质,最后用模拟数据和实际例子对均值两步子抽样算法和多目标两步子抽样方法进行比较。结果表明,在样本量相同时,A-最优准则下均值两步子抽样算法在估计精度上优于基于两步子抽样算法的MPPS抽样和L-最优准则下均值多目标两步子抽样算法。在计算效率上也较全样本估计有显著的提高,节约了计算时间。     

加速失效时间模型下关于广义病例队列抽样功效计算的一个注记（英文）

本文在加速失效时间模型下研究了广义病例队列抽样的功效计算问题.利用光滑加权Gehan估计方程方法估计了未知回归参数,研究了固定预算下广义病例队列抽样的功效计算.模拟研究和实际数据分析评估了提出方法在有限样本下的表现.     

混合截尾和定时截尾试验weibull分布抽样方案的比较

混合截尾试验是定时和定数截尾的一种有用的推广。本文研究了Ｗｅｉｂｕｌｌ分布和混合截尾试验的一次抽样方案,并对可靠决策损失函数给出了贝叶斯风险的显式表达式。比较陈和林的模型（１９９９）,我们得混合截尾试验的抽样方案于定时抽样方案。     

基于贝叶斯平滑转移模型的汇率非线性协整关系研究

针对平滑转移模型参数估计不确定性导致的协整检验方法相对复杂问题,提出基于平滑转移模型的贝叶斯非线性协整分析。通过模型的统计结构分析,选择参数先验分布,结合参数的后验条件分布特征设计Metropolis-Hasting-Gibbs混合抽样方案,据此估计平滑转移模型的参数,并对回归残差进行贝叶斯单位根检验,解决参数估计过程中遇到的参数估计不确定性及协整检验复杂的问题;利用人民币对美元汇率与中美两国的利率数据进行实证分析。研究结果表明:MH-Gibbs抽样方案能够有效估计平滑转移模型的参数,中美汇率波动和利差之间存在平滑转移协整关系。     

《方差估计引论》

书评:近年来,抽样调查在我国得到愈来愈广泛的应用,它的重要性和作用也日益被各方面所接受和认识。众所周知,抽样调查的主要目的是对总体目标量进行估计,而估计量的方差则是衡量抽样调查的精度,特别是有关抽样误差的一个重要标准。因此对估计量的方差进行控制与估计是抽样设计与分析中的一个十分重要的技术问题。一般的抽样调查教科书在介绍各种抽样方法时,虽然也涉及针对这种抽样方法的目标量估计的方差估计,但是由于实际中真正使用的抽样方案绝少只是一种抽样方法的单独使用,往往是多种方法的结合。根据这种实际抽样所得的复杂样本的方差…     

H∞控制的变分法与计算

通过将未来时段（t,tf]H∞状态反馈问题的泛函指标和过去时段[0,t)H∞滤波问题的泛函指标组合在一起,可以得到整个时段[0,tf]的H∞量测反馈问题的泛函指标,从而可以将变分法进一步用于研究H∞量测反馈问题。在过支的时段与未来时段的连接点,即当前时刻t,有两个连接条件：一个量估计状态x^-（t)必须是未来时段状态向量估计的初始条件,另一个关于协态向λ(t)的连接条件则可以由变分压原理导出。进一步的推证表明：最优参数γ^-2cr所满足的第三个条件依然是最小瑞利商。因此精细积分方法可以用于H∞量测反馈控制问题最优参数的计算,此前该方法已用来计算H∞状态反馈和H∞滤波问题中的最优参数γ^-2cr。     

用Gibbs抽样算法计算定数截尾时Weibull分布的贝叶斯估计

本文假设产品寿命服从两参数 Weibull分布 ,在定数截尾寿命试验的情况下 ,利用近年来发展起来的 Gibbs抽样算法 ,设计了计算参数贝叶斯估计的 Gibbs抽样方案 ,计算了一个实例并与经典的 BL UE和 BL IE估计结果进行比较。结果表明 ,较之传统的数值积分法 ,Gibbs抽样算法使用起来方便直接 ,更适合于计算可靠性指标的贝叶斯估计     

基于Koopman-Darmois分布族的序贯网图检验方法

通常高成本、破坏性的抽样检验需采用具有最大样本量限制的序贯检验方法．立足于设计出最大样本量尽量小的序贯检验方案,本文基于Koopman-Darmois分布族建立了序贯网图检验方法．与目前广泛采用的截尾序贯概率比检验方法相比,序贯网图检验方案具有更小的样本量上界,更适合高成本、破坏性的抽样检验．     

