基于模糊聚类的高速公路运营成本预测

以高速公路全寿命周期成本为基础，提出了应用模糊聚类估算模型计算高速公路运营成本的方法，并以大广高速深州至大名段为例，选取高速公路途经地区平均人均国内生产总值、总里程、路基宽度、桥涵设施数量、立交数量、收费站数量、服务区数量和平均日交通量等特征属性，依据12条高速公路的特征属性和运营成本，预测出大广高速的运营成本。     

一种基于最大模糊熵的高斯聚类算法

介绍了一种新的模糊聚类方法，定义了模糊熵，提出了基于最大模糊熵的模糊聚类的方法，得到了一种新的聚类算法——GCM算法。该算法的物理意义清晰，有明确的数学含义，相对于传统的FCM聚类算法，其聚类效果更好。     

A joint model for hierarchical continuous and zero-inflated overdispersed count data

Many applications in public health, medical and biomedical or other studies demand modelling of two or more longitudinal outcomes jointly to get better insight into their joint evolution. In this regard, a joint model for a longitudinal continuous and a count sequence, the latter possibly overdispersed and zero-inflated (ZI), will be specified that assembles aspects coming from each one of them into one single model. Further, a subject-specific random effect is included to account for the correlation in the continuous outcome. For the count outcome, clustering and overdispersion are accommodated through two distinct sets of random effects in a generalized linear model as proposed by Molenberghs et al. [A family of generalized linear models for repeated measures with normal and conjugate random effects. Stat Sci. 2010;25:325–347]; one is normally distributed, the other conjugate to the outcome distribution. The association among the two sequences is captured by correlating the normal random effects describing the continuous and count outcome sequences, respectively. An excessive number of zero counts is often accounted for by using a so-called ZI or hurdle model. ZI models combine either a Poisson or negative-binomial model with an atom at zero as a mixture, while the hurdle model separately handles the zero observations and the positive counts. This paper proposes a general joint modelling framework in which all these features can appear together. We illustrate the proposed method with a case study and examine it further with simulations.     

Volatility clustering in the presence of time-varying model parameters

The volatility pattern of financial time series is often characterized by several peaks and abrupt changes, consistent with the time-varying coefficients of the underlying data-generating process. As a consequence, the model-based classification of the volatility of a set of assets could vary over a period of time. We propose a procedure to classify the unconditional volatility obtained from an extended family of Multiplicative Error Models with time-varying coefficients to verify if it changes in correspondence with different regimes or particular dates. The proposed procedure is experimented on 15 stock indices.     

Robust estimation of the mean vector for high-dimensional data set using robust clustering

The first step in statistical analysis is the parameter estimation. In multivariate analysis, one of the parameters of interest to be estimated is the mean vector. In multivariate statistical analysis, it is usually assumed that the data come from a multivariate normal distribution. In this situation, the maximum likelihood estimator (MLE), that is, the sample mean vector, is the best estimator. However, when outliers exist in the data, the use of sample mean vector will result in poor estimation. So, other estimators which are robust to the existence of outliers should be used. The most popular robust multivariate estimator for estimating the mean vector is S-estimator with desirable properties. However, computing this estimator requires the use of a robust estimate of mean vector as a starting point. Usually minimum volume ellipsoid (MVE) is used as a starting point in computing S-estimator. For high-dimensional data computing, the MVE takes too much time. In some cases, this time is so large that the existing computers cannot perform the computation. In addition to the computation time, for high-dimensional data set the MVE method is not precise. In this paper, a robust starting point for S-estimator based on robust clustering is proposed which could be used for estimating the mean vector of the high-dimensional data. The performance of the proposed estimator in the presence of outliers is studied and the results indicate that the proposed estimator performs precisely and much better than some of the existing robust estimators for high-dimensional data.     

基于共词聚类分析法的我国档案管理研究热点

以CNKI数据库收录的9种档案学核心期刊为数据源,采用共词聚类分析法对其刊载的档案管理研究论文的关键词进行了分析,绘制了共词聚类分析树图,以揭示统计时域内我国档案管理的研究热点,为我国档案管理研究与实践提供定量化的参考信息。     

基于组合分类的消费者信用评估

为了降低单个分类模型的不稳定性,提高其应用于消费者信用评估的准确性,提出一种基于组合分类的消费者信用评估方法.首先通过有监督聚类将各个类别的数据样本划分为若干子集合,使得各个子集合中数据样本均来自同一类别,再对不同类别子集合之间进行两两组合得到训练样本子集合,然后在各个训练样本子集合中分别建立不同的分类模型.在分类模型结果综合阶段,以各个分类模型在待分类样本的近邻训练样本中的分类性能作为权重,对各个分类模型的结果进行加权投票以产生待分类样本的最终分类结果.实证研究以决策树作为基本分类器,通过在实际的消费者信用数据集上的比较分析,说明所提出方法相对于其它组合分类方法具有更高的分类准确性,可以有效应用于消费者信用评估.     

Cluster‐Specific Variable Selection for Product Partition Models

We propose a random partition model that implements prediction with many candidate covariates and interactions. The model is based on a modified product partition model that includes a regression on covariates by favouring homogeneous clusters in terms of these covariates. Additionally, the model allows for a cluster‐specific choice of the covariates that are included in this evaluation of homogeneity. The variable selection is implemented by introducing a set of cluster‐specific latent indicators that include or exclude covariates. The proposed model is motivated by an application to predicting mortality in an intensive care unit in Lisboa, Portugal.     

一种带有导向性的聚类方法在电信客户细分中的应用

聚类是一种常用的客户细分方法。本文结合电信市场客户细分实践,针对一般聚类算法的不足,设计了一种带有导向性的聚类方法。使用该方法得出的聚类结果区分了普通类与极端类,提高了聚类效果,在指导实际市场营销活动中有着明显的优势。     

湖南农业功能拓展思路研究

通过科学制定农业功能分区指导思想与原则,构建科学合理的农业功能分区指标体系,利用聚类分析法和德尔菲法把湖南122个县(市、区)划分为丘岗盆地及洞庭湖平原农产品供给功能主导区、低山丘陵生活保障功能主导区、"四水"上游-南岭山脉生态调节功能主导区、城郊文化传承与休闲观光功能主导区等4大功能主导区,科学提出了各功能主导区的功能定位、功能拓展方向和途径,制定了各功能主导区实现及协调发展的保障性措施。