核主成分遗传算法与SVR选股模型改进

 量化选股一直是金融领域研究的热点。随着人工智能技术的空前发展,量化选股方法取得了很大进步。本文构建了基于核主成分遗传算法改进的支持向量回归机人工智能选股模型(KPCA-GA-SVR),并基于沪深股市股票基本面及交易数据,分别从短期和中长期对其选股性能和预测精度进行了实证分析。主要结论为：①遗传算法(GA)改进的SVR较传统模型预测精度更高,且避免了过度拟合;②与采用主成分降维技术的PCA-GA-SVR模型相比,基于核主成分特征提取的KPCA-GA-SVR模型,具有更好模型稳健性及预测准确性;③中长期内该模型的预测误差随滑窗长度的增加有降低趋势,且一年期预测精度最高;短期内不同滑窗下,一周的预测效果最佳。本研究对个人投资者的投资决策及国家宏观监控股市动态变化都具积极意义。     

Least Median of Squares Solution of Multiple Linear Regression Models Through the Origin

Barreto and Maharry (2006 Barreto , H. , Maharry , D. ( 2006 ). Least median of squares and regression through the origin . Comput. Statist. Data Anal. 50 : 1391 – 1397 .[Crossref], [Web of Science ®] , [Google Scholar]) showed that PROGRESS algorithm fails to find a correct minimum “Least Median of Squares/LMS” estimate for bivariate regression models which have no intercept. Kayhan and Gunay (2008 Kayhan , Y. , Gunay , S. ( 2008 ). A new approach to least median of squares and regression through the origin . Commun. Statist. Theor. Meth. 37 ( 5 ): 773 – 781 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) presented a different approach for the regression models through the origin which includes at most two unknown parameters. However, LMS estimate for multiple linear regression models still remains an open issue. The aim of this study is to show that finding true LMS estimate for zero intercept multiple linear regression models can be treated as a convex optimization problem and to provide a more general algorithm for any dimensional linear regression models.     

Bayesian Versus Frequentist Approach of the Frailty Cox Model,Application to Calf Gastroenteritis

In the frailty Cox model, frequentist approaches often present problems of numerical resolution, convergence, and variance calculation. The Bayesian approach offers an alternative. The goal of this study was to compare, using real (calf gastroenteritis) and simulated data, the results obtained with the MCMC method used in the Bayesian approach versus two frequentist approaches: the Newton–Raphson algorithm to solve a penalized likelihood and the EM algorithm. The results obtained showed that when the number of groups in the population decreases, the Bayesian approach gives a less biased estimation of the frailty variance and of the group fixed effect than the frequentist approaches.     

基于混合算法的具有硬时间窗口约束的VRPSPD问题

具有硬时间窗口约束下同时收发的车辆路径问题（vehicle routing problem with simultaneous pick-up and delivery with hard time windows,VRPSPD with Hard TW）是将产品配送和废物回收统一进行优化的问题。本文研究了一个中心仓库,多台车辆向具有配送需求和回收需求的客户提供服务,客户存在硬时间窗口要求的车辆运输问题。该问题中,客户的配送需求和回收需求必须同时获得满足,即客户仅允许被访问一次,且需求不可分割,只能由一台车辆提供服务。且客户的硬时间窗口要求必须获得满足。首先将修正后的旅程分割方法应用于硬时间窗口约束下的VRPSPD问题初始解的获得,并利用响应性禁忌搜索算法框架,结合基于多种领域的可变式搜索方法,给出一启发式算法。计算机实验结果表明,该启发式算法在求解VRPSPD with hard TW上是有效的。     

混合指数几何分布

本文讨论元件的寿命分布服从双参数混合指数分布,元件个数服从几何分布的情形下,定义了混合指数几何分布,并研究了该分布的各种性质,讨论了参数的极大似然估计,并使用EM算法得到参数的近似估计. 最后,本文给出了参数的渐近方差、协方差、置信区间.     

基于CBR的化妆品销售组合预测模型

预测是销售企业面临的至关重要的问题,预测精度会直接影响到企业的利润及长远发展。考虑到化妆品销售独特的生命周期特点,提出基于案例推理技术的化妆品销售组合预测模型。基于案例推理技术能够弥补短生命周期产品数据不足的问题,根据"最相似"产品的数据对目标产品属性进行推理计算,既提高了预测精度,又便于实际操作。在此基础上,提出了改进的BASS模型及季节性趋势增长模型,充分考虑数据序列波动的季节性、趋势性及其完整的生命周期特征。为了进一步提高预测精度,提出AFTER-GA组合预测算法,并添加PSOWv-SVM模型作为组合预测的模型之一,利用模型处理外界影响因素的能力,使得各模型取长补短,更有效地突出各模型的优势,从而取得更精确的预测结果。实例分析表明,AFTER-GA组合预测算法在精度上要明显优于其他方法;基于CBR的组合预测模型在化妆品销售预测方面,能够达到令人满意的精度要求,具有实际应用价值。     

基于蝙蝠算法SVR模型的北京市二手房价预测研究

传统SVR模型可预测房价变化趋势,但不恰当的参数设置会影响预测的精度。本文针对北京二手房同比价格指数的非线性变化特征,将蝙蝠算法(BatAlgorithm,BA)引入到SVR模型中,使其对模型的三个参数进行优化设置,结合网络搜索数据(Web Search Data,WSD),构建了BA-SVR&WSD混合模型,并给出了该模型算法的预测流程,通过引入多个基准预测模型和预测性能度量指标进行对比研究。研究结果表明：基于蝙蝠算法的SVR模型的具有较好的泛化能力、预测效果更准确且预测精度更高,该预测方法也为北京二手房价格的监测和调控提供有价值的参考。     

交通路网中最优路径算法的道路权重选择

在交通路网中,寻找任意两点间最优路径是出行导航的基本功能.除了最优路径算法自身性能外,道路权重的选择也直接决定了寻径结果的优劣.现有最优路径算法通常以通行能力为道路权重,其可能导致不合理的寻径结果,同时也不具有全局负载均衡的能力.因此本文以Dijkstra算法为例,引入可达性概念作为道路权重,从而弥补以通行能力为道路权重的缺陷.     

一个新的银行信用风险评估方法

本文以银行信用风险管理为例,将粗糙集和决策树两种具有互补优势的数据挖掘方法相结合,对客户信用做出归类分析判断,最后利用决策树生成决策规则.实践证明,这种方法忠于原始数据,提高了分类准确度,减小了决策树规模,具有良好的性能.     

A pseudo restricted MLE under multivariate order restrictions and its algorithm

Abstract

It is shown in this paper that a quasi order for the vectors in R^p is a cone induced if and only if the order is preservable under limits and under linear combinations with non-negative coefficients. For the mean vectors in MANOVA subject to the restriction of simple ordering, a pseudo restricted MLE is proposed. This estimator is a matrix projection onto a closed convex set inside the restricted domain. An algorithm for the pseudo restricted MLE is developed, that computes the matrix projections using only vector projections.