批发零售贸易业、餐饮业抽样调查方案及数据处理方法

本文介绍我国批发零售贸易业、餐饮业调查的抽样方案,方案以省为总体,区县为初级抽样单元,为满足对部分地市(域)估计的需要,对区县的抽样考虑了不放回样本追加。文中也给出了与抽样方案配套的总体和域目标量的估计及相应的方差估计公式。     

Multi-period forecasting and scenario generation with limited data

Data for optimization problems often comes from (deterministic) forecasts, but it is naïve to consider a forecast as the only future possibility. A more sophisticated approach uses data to generate alternative future scenarios, each with an attached probability. The basic idea is to estimate the distribution of forecast errors and use that to construct the scenarios. Although sampling from the distribution of errors comes immediately to mind, we propose instead to approximate rather than sample. Benchmark studies show that the method we propose works well.     

Dynamic pricing with real-time demand learning

In many service industries, the firm adjusts the product price dynamically by taking into account the current product inventory and the future demand distribution. Because the firm can easily monitor the product inventory, the success of dynamic pricing relies on an accurate demand forecast. In this paper, we consider a situation where the firm does not have an accurate demand forecast, but can only roughly estimate the customer arrival rate before the sale begins. As the sale moves forward, the firm uses real-time sales data to fine-tune this arrival rate estimation. We show how the firm can first use this modified arrival rate estimation to forecast the future demand distribution with better precision, and then use the new information to dynamically adjust the product price in order to maximize the expected total revenue. Numerical study shows that this strategy not only is nearly optimal, but also is robust when the true customer arrival rate is much different from the original forecast. Finally, we extend the results to four situations commonly encountered in practice: unobservable lost customers, time dependent arrival rate, batch demand, and discrete set of allowable prices.     

A new method for estimating the forecast quality with consideration for the errors of calculating the unknown parameters

In this study, the problem of estimating the forecast accuracy of a model is considered. A widespread practice is to approximate the population expectation of the forecast accuracy by the sample expectation, which is equivalent to the uniform consideration for the deviations of the forecast from the exact value of a quantity for all time moments. If the vector of unknown parameters is estimated at each step only from the preceding observations, the significance of the deviations is not the same at all time moments. In this study, we propose a method that takes into account the forecast errors with different weights. The problem of constructing the most accurate estimate of the forecast quality, a parameter from which the condition for the optimal weights can be derived, is formalized. Monte-Carlo experiments are used to compare the accuracy of the methods for estimating the forecast quality in the cases when the observations are taken into account with the same weights, with optimum weights, and with the weights calculated using a numerical procedure.     

A semi-parametric approach for estimating critical fractiles under autocorrelated demand

Forecasting critical fractiles of the lead time demand distribution is an important problem for operations managers making newsvendor-type inventory decisions. In this paper, we propose a semi-parametric approach to forecasting the critical fractile when demand is serially correlated. Starting from a user-defined but potentially misspecified forecasting model, we use historical demand data to generate empirical forecast errors of this model. These errors are then used to (1) parametrically correct for any bias in the point forecast conditional on the recent demand history and (2) non-parametrically estimate the critical fractile of the demand distribution without imposing distributional assumptions. We present conditions under which this semi-parametric approach provides a consistent estimate of the critical fractile and evaluate its finite sample properties using simulation and real data for retail inventory planning.     

Estimation of high-dimensional prior and posterior covariance matrices in Kalman filter variants

This work studies the effects of sampling variability in Monte Carlo-based methods to estimate very high-dimensional systems. Recent focus in the geosciences has been on representing the atmospheric state using a probability density function, and, for extremely high-dimensional systems, various sample-based Kalman filter techniques have been developed to address the problem of real-time assimilation of system information and observations. As the employed sample sizes are typically several orders of magnitude smaller than the system dimension, such sampling techniques inevitably induce considerable variability into the state estimate, primarily through prior and posterior sample covariance matrices. In this article, we quantify this variability with mean squared error measures for two Monte Carlo-based Kalman filter variants: the ensemble Kalman filter and the ensemble square-root Kalman filter. Expressions of the error measures are derived under weak assumptions and show that sample sizes need to grow proportionally to the square of the system dimension for bounded error growth. To reduce necessary ensemble size requirements and to address rank-deficient sample covariances, covariance-shrinking (tapering) based on the Schur product of the prior sample covariance and a positive definite function is demonstrated to be a simple, computationally feasible, and very effective technique. Rules for obtaining optimal taper functions for both stationary as well as non-stationary covariances are given, and optimal taper lengths are given in terms of the ensemble size and practical range of the forecast covariance. Results are also presented for optimal covariance inflation. The theory is verified and illustrated with extensive simulations.     

