Bootstrap inference of the skew-normal two-way classification random effects model with interaction

In this paper,we consider the statistical inference problems for the fixed effect and variance component functions in the two-way classification random effects model with skewnormal errors.Firstly,the exact test statistic for the fixed effect is constructed.Secondly,using the Bootstrap approach and generalized approach,the one-sided hypothesis testing and interval estimation problems for the single variance component,the sum and ratio of variance components are discussed respectively.Further,the Monte Carlo simulation results indicate that the exact test statistic performs well in the one-sided hypothesis testing problem for the fixed effect.And the Bootstrap approach is better than the generalized approach in the one-sided hypothesis testing problems for variance component functions in most cases.Finally,the above approaches are applied to the real data examples of the consumer price index and value-added index of three industries to verify their rationality and effectiveness.

INFLUENCE ANALYSIS ON EXPONENTIAL NONLINEAR MODELS WITH RANDOM EFFECTS

This paper presents a unified diagnostic method for exponential nonlinear models with random effects based upon the joint likelihood given by Robinson in 1991. The authors show that the case deletion model is equivalent to mean shift outlier model. From this point of view, several diagnostic measures, such as Cook distance, score statistics are derived. The local influence measure of Cook is also presented. Numerical example illustrates that our method is available.

INFLUENCE ANALYSIS IN NONLINEAR MODELS WITH RANDOM EFFECTS

In this paper,a unified diagnostic method for the nonlinear models with random effects based upon the joint likelihood given by Robinson in 1991 is presented.It is shown that the case deletion model is equivalent to the mean shift outlier model.From this point of view,several diagnostic measures,such as Cook distance,score statistics are derived.The local influence measure of Cook is also presented. A numerical example illustrates that the method is available.

Estimation in Nonlinear Models with Random Effects

本文首次给出了非线性随机效应模型参数估计弱相合性的必要条件，并证明了在一定正则条件下估计的强相合性和渐近正态性，推广了ＢｒｅｓｌｏｗａｎｄＣａｌｙｔｏｎ（１９９３），ＬｅａｎｄＮｅｌｄｅｒ（１９９６）的结果．

A combined random effect and fixed effect forecast for panel data models

When some of the regressors in a panel data model are correlated with the random individual effects,the random effect(RE)estimator becomes inconsistent while the fixed effect(FE)estimator is consistent.Depending on the various degree of such correlation,we can combine the RE estimator and FE estimator to form a combined estimator which can be better than each of the FE and RE estimators.In this paper,we are interested in whether the combined estimator may be used to form a combined forecast to improve upon the RE forecast(forecast made using the RE estimator)and the FE forecast(forecast using the FE estimator)in out-of-sample forecasting.Our simulation experiment shows that the combined forecast does dominate the FE forecast for all degrees of endogeneity in terms of mean squared forecast errors(MSFE),demonstrating that the theoretical results of the risk dominance for the in-sample estimation carry over to the out-of-sample forecasting.It also shows that the combined forecast can reduce MSFE relative to the RE forecast for moderate to large degrees of endogeneity and for large degrees of heterogeneity in individual effects.

Hazard Model with Random Treatment Time-Lag Effect

In clinical studies,it is often that the medical treatments take a period of time before having an effect on patients and the delayed time may vary from person to person.Even though there exists a rich literature developing methods to estimate the time-lag period and treatment effects after lag time,most of these existing studies assume a fixed lag time.In this paper,we propose a hazard model incorporating a random treatment time-lag effect to describe the heterogeneous treatment effect among subjects.The EM algorithm is used to obtain the maximum likelihood estimator.We give the asymptotic properties of the proposed estimator and evaluate its performance via simulation studies.An application of the proposed method to real data is provided.

Double Penalized Quantile Regression for the Linear Mixed Effects Model

This paper proposes a double penalized quantile regression for linear mixed effects model,which can select fixed and random effects simultaneously.Instead of using two tuning parameters,the proposed iterative algorithm enables only one optimal tuning parameter in each step and is more efficient.The authors establish asymptotic normality for the proposed estimators of quantile regression coefficients.Simulation studies show that the new method is robust to a variety of error distributions at different quantiles.It outperforms the traditional regression models under a wide array of simulated data models and is flexible enough to accommodate changes in fixed and random effects.For the high dimensional data scenarios,the new method still can correctly select important variables and exclude noise variables with high probability.A case study based on a hierarchical education data illustrates a practical utility of the proposed approach.

Dynamic thermo-mechanical coupled response of random particulate composites:A statistical two-scale method

This paper focuses on the dynamic thermo-mechanical coupled response of random particulate composite materials. Both the inertia term and coupling term are considered in the dynamic coupled problem. The formulation of the problem by a statistical second-order two-scale （SSOTS） analysis method and the algorithm procedure based on the finite-element difference method are presented. Numerical results of coupled cases are compared with those of uncoupled cases. It shows that the coupling effects on temperature, thermal flux, displacement, and stresses are very distinct, and the micro- characteristics of particles affect the coupling effect of the random composites. Furthermore, the coupling effect causes a lag in the variations of temperature, thermal flux, displacement, and stresses.

基于长期气温模拟的混凝土坝变形预测混合模型

混凝土坝变形预测模型通常采用谐波函数模拟温度效应,但没有考虑不同年份坝体温度变化对混凝土坝变形的影响。针对此问题,利用有限元模型(FEM)计算水压分量,利用长期气温序列代替谐波函数计算温度分量。考虑混凝土坝变形过程的非线性特征,引入麻雀搜索算法(SSA)对随机森林回归(RFR)模型的参数进行优化,获取最优参数对混合模型进行训练,最终构建一种基于长期气温进行温度效应模拟的混凝土坝变形预测混合模型。结果表明,相较于RFR变形预测模型和基于统计学回归方法的变形预测模型,采用SSA优化RFR的变形预测混合模型具有更好的拟合效果和预测能力。

基于优化随机森林的对地攻击无人机自主作战效能评估

为实现高效、快速、客观地对对地攻击无人机自主作战效能进行评估,文中引入向量加权平均算法(Weighed Mean of Vectors Algorithm,INFO)和K折交叉验证方法对随机森林算法(Random Forest,RF)进行优化寻找最优参数组合,提出了基于优化随机森林的对地攻击无人机自主作战效能评估方法。首先,基于向量加权平均优化算法理论,对随机森林决策树模型数量以及最大深度两项超参数进行寻优。其次,结合对地攻击无人机作战任务,对自主作战效能评估的主要作战因素进行分析,归纳总结了对地攻击无人机自主作战效能评估指标体系,并建立了基于INFO-RF的无人机自主作战效能评估模型。最后,通过对评估模型进行实例验证并与其他方法进行对比分析,结果表明,相较于传统RF模型、GA-RF模型和SVM模型,INFO-RF模型输出结果具有较高的拟合度和更为精确的评估值,实例结果有效验证了所提方法的合理性和优化模型的可靠性。

合理进行均值比较——重复测量设计定量资料方差分析

本文目的是介绍与混合模型有关的5个基本概念、计算方法、一个临床试验实例及其SAS实现。基本概念包括固定效应、随机效应、随机系数、混合模型和多水平模型。计算方法涉及混合模型的经典计算方法和贝叶斯计算方法。临床试验实例涉及三种药物对哮喘患者呼吸功能的影响。本文给出了采用SAS处理实例中定量资料的全过程,包括基于mixed过程和bglimm过程构建6个重复测量设计定量资料混合模型,对输出结果作出了解释,并基于模型拟合效果评价统计量,对所构建的多个混合模型进行了比较,得出了适合本例资料的最优混合模型。

基于随机森林算法的现代建筑工程施工过程监理效果影响因素分析

文中以某现代建筑工程为背景,结合监理特点,选择施工过程质量监理、安全监理、进度监理以及投资监理作为监理效果影响因子,并提出假设。以监理效果作为因变量,以选择的影响因子作为自变量,利用随机森林算法建立分析模型,并对模型开展训练,通过模型迭代计算,对提出的假设进行验证,并确定自变量对因变量的重要性。经模型验证,监理效果主要受施工过程质量监理、安全监理、进度监理以及投资监理影响,其中,安全监理对整个施工过程监理效果影响最大。

Multi-item EPQ model with learning effect on imperfect production over fuzzy-random planning horizon

Uncertainty is certain in the world of uncertainty.This study revisits an economic production quantity(EPQ)model with shortages for stock-dependent demand of the items with reworking and disposing of the imperfect ones over a random planning horizon under the joint effect of inflation and time value of money,where the expected time length is imprecise in nature.Transmission of learning effect has been incorporated to reduce the defective production.The total expected profit over the random planning horizon is maximized subject to the imprecise space constraint.The possibility,necessity and credibility measures have been introduced to defuzzify the model.The simulation-based genetic algorithm is used to make decision for the above EPQ model in different measures of uncertainty.The model is illustrated through an example.Sensitivity analysis shows the impacts of different parameters on the objective function in the model.

