非平衡数据集的改进SMOTE再抽样算法

非平衡数据集的不均衡学习特点通常表现为负类的分类效果不理想。改进SMOTE再抽样算法,将过抽样和欠抽样方式有机结合,有针对性地选择近邻并采用不同策略合成样本。实验表明,分类器在经此算法处理后的非平衡数据集的正负两类上,均可获得较理想的分类效果。     

分布函数的非参数局部多项式估计

文章讨论了当分布函数F(x)经过-log(-log(F(x)))变换后,可以采用局部多项式逼近;使用非参数分布的泛函估计,建立了分布函数的强相合估计。模拟结果表明,估计拟合情况要优于经验分布,较为理想。     

CEO变更、董事特征与盈余管理的实证研究

文章研究了具有特定较强公司治理特征的董事会，在存在CEO变更的公司的年度报表中操纵盈余管理方面的作用。在对CEO变更当年及其滞后的盈利控制之后，文章在CEO辞职的样本公司中发现了负的非预期盈余，并且较大的董事规模和较高的独立董事比例会限制这种可观察的负的盈余管理。在CEO变更的下一年，没有发现在CEO辞职后存在正的非预期盈余，也没有发现任何董事会特征与非预期盈余有任何显著的关系。通过研究发现在CEO退休后，较高的管理层持股比例在很大程度上抵消了可观察的正的非预期盈余。     

基于非参数GARCH的模型的一种波动率估计方法

一、方法描述 (一)Bollerslev(1986)提出的标准的GARCH(1,1)形式 εt=(√ht zt) V(ε/Ωt-1)=ht=α0+α1ε2 t-1+β1ht-1 (1) 其中,Ωt-1是时间的信息集,包含了εt-1及其以前的信息,εt是扰动项,ht是条件方差,z1是白噪声.为确保有条件的方差非负,α1和β1必须非负,且满足α1+β1＜1才保证序列是宽平稳的.     

基于Logistic回归和后验概率SVM的住房贷款组合评估模型

本文针对单一模型存在的分类精度有限的不足,提出了应用组合预测模型进行信用评估的方法.选择Logistic回归和后验概率SVM模型作为单一模型,构建了基于二者的非负权重线性组合预测模型,并将模型应用于住房信贷评估.应用结果表明,组合评估模型的分类精度高于单一模型,并且获得了较好的稳健性,对于构建住房信贷评估模型是一个很好的选择.     

基于非负矩阵分解的函数型聚类算法

函数型聚类分析算法涉及投影和聚类两个基本要素。通常,最优投影结果未必能够有效地保留类别信息,从而影响后续聚类效果。为此,本文梳理了函数型聚类的构成要素及运行过程;借助非负矩阵分解的聚类特性,提出了基于非负矩阵分解的函数型聚类算法,构建了“投影与聚类”并行的实现框架,并采用交替迭代方法更新求解,分析了算法的计算时间复杂度。针对随机模拟数据验证和语音识别数据的实例检验结果显示,该函数型聚类算法有助于提高聚类效果;针对北京市二氧化氮(NO2)污染物小时浓度数据的实例应用表明,该函数型聚类算法对空气质量监测点类型的区分能够充分识别站点布局的空间模式,具有良好的实际应用价值。     

非负系数线性回归模型的构建

本文针对某些线性回归模型负的回归系数不具有实际的物理意义和经济意义的问题,提出了非负系数的线性回归模型构建的新方法。与现有的方法相比较,该方法具有简单和易操作的特点。在实际中具有一定的应用价值。     

弱齐次马氏链的两个强大数定律

§1 几个引理 引理1 设{x_n}n≥1为一非负实数序列,若liminf x_n=a,limsup x_n=b,则对{x_n}_(n≥1)的任一子列{x_(n_k)}_(k≥1)有     

房价波动对我国城镇居民消费的影响研究

 本文将房价、住房面积、消费习惯及借贷约束等变量引入消费者最优选择模型中,构建出在综合考虑各个因素的条件下,能够检验房价波动对居民消费影响的动态面板模型,运用动态系统广义矩阵方法,采用我国29个省市的年度数据进行多角度的实证分析。研究显示:(1)我国城镇居民受到较强消费习惯与收入敏感性的影响;(2)房价波动对居住消费的影响为负,对非居住消费的影响为正,且均存在明显的非对称性;(3)中东西地区房价波动对居住消费的影响均为负,但影响系数差异较大;对非居住消费的影响差异就更为明显,东西部地区影响为正,中部地区为负。分析也表明,我国城镇高房价收入比、地区经济发展不平衡及居民收入差距的不断拉大是导致实证结果的主要原因。     

基于偏好变量的指数跟踪方法

考虑到在进行指数跟踪时影响强度大并且流动性好的成份股往往是被偏好的,结合股票市场的网络结构和指数的编制规则,提出基于偏好变量的指数跟踪方法;对沪深300指数进行实证分析,从跟踪偏离度、平均超额收益和年跟踪误差三方面对新方法进行评估,并与非负LASSO模型进行对比分析。实证结果显示,新方法不仅优于非负LASSO模型,而且优于市场上大多数指数基金。     

On a new family of shifted logarithmic transformations

Most parametric statistical methods are based on a set of assumptions: normality, linearity and homoscedasticity. Transformation of a metric response is a popular method to meet these assumptions. In particular, transformation of the response of a linear model is a popular method when attempting to satisfy the Gaussian assumptions on the error components in the model. A particular problem with common transformations such as the logarithm or the Box–Cox family is that negative and zero data values cannot be transformed. This paper proposes a new transformation which allows negative and zero data values. The method for estimating the transformation parameter consider an objective criteria based on kurtosis and skewness for achieving normality. Use of the new transformation and the method for estimating the transformation parameter are illustrated with three data sets.     

New Sigmoid-like function better than Fisher z transformation

ABSTRACT

In the mathematical statistics, in order to close approximately to the cumulative distribution function of standard normal distribution, the Fisher z transformation is the widely employed explicit elementary function, and is used to estimate the confidence interval of Pearson product moment correlation coefficient. A new Sigmoid-like function is suggested to replace the Fisher z transformation, and the new explicit elementary function is not more complicated than the Fisher z transformation. The new Sigmoid-like function can be 4.677 times more accurate than the Fisher z transformation.     

邓氏灰靶变换的注记

对灰靶理论的灰靶变换进行了理论基础分析。对于邓氏灰靶变换，证明了在序列取值于正数范围内时可以作为极大值极性和极小值极性指标列的灰靶变换；通过给出特殊例子说明了对于适中值极性指标序列，该变换不满足灰靶变换的定义。给出了一个适合于三种极性的灰靶变换，进一步完善了灰靶理论的计算问题。给出了新的灰靶变换在经济中的应用，调整了已有文献的一些结果。     

Semiparametric Regression Analysis of Longitudinal Skewed Data

In this paper, we develop a semiparametric regression model for longitudinal skewed data. In the new model, we allow the transformation function and the baseline function to be unknown. The proposed model can provide a much broader class of models than the existing additive and multiplicative models. Our estimators for regression parameters, transformation function and baseline function are asymptotically normal. Particularly, the estimator for the transformation function converges to its true value at the rate n ^{? 1 ∕ 2}, the convergence rate that one could expect for a parametric model. In simulation studies, we demonstrate that the proposed semiparametric method is robust with little loss of efficiency. Finally, we apply the new method to a study on longitudinal health care costs.     

Transformation of the bathtub failure rate data in reliability for using Weibull-model analysis

All statistical methods involve basic model assumptions, which if violated render results of the analysis dubious. A solution to such a contingency is to seek an appropriate model or to modify the customary model by introducing additional parameters. Both of these approaches are in general cumbersome and demand uncommon expertise. An alternative is to transform the data to achieve compatibility with a well understood and convenient customary model with readily available software. The well-known example is the Box–Cox data transformation developed in order to make the normal theory linear model usable even when the assumptions of normality and homoscedasticity are not met.In reliability analysis the model appropriateness is determined by the nature of the hazard function. The well-known Weibull distribution is the most commonly employed model for this purpose. However, this model, which allows only a small spectrum of monotