Indirect membership function assignment based on ordinal regression

In many fuzzy sets applications, fuzzy membership functions are commonly developed based on empirical or expert knowledge. The equation of a membership function is usually determined somewhat arbitrarily. This paper explores a novel membership function design method based on ordinal regression analysis. The estimated thresholds between ordinal measurement categories are applied to calculate the intersection points between fuzzy sets. These intersection points are further applied to determine the equations of the membership functions. Information distortion due to empirical guess can thus be reduced and more latent information in the fuzzy responses can therefore be captured. A case study investigating the relationship between foster mothers’ satisfaction and the foster time and information provided has been conducted in this research. The applicability and effectiveness of the proposed membership function assignment approach have been demonstrated through several case studies.     

Choosing summary statistics by least angle regression for approximate Bayesian computation

Bayesian statistical inference relies on the posterior distribution. Depending on the model, the posterior can be more or less difficult to derive. In recent years, there has been a lot of interest in complex settings where the likelihood is analytically intractable. In such situations, approximate Bayesian computation (ABC) provides an attractive way of carrying out Bayesian inference. For obtaining reliable posterior estimates however, it is important to keep the approximation errors small in ABC. The choice of an appropriate set of summary statistics plays a crucial role in this effort. Here, we report the development of a new algorithm that is based on least angle regression for choosing summary statistics. In two population genetic examples, the performance of the new algorithm is better than a previously proposed approach that uses partial least squares.     

An exploratory data analysis in scale-space for interval-valued data

We propose an exploratory data analysis approach when data are observed as intervals in a nonparametric regression setting. The interval-valued data contain richer information than single-valued data in the sense that they provide both center and range information of the underlying structure. Conventionally, these two attributes have been studied separately as traditional tools can be readily used for single-valued data analysis. We propose a unified data analysis tool that attempts to capture the relationship between response and covariate by simultaneously accounting for variability present in the data. It utilizes a kernel smoothing approach, which is conducted in scale-space so that it considers a wide range of smoothing parameters rather than selecting an optimal value. It also visually summarizes the significance of trends in the data as a color map across multiple locations and scales. We demonstrate its effectiveness as an exploratory data analysis tool for interval-valued data using simulated and real examples.     

中国生产性服务业TFP的时空差异与影响因素分析

在利用Malmquist指数法测算2005-2013年中国30个省份生产性服务业的全要素生产率(TFP)的基础上,采用核密度估计法分析了TFP的动态演变,并运用分位数回归方法对中国生产性服务业TFP的影响因素进行了实证分析。研究发现:在考察期内中国生产性服务业TFP总体呈下降态势,技术效率下降是其下降的主要原因;核密度曲线说明中国生产性服务业TFP省际差距扩大,技术效率和技术进步逐渐呈现两极分化的趋势;分位数回归结果表明,工业化水平和人力资本水平对生产性服务业TFP提升具有普遍的促进作用,而信息化水平、对外开放水平、制造业集中度对生产性服务业TFP提高的贡献大小均与地区生产性服务业TFP的水平有关。     

Some maximum-indifference estimators for the slope of a univariate linear model

As known, the least-squares estimator of the slope of a univariate linear model sets to zero the covariance between the regression residuals and the values of the explanatory variable. To prevent the estimation process from being influenced by outliers, which can be theoretically modelled by a heavy-tailed distribution for the error term, one can substitute covariance with some robust measures of association, for example Kendall's tau in the popular Theil–Sen estimator. In a scarcely known Italian paper, Cifarelli [(1978), ‘La Stima del Coefficiente di Regressione Mediante l'Indice di Cograduazione di Gini’, Rivista di matematica per le scienze economiche e sociali, 1, 7–38. A translation into English is available at http://arxiv.org/abs/1411.4809 and will appear in Decisions in Economics and Finance] shows that a gain of efficiency can be obtained by using Gini's cograduation index instead of Kendall's tau. This paper introduces a new estimator, derived from another association measure recently proposed. Such a measure is strongly related to Gini's cograduation index, as they are both built to vanish in the general framework of indifference. The newly proposed estimator is shown to be unbiased and asymptotically normally distributed. Moreover, all considered estimators are compared via their asymptotic relative efficiency and a small simulation study. Finally, some indications about the performance of the considered estimators in the presence of contaminated normal data are provided.     

