基于分位点回归系数聚类的时间序列分类方法

时间序列曲线分类的目的是为了找到曲线之间相似波动结构、减少建模工作量和进行预测,所以分类的结果将直接影响模型的质量和预测的精度.为此,文章提出了一种新的时序曲线分类方法-分位点回归系数聚类法.它可以有效地避免一些分类方法带来的局限性,能够更为全面、详尽地考查待分类时序数据的运行方式,改善分类的效果并为预测提供强大的支持.     

二次指数修匀法在经济决策中的作用

二次指数修匀法在经济决策中的作用预测对企业来说是运用科学的方法，定性或定量地估计产品销售未来的发展趋势；从而给企业提供是否继续经营这种产品的决策方案，因而，预测是决策的依据和编制计划的根据。指数修匀法，是根据过去的实际数和预测数，通过加权平均，来预测...     

景气指标分类方法的理论研究与实证

景气指标分类是景气预测法中至关重要的环节.在以往的文献中,一般采用一种方法对备选指标进行景气分类,但其结果还存在优化的空间.文章采用四种典型的景气指标分类方法:峰谷对应法、时差相关分析法、K-L信息量法、灰色关联度指标分类法对同样的多个指标进行分类.按照初定方案对四种方法的结果进行综合分析,对结果进行优化调整,应用实例表明:四种方法的优化调整结果比单一方法的结果更合理、更有效、更具有科学性.     

AID法在回归分析中的应用

一、问题的提出回归预测的一个基本前题，是样本期数据比较均匀地分布在一条曲线周围，如果有些偏离回归曲线的“特异值”，并不是由随机因素的影响造成的，此时，很可能产生较大的误差，而多元统计的聚类分析中有许多适用于此情况的预测方法，其中AID法就是一种较为简便易行的方法。二、AJD预测法AID方法的基本思想是依据最优分割的原则，对有序样本进行合理分类，使划分后的各类离差平方和达到极小。然后再根据预测值所属的类进行预测。AID方法的关键是如何分类，为方便起见设样本期有n牟观察值为lx，y；，i－1，2…，n卜x，v；均为…     

主观预测与定量预测的结合应用

主观预测是根据人们长期积累的经验，对未来经济活动中经济变量的变化作出主观估计．其优越性是使用方便、容易掌握，不需复杂的数学运算．而定量预测法是根据历史数据，按一定的数学模型推导出预测值的方法。定量预测和定性预测方法各有其优点和不足，二者相结合，对同一经济现象预测，其关健在于对两种预测结果的处理上，作者认为可以采用常用的简单算术平均数或加权算术平均数对两种结果求得平均值，作为预测值，其公式为：A一（AI＋A。）／2（A为预测值，AI为主观预测值，A。为定量预测值）或A一（K入十几人）／（KI十几）（尼为主…     

基于Shapley值的分类预测模型变量筛选方法改进

在分类预测模型的自变量间存在交互效应时，传统Shapley值法的可加性无法满足，造成变量筛选效果变差，导致分类模型的预测精度降低。针对此问题，文章提出使用稳健独立成分分析，从原始数据中估计出具有独立性的数据集并对其进行Shapley值分解，从而提高变量筛选的准确度。统计模拟与实证分析的结果表明，改进后的方法在变量筛选上的表现优于传统Shapley值法。     

系统安全数据预测方法综述

据不完全统计,现有的各类预测方法达300种之多,而通常用于系统安全数据预测的方法主要有回归分析法、德尔菲法、趋势外推法、马尔可夫预测、齐次泊松过程模型、指数平滑线、残差辩识法、模型法和灰色预测法等。这些预测方法可分成3类:前5种是统计型的;指数平滑与残差辩识属递推型;灰色预测与模型法则属于连续型。现就这几种预测方法作一简要评述。一、统计型的预测方法1、回归分析法这是一种传统的分析、预测方法,长期以来作为一种经典方法而广泛应用,且种类较多。在系统安全数据的预测上,目前运用较多的为单元线性回归和单元指数回归。由于…     

五种常用系统聚类分析方法及其比较

一、系统聚类分析概述聚类分析是研究如何将对象按照多个方面的特征进行综合分类的一种统计方法[1]。然而在以往的分类学中,人们主要靠经验和专业知识作定性分类处理,许多分类不可避免地带有主观     

两种数据挖掘技术在预测顾客满意度中的对比研究

分类发现是数据挖掘的重要内容,贝叶斯分类和决策树在数据挖掘中应用相当广泛,它们是生成分类器的两种有效方法。文章分别用两种方法对顾客满意度进行分类及预测,并将两种方法进行比较分析,认为用决策树分类法来预测顾客满意度具有简洁、高效等特点。     

数说天下

注：1、IMF公布的世界及分类数据按照购买力平价方法进行汇总，世界银行和ConsensusForecasts按汇率法进行汇总。2、2012年和2013年为预测数。3、印度来源于ConsensusForecasts的数据指财政年度。     

统计数据质量评估方法研究述评

数据质量评估是统计数据质量管理的重要环节。统计数据质量的评估方法有逻辑关系检验法、计量模型分析法、核算数据重估法、统计分布检验法、调查误差评估法以及多维评估法六个类别,在详细讨论其各自的评估过程、适用性及其优缺点的基础上,按照评估维度和评估方式对各种统计数据质量的评估方法进行再归类,指出评估方法的进一步研究应该围绕计量模型分析法、核算数据重估法中的物量指数重估法、调查误差评估法和多维评估法等方向展开。     

