决策支持系统的自适应建模研究及应用实例分析

在进行决策的过程中 ,建立正确的系统模型是解决问题的关键 .由于多方面的原因 ,有时要针对研究对象选用适用的模型来进行分析是很困难的 .本文根据遗传算法在程序设计中的应用 ,探讨了基于遗传算法的自适应建模方法 ,并对实际应用中自适应建模的算法存在的问题提出了改进方法 .最后应用自适应建模方法对全国的集装箱与外贸数据进行了建模分析 ,应用实例表明自适应建模方法对于解决难以选择合适的模型进行建模分析时是很有效的     

Books on probability ideas are reviewed

In this paper, semiparametric methods are applied to estimate multivariate volatility functions, using a residual approach as in [J. Fan and Q. Yao, Efficient estimation of conditional variance functions in stochastic regression, Biometrika 85 (1998), pp. 645–660; F.A. Ziegelmann, Nonparametric estimation of volatility functions: The local exponential estimator, Econometric Theory 18 (2002), pp. 985–991; F.A. Ziegelmann, A local linear least-absolute-deviations estimator of volatility, Comm. Statist. Simulation Comput. 37 (2008), pp. 1543–1564], among others. Our main goal here is two-fold: (1) describe and implement a number of semiparametric models, such as additive, single-index and (adaptive) functional-coefficient, in volatility estimation, all motivated as alternatives to deal with the curse of dimensionality present in fully nonparametric models; and (2) propose the use of a variation of the traditional cross-validation method to deal with model choice in the class of adaptive functional-coefficient models, choosing simultaneously the bandwidth, the number of covariates in the model and also the single-index smoothing variable. The modified cross-validation algorithm is able to tackle the computational burden caused by the model complexity, providing an important tool in semiparametric volatility estimation. We briefly discuss model identifiability when estimating volatility as well as nonnegativity of the resulting estimators. Furthermore, Monte Carlo simulations for several underlying generating models are implemented and applications to real data are provided.     

Double shrinkage estimators for large sparse covariance matrices

Covariance matrices play an important role in many multivariate techniques and hence a good covariance estimation is crucial in this kind of analysis. In many applications a sparse covariance matrix is expected due to the nature of the data or for simple interpretation. Hard thresholding, soft thresholding, and generalized thresholding were therefore developed to this end. However, these estimators do not always yield well-conditioned covariance estimates. To have sparse and well-conditioned estimates, we propose doubly shrinkage estimators: shrinking small covariances towards zero and then shrinking covariance matrix towards a diagonal matrix. Additionally, a richness index is defined to evaluate how rich a covariance matrix is. According to our simulations, the richness index serves as a good indicator to choose relevant covariance estimator.     

Adaptive designs for single‐arm phase II trials in oncology

Clinical phase II trials in oncology are conducted to determine whether the activity of a new anticancer treatment is promising enough to merit further investigation. Two‐stage designs are commonly used for this situation to allow for early termination. Designs proposed in the literature so far have the common drawback that the sample sizes for the two stages have to be specified in the protocol and have to be adhered to strictly during the course of the trial. As a consequence, designs that allow a higher extent of flexibility are desirable. In this article, we propose a new adaptive method that allows an arbitrary modification of the sample size of the second stage using the results of the interim analysis or external information while controlling the type I error rate. If the sample size is not changed during the trial, the proposed design shows very similar characteristics to the optimal two‐stage design proposed by Chang et al. (Biometrics 1987; 43:865–874). However, the new design allows the use of mid‐course information for the planning of the second stage, thus meeting practical requirements when performing clinical phase II trials in oncology. Copyright © 2012 John Wiley & Sons, Ltd.     

A general inverse sampling scheme and its application to adaptive cluster sampling

Not having a variance estimator is a seriously weak point of a sampling design from a practical perspective. This paper provides unbiased variance estimators for several sampling designs based on inverse sampling, both with and without an adaptive component. It proposes a new design, which is called the general inverse sampling design, that avoids sampling an infeasibly large number of units. The paper provide estimators for this design as well as its adaptive modification. A simple artificial example is used to demonstrate the computations. The adaptive and non‐adaptive designs are compared using simulations based on real data sets. The results indicate that, for appropriate populations, the adaptive version can have a substantial variance reduction compared with the non‐adaptive version. Also, adaptive general inverse sampling with a limitation on the initial sample size has a greater variance reduction than without the limitation.     

On the improvement of one-sided S control charts

The most common charting procedure used for monitoring the variance of the distribution of a quality characteristic is the S control chart. As a Shewhart-type control chart, it is relatively insensitive in the quick detection of small and moderate shifts in process variance. The performance of the S chart can be improved by supplementing it with runs rules or by varying the sample size and the sampling interval. In this work, we introduce and study one-sided adaptive S control charts, supplemented or not with one powerful runs rule, for detecting increases or decreases in process variation. The properties of the proposed control schemes are obtained by using a Markov chain approach. Furthermore, a practical guidance for the choice of the most suitable control scheme is also provided.     

空间数据分析的发展

空间统计学是研究空间问题的一门学科,它是应用数学快速发展的一个分支。尽管传统的数据分析中有许多很好的方法,但却不能完全地套用于空间数据的分析。空间模型的估计不仅与各种回归形式的假设有关,而且还与空间相关、空间异质的特性有关。从空间模型及推断、适应性估计、非参数回归、空间不相关性检验几个方面研究了空间数据分析方法的发展以及未来的趋势。     

基于复杂网络理论的供应链网络研究综述

本文介绍了供应链网络具有复杂网络特征的认识发现过程、供应链网络的复杂网络属性及其具体内涵、基于复杂网络理论的供应链建模技术拓展和重构、基于复杂网络理论的供应链网络行为和现象专题研究、相关实践应用研究,并对未来研究方向提出了建议。     

EMDR、适应性信息加工与个案概念化(下)

EMDR是一个整合性的、以咨客为中心的心理治疗方法。该方法用于治疗那些不是由器质性损伤为病理学基础的心理问题,它的治疗重点是处理大脑的信息加工系统中的痛苦记忆。EMDR既能处理那些引起困扰的经历和体验,也能帮助咨客获得达到更健康的心理状态所需要的经历和体验。综览EMDR的历史、发展和研究,它是一个主要以经验为支持的治疗方法。本文在阐述适应性信息加工模型之后,用一个延续的个案实例,来说明EMDR的个案概念化和8个阶段的治疗。EMDR技术主要是加工那些有病理学基础的早期记忆和引发不良功能的当前情境,并为未来适应性的行为提供模板,包括克服技能不足和(或)发展性的不足所需的知识与行为。整合的EMDR与家庭系统观点的好处是可以提供最综合的治疗效果。     

基于EMD的非线性信号自适应分析

根据EMD分解原理,提出通过选择平均包络中的突变点确定自适应滤波器截止分量阶,以此构造时空滤波器组进行非线性信号自适应分析的方法,成功应用于分析波音737飞机涡轮轴振动信号。从噪声中自适应地提取随转速变化的信号分量和低频振动分量,与小波分析方法相比,在轴向位移检测中取得了更为满意的效果。该方法不仅改进了涡轮轴振动信号分析方法,也适用于解决其他非线性信号的分析问题。