Error rates in quadratic discrimination with constraints on the covariance matrices

In multivariate discrimination of several normal populations, the optimal classification procedure is based on quadratic discriminant functions. We compare expected error rates of the quadratic classification procedure if the covariance matrices are estimated under the following four models: (i) arbitrary covariance matrices, (ii) common principal components, (iii) proportional covariance matrices, and (iv) identical covariance matrices. Using Monte Carlo simulation to estimate expected error rates, we study the performance of the four discrimination procedures for five different parameter setups corresponding to standard situations that have been used in the literature. The procedures are examined for sample sizes ranging from 10 to 60, and for two to four groups. Our results quantify the extent to which a parsimonious method reduces error rates, and demonstrate that choosing a simple method of discrimination is often beneficial even if the underlying model assumptions are wrong.The authors wish to thank the editor and three referees for their helpful comments on the first draft of this article. M. J. Schmid supported by grants no. 2.724-0.85 and 2.038-0.86 of the Swiss National Science Foundation.     

随机产出下的农产品供应链合同

本文设计了存在外部市场的二级农产品供应链,对农产品销货补货渠道进行分析,提出一种考虑外部市场的成本-收入共享合同.研究结果表明,供应商通过与加工企业共享丰收年超出订单量的额外收入,能有效激励加工企业增加订单量,提高双方利润;增添加工企业从外部市场进行补货的渠道能有效缓解供应商欠收的经济压力;考虑外部市场的成本-收入共享...     

Forecasting under model uncertainty: Non-homogeneous hidden Markov models with Pòlya-Gamma data augmentation

We consider finite state-space non-homogeneous hidden Markov models for forecasting univariate time series. Given a set of predictors, the time series are modeled via predictive regressions with state-dependent coefficients and time-varying transition probabilities that depend on the predictors via a logistic/multinomial function. In a hidden Markov setting, inference for logistic regression coefficients becomes complicated and in some cases impossible due to convergence issues. In this paper, we aim to address this problem utilizing the recently proposed Pólya-Gamma latent variable scheme. Also, we allow for model uncertainty regarding the predictors that affect the series both linearly — in the mean — and non-linearly — in the transition matrix. Predictor selection and inference on the model parameters are based on an automatic Markov chain Monte Carlo scheme with reversible jump steps. Hence the proposed methodology can be used as a black box for predicting time series. Using simulation experiments, we illustrate the performance of our algorithm in various setups, in terms of mixing properties, model selection and predictive ability. An empirical study on realized volatility data shows that our methodology gives improved forecasts compared to benchmark models.     

Long Memory of Financial Time Series and Hidden Markov Models with Time‐Varying Parameters

Hidden Markov models are often used to model daily returns and to infer the hidden state of financial markets. Previous studies have found that the estimated models change over time, but the implications of the time‐varying behavior have not been thoroughly examined. This paper presents an adaptive estimation approach that allows for the parameters of the estimated models to be time varying. It is shown that a two‐state Gaussian hidden Markov model with time‐varying parameters is able to reproduce the long memory of squared daily returns that was previously believed to be the most difficult fact to reproduce with a hidden Markov model. Capturing the time‐varying behavior of the parameters also leads to improved one‐step density forecasts. Finally, it is shown that the forecasting performance of the estimated models can be further improved using local smoothing to forecast the parameter variations. Copyright © 2016 John Wiley & Sons, Ltd.     

非下采样Contourlet HMT模型

非下采样Contourlet变换（NSCT）不仅具有类似传统小波变换和Contourlet变换的多尺度和多方向特性,而且还具有良好的平移不变特性．文中对NSCT变换系数进行研究,获得NSCT变换的方向子带系数同样具有持续性和聚集性的结论;在此基础上结合隐Markov树模型,建立了一种新的基于NSCT域的隐Markov树模型NSCT—HMT,并且给出了该模型的参数结构以及模型参数的训练方法;最后将所提出的模型应用于图像去噪中取得了很好的仿真效果．     

医疗服务供应链管理模型研究

在分析医疗服务特点的基础上,建立了基于信息平台的医疗服务供应链管理模型.其中,供应商、医院和患者通过信息平台进行有效沟通,进行需求管理、人力资源管理、医疗服务能力管理、供应商关系管理、医疗服务质量管理和医患关系管理,以及与卫生局、食品药品监督管理局、银行和政府等医疗服务供应链外部的联系.通过CR医院的实证分析,证明了基于信息平台的医疗服务供应链管理模型的有效性和实用性.     

基于稳态随机动力响应的蒙特卡洛模拟精度研究

以高斯白噪声作用下的单质点线性系统为例,探讨了系统稳态随机响应的蒙特卡洛模拟精度.结果表明,模拟的精度与样本数、样本值域分段数和系统参数的取值有关:样本数的选择应综合考虑计算精度与计算量,而合理选择分段数可以有效改善计算结果的精度.     

改进萤火虫算法及其收敛性分析

萤火虫算法因具有结构简单、控制参数少、易于实现的特点而得到广泛的关注和应用,但其易陷入局部最优导致过早收敛,从而影响寻优精度。针对这一问题,本文在位置更新规则中加入随机扰动因子,并剔除了冗余的随机项,以提高算法搜索能力;引入位置置换变异和差分进化算法中的最优变异策略,在保持种群多样性的同时,增强算法跳出局部最优的能力。采用马尔可夫过程证明了算法以概率1收敛到全局最优。利用基准函数和装箱问题对算法进行仿真测试,结果表明,改进后的算法能够有效跳出局部最优,对给出的所有问题均能找到理论最优解,寻优精度和成功率有明显提升。     

基于指标函数的舰载机机队回收调度优化研究

由于复飞逃逸、飞行故障等因素随机发生, 传统静态调度方法难以有效解决动态随机调度问题。对此,提出一种基于飞机优先序指标函数的蒙特卡罗模拟-差分进化搜索的实时调度算法。在离线模拟回收环境下,由算法完成基于随机模型的指标函数系数优化,后由该指标函数实现对机队优先序的实时评估排序,进而完成回收在线动态调度。仿真结果表明所提算法能有效解决回收调度问题。在相同机队初始输入下, 目标值呈现较好统计特性, 其期望值能够快速收敛到一定范围, 且无复飞情况下着舰时间窗目标呈显著正态性; 不同回收机队输入对指标系数最优值的散布较小, 表明指标函数评估优先序具有较好的通用性和有效性; 模拟回收调度发现, 着舰成功率的提高将显著降低机队复飞次数, 提高回收效率。     

基于词二元接续的中文文本自动查错研究

运用语料库语言统计方法对中文文中自动查错的有关问题进行探讨，运用词二元接续关系进行查错，主要依据词二元同现概率，互信息，t-测试差，其中，t-测试差是首次被应用于查错。