基于主成分logistic回归模型的餐饮商务信息意识研究

数据挖掘技术日趋成熟,广泛应用于金融、地产、投资、评估等各个领域。数据挖掘技术亦可应用于餐饮业,为其经营决策做出分析。其他领域的数据挖掘的成功案例也可以引用到餐饮行业中。本文对餐饮人士在餐饮商务中是否具备信息意识、对信息意识的重视程度进行调查,利用数据挖掘中主成分分析和 logistic 回归处理和分析收集来的数据,从而说明数据挖掘在餐饮业管理中的重要意义,反映餐饮人士信息意识状况。利用数据挖掘技术对餐饮商务资讯进行管理势必提高企业效率和盈利水平,促进餐饮业健康稳定的发展。     

城投公司债券信用利差影响因素与对策研究

为防范政府债务风险,减少城投债信用违约事件的发生,采用多元线性回归模型,选取2014～2016年新发城投债中7年期公司债为样本,将政府财政及债券等个体因素相关指标作为自变量,以宏观因素指标作为控制变量,分析中国不同行政级别城投债信用利差的影响因素。研究认为,债券个体因素对县、市和省级城投债信用利差均有显著影响,宏观因素的影响随城投公司所属行政级别升高而升高;地方政府财政因素对各行政级别的影响方向相同,由于城投公司资质存在差异,政府财政因素对低行政级别信用利差的影响显著;不同行政级别下,各类因素对信用利差的影响略有不同,城投公司债券发行信用利差与评级情况和发行规模都呈负向关系,系数绝对值随行政级别的升高而降低,企业自身财务因素影响并不显著。地方政府应大力发展经济使城投公司融资成本降低,投资者在投资前利用多种指标估算债券发行信用利差,城投公司也要注意合理设计发债规模,使自身融资成本达到最低,监管机构加强对低行政级别城投公司债券发行的监管和宏观经济方面的政策引导,以此促进解决城投债问题和推动城投债公司合理发行债券。     

Factors influencing Soay sheep survival

We present a survival analysis of Soay sheep mark recapture and recovery data. Unlike previous conditional analyses, it is not necessary to assume equality of recovery and recapture probabilities; instead these are estimated by maximum likelihood. Male and female sheep are treated separately, with the higher numbers and survival probabilities of the females resulting in a more complex model than that used for the males. In both cases, however, age and time aspects need to be included and there is a strong indication of a reduction in survival for sheep aged 7 years or more. Time variation in survival is related to the size of the population and selected weather variables, by using logistic regression. The size of the population significantly affects the survival probabilities of male and female lambs, and of female sheep aged 7 or more years. March rainfall and a measure of the North Atlantic oscillation are found to influence survival significantly for all age groups considered, for both males and females. Either of these weather variables can be used in a model. Several phenotypic and genotypic individual covariates are also fitted. The only covariate which is found to influence survival significantly is the type of horn of first-year female sheep. There is a substantial variation in the recovery probabilities over time, reflecting in part the increased effort when a population crash was expected. The goodness of fit of the model is checked by using graphical procedures.     

Bootstrapping regression models with BLUS residuals

To bootstrap a regression problem, pairs of response and explanatory variables or residuals can be resam‐pled, according to whether we believe that the explanatory variables are random or fixed. In the latter case, different residuals have been proposed in the literature, including the ordinary residuals (Efron 1979), standardized residuals (Bickel & Freedman 1983) and Studentized residuals (Weber 1984). Freedman (1981) has shown that the bootstrap from ordinary residuals is asymptotically valid when the number of cases increases and the number of variables is fixed. Bickel & Freedman (1983) have shown the asymptotic validity for ordinary residuals when the number of variables and the number of cases both increase, provided that the ratio of the two converges to zero at an appropriate rate. In this paper, the authors introduce the use of BLUS (Best Linear Unbiased with Scalar covariance matrix) residuals in bootstrapping regression models. The main advantage of the BLUS residuals, introduced in Theil (1965), is that they are uncorrelated. The main disadvantage is that only n —p residuals can be computed for a regression problem with n cases and p variables. The asymptotic results of Freedman (1981) and Bickel & Freedman (1983) for the ordinary (and standardized) residuals are generalized to the BLUS residuals. A small simulation study shows that even though only n — p residuals are available, in small samples bootstrapping BLUS residuals can be as good as, and sometimes better than, bootstrapping from standardized or Studentized residuals.