ON EFFECTIVE STOCHASTIC GALERKIN FINITE ELEMENT METHOD FOR STOCHASTIC OPTIMAL CONTROL GOVERNED BY INTEGRAL-DIFFERENTIAL EQUATIONS WITH RANDOM COEFFICIENTS

In this paper, we apply stochastic Galerkin finite element methods to the optimal control problem governed by an elliptic integral-differential PDEs with random field. The control problem has the control constraints of obstacle type. A new gradient algorithm based on the pre-conditioner conjugate gradient algorithm （PCG） is developed for this optimal control problem. This algorithm can transform a part of the state equation matrix and co-state equation matrix into block diagonal matrix and then solve the optimal control systems iteratively. The proof of convergence for this algorithm is also discussed. Finally numerical examples of a medial size are presented to illustrate our theoretical results.

考虑个体差异效应的高拱坝测点群变形监控

针对水压-温变-时效(hydraulic-seasonal-time,简称HST)模型监控高拱坝变形的不足,以变形规律相似的测点群为对象,搭建一种新的监控模式。首先,依据面板数据格式,建立水压、温变和时效面板模型;其次,引入固定效应(fixed effect,简称FE)和随机效应(random effect,简称RE)表征个体差异效应,据此构建HST-FE/RE面板模型;然后,通过建立置信椭球,借助典型小概率原理,提出测点群变形监控准则;最后,结合某实际工程,检验方法的可行性和有效性。结果显示:HST-FE/RE复相关系数及剩余标准差分别为99.875%和0.01634,拟合性能比HST更优;HST-FE/RE变形预测相对误差波动平稳,最大值为3.28%,精度高于HST;置信椭球准则对典型小概率变形的辨识比置信区间准则严格。相较于常规方法,本方法性能提升较大,更适用于高拱坝变形监控。

传统meta分析统计模型的合理使用

meta分析是一种用于合并多个独立研究发现、结果和数据的常用统计学技术。在传统meta分析过程中选择合适的统计模型非常重要,但在实践中研究者常常会误用统计模型。本文主要介绍传统meta分析统计模型的新观点,列举国内外期刊上已发表的文献中正确或错误选择模型的情形,并给出合理使用模型的推荐建议和依据。

空间面板数据模型设定问题分析

空间面板数据模型将空间计量经济学和面板数据方法相结合,不仅同时考虑时空特征,而且将空间效应纳入研究体系,成为当前计量经济学的热点研究领域,但其模型设定、参数估计及模型检验也更为复杂,实证研究中往往出现模型设定偏误等问题。因此,基于空间面板数据模型的前沿理论,重点探讨模型设定中的常见问题,包括空间滞后模型与空间误差模型的选择、随机效应与固定效应的选择以及模型拟合优度的选择与比较,为模型的应用和新模型的扩展提供理论依据和参考。

用Mixed和Nlmixed过程建立混合生长模型

本文用5块不同密度样地的树高生长资料,根据线性和非线性混合模型理论,利用SAS的Mixed过程和Nlmixed过程,分别拟合线性混合模型和非线性混合树高生长模型。根据预测值和固定效应同时绘制出不同密度下的高生长曲线和平均高生长曲线,充分显示了混合模型的优势,即它可以同时反映总体的平均变化趋势和个体之间的差异。

基于非线性混合模型的落叶松树干削度模型

以黑龙江省带岭林业局大青川林场84株人工落叶松解析木数据为例,采用Max和Burkhart分段削度模型作为基础模型,利用SAS软件中的似乎不相关回归过程得到该分段削度模型的4个参数和2个拐点参数同时估计。参数估计显著性检验(P<0.000 1)以及模型检验(F=31 392.30,P<0.000 1)都证明该分段模型能较好地描述落叶松树干干形变化。然后以该分段模型为基础模型,采用非线性混合模型的方法,建立落叶松人工林树干削度混合效应模型。结果表明:当考虑样地效应影响时,b1,b2同时作为混合参数时模型拟合最好;当考虑树木效应影响时,b2,b4同时作为混合参数时模型拟合最好。无论考虑样地效应影响还是考虑树木效应影响,混合模型的拟合精度都比基本模型的拟合精度高,并且考虑树木效应影响要比考虑样地效应影响的精度更高。模型检验结果表明:混合模型通过校正随机参数值能提高模型的预测精度。

基于Panel Data的高速公路事故预测模型

使用Panel Data模型进行不同路段交通事故的统计回归,可以识别路段样本间的固有差异以及未观测到的变量影响.作者介绍了个体固定效应模型和随机效应模型的建立过程和相关检验,并以京津塘高速为例,分别建立了一般混合回归模型、个体固定效应模型和随机效应模型,通过Hausman检验比较模型效果,最终得出个体固定效应模型更加合理、适合于高速公路事故分析的结论.