高速公路网络上行车时间估计和最优路线的选取

首先建立交通流动力学模型求解问题Ⅰ.在不考虑流量和考虑流量的两种情况下,该模型都能够解出在任意给定的时刻t位于第一个传感器的车辆到达第5个感应器的行车时间.我们还从四个方面给出了判断交通堵塞的衡量标准,并且利用神经网络方法准确地对未来的车流状态进行了预测.问题Ⅱ建立了交通网络的加权有向图模型,引入协方差矩阵描述网络中道路之间的相关性,并设计了查找最优路径的动态Dijkstra算法.问题Ⅲ构建了统计多目标规划模型,利用车比雪夫不等式,成功找到了从端点3到14和14到3的最优路径,并估算出了对应的行车时间.     

移动GSM网话务量的ARIMA模型的建立及其预测

本文用ARIMA模型对株洲移动GSM网的话务量进行了建模分析和预报，研究表明ARIMA模型不但适合株洲移动GSM网话务量的非平稳时间序列的特点，而且预测效果比较理想。结果表明，ARIMA（1，1，1）提供了较精确的预测结果，可以用来对未来几周的话务量进行预测，有一定的实际价值。     

成分数据的组合预测

成分数据是一类具有复杂性质的数据,特别是它的预测研究在管理学与经济学中占有很重要的地位.组合预测则是近年来在预测中应用比较广泛的一种方法,它能够充分利用单预测模型的信息,提高预测精度,增强预测的稳定性,且具有较高的适应能力.本文首次把组合预测方法应用到成分数据的预测分析中,基于成分数据的一些基本性质,利用组合预测得到了较好的预测结果.     

Incorporating Managerial Thinking in Prediction and Control: Case Study of Market Penetration

Managerial strategies, especially at the higher echelons of management, are often linguistically stated. This is because they need to be based on information which often defies quantification. Such verbal strategies and qualitative information have often been found to be difficult to incorporate in quantitative models. Thus, the quantitative effects of implementing one strategy as opposed to another have generally been difficult to forecast.In this paper, we show that, through the use of fuzzy logic, we can incorporate such qualitative (linguistically stated) information. Furthermore, we show that a fuzzy controller can be designed so as to reach desired goals while being cognizant of linguistically stated strategies, scenarios, and decision rules as well as quantitative data types.The approach is applied to the modeling and control of market penetration, a field which has attracted considerable attention in recent years.     

The use of forecast gradients in 3DVar data assimilation

In this paper, we propose an optimization approach for data assimilation by the use of forecast gradients. The proposed objective function consists of two data-fitting terms. The first term is based on the difference between the gradients of the forecast and the analysis, and the second term is based on the difference between the observations and the analysis in observation space. The motivation for using forecast gradients is that the forecast values provide an estimation of the system state, but they may not be accurate enough. We therefore propose to construct analysis gradients driven by the forecast gradients, instead of the forecast values. The associated data-fitting term can be interpreted by using the second-order finite difference matrix as the inverse of the background error covariance matrix in the 3DVar setting. In the proposed approach, it is not necessary to estimate the background covariance matrix and to deal with its inverse in the 3DVar algorithm. The existence and uniqueness of the analysis solution of the proposed objective function are established in this paper. The solution can be calculated by using the conjugate gradient method iteratively. Experimental results based on Community Multiscale Air Quality (CMAQ) and Weather Research and Forecasting (WRF) simulations are presented. We show in our air quality data assimilation experiment that the performance of the proposed method is better than that of the 3DVar method and the En3DVar method. The average improvements over the CMAQ simulation results for single-species NO₂, O₃, SO₂, NO, and CO are 18.9%, 34.0%, 22.2%, 4.3%, and 91.9%, respectively; and for the multiple-species PM2.5 and PM10, the improvements are 61.2% and 70.1%, respectively. In addition, the performance of the proposed method in temperature data assimilation is improved by 45.1% compared with the 3DVar method.