A comparison of statistical methods for combining relative bioactivities from parallel line bioassays

This paper compares the ordinary unweighted average, weighted average, and maximum likelihood methods for estimating a common bioactivity from multiple parallel line bioassays. Some of these or similar methods are also used in meta‐analysis. Based on a simulation study, these methods are assessed by comparing coverage probabilities of the true relative bioactivity and the length of the confidence intervals computed for these methods. The ordinary unweighted average method outperforms all statistical methods by consistently giving the best coverage probability but with somewhat wider confidence intervals. The weighted average methods give good coverage and smaller confidence intervals when combining homogeneous bioactivities. For heterogeneous bioactivities, these methods work well when a liberal significance level for testing homogeneity of bioactivities is used. The maximum likelihood methods gave good coverage when homogeneous bioactivities were considered. Overall, the preferred methods are the ordinary unweighted average and two weighted average methods that were specifically developed for bioassays. Copyright © 2013 John Wiley & Sons, Ltd.     

A comparative study of smoothing procedures for ordered categorical data

Methods for estimating probabilities on sample spaces for ordered-categorical variables are surveyed. The methods all involve smoothing the relative frequencies in manners which recognise the ordering among categories. Approaches of this type include convex smoothing, weighting-function and kernel-based methods, near neighbour methods, Bayes-based methods and penalized minimum-distance methods. The relationships among the methods are brought out, application is made to a medical example and a simulation study is reported which compares the methods on univariate and bivariate examples. Links with smoothing procedures in other contexts are indicated.     

Estimating the number of factors to include in a high-dimensional multivariate bilinear model

We present two new statistics for estimating the number of factors underlying in a multivariate system. One of the two new methods, the original NUMFACT, has been used in high profile environmental studies. The two new methods are first explained from a geometrical viewpoint. We then present an algebraic development and asymptotic cutoff points. Next we present a simulation study that shows that for skewed data the new methods are typically superior to traditional methods and for normally distributed data the new methods are competitive to the best of the traditional methods. We finally show how the methods compare by using two environmental data sets.     

Methods for national population forecasts: a review

"Three widely used classes of methods for forecasting national populations are reviewed: demographic accounting/cohort-component methods for long-range projections, statistical time series methods for short-range forecasts, and structural modeling methods for the simulation and forecasting of the effects of policy changes. In each case, the major characteristics, strengths, and weaknesses of the methods are described. Factors that place intrinsic limits on the accuracy of population forecasts are articulated. Promising lines of additional research by statisticians and demographers are identified for each class of methods and for population forecasting generally."     

Testing the equality means of several log-normal distributions

Researches propose various methods for comparing the means of two log-normal distributions. Some of these methods have been recently extended to test the equality means of several log-normal populations. Investigations show that none of the established methods is satisfactory. In this article, we provide three methods based on the computational approach test, which is a parametric bootstrap approach, for testing the means of several log-normal distributions. Further, we compare our methods with the existing methods through Monte Carlo simulation. The numerical results show that the Type I errors of these procedures are satisfactory regardless of the sample size, number of populations, and the true parameters. Finally, we explain the considered methods by real examples.     

Regression Methods for Combining Multiple Classifiers

As no single classification method outperforms other classification methods under all circumstances, decision-makers may solve a classification problem using several classification methods and examine their performance for classification purposes in the learning set. Based on this performance, better classification methods might be adopted and poor methods might be avoided. However, which single classification method is the best to predict the classification of new observations is still not clear, especially when some methods offer similar classification performance in the learning set. In this article we present various regression and classical methods, which combine several classification methods to predict the classification of new observations. The quality of the combined classifiers is examined on some real data. Nonparametric regression is the best method of combining classifiers.     

社会核算矩阵不同更新方法的比较研究

更新社会核算矩阵(Social Accounting Matrix, SAM)作为一种非调查技术一直受到普遍重视。本文用十种更新方法,包括RAS法、交叉熵法、广义交叉熵法等,以江苏省1997年的宏观和细化SAM为初始表,将其分别更新到2002年的宏观和细化SAM。我们通过保号检验、方向检验和接近程度检验比较了各种方法的更新结果。研究结论显示：第一,基于商的更新方法要优于基于距离的更新方法;第二,保号类方法不仅具有保号的功能,而且在方向检验和接近度检验中有着良好表现;第三,对某些重要系数采用适当的外生估算方式,可以提高SAM更新的精度。     

教学质量评估的两种方法

文章研究了教学质量评估中两种定量分析的方法:马尔可夫链评估法、奖罚权系数矩阵法,给出了这两种方法的理论依据及实施程序。用该方法对哈尔滨工程大学2002级大学数学(高等数学→线性代数→概率论与数理统计)教学质量进行了分析,指出该方法较之其他教学质量评估法更显合理。     

Clustered data with small sample sizes: Comparing the performance of model-based and design-based approaches

Two classes of methods properly account for clustering of data: design-based methods and model-based methods. Estimates from both methods have been shown to be approximately equal with large samples. However, both classes are known to produce biased standard error estimates with small samples. This paper compares the bias of standard errors and statistical power of marginal effects for generalized estimating equations (a design-based method) and generalized/linear mixed effects models (model-based methods) with small sample sizes via a simulation study. Provided that the distributional assumptions are met, model-based methods produced the least-biased standard error estimates and greater relative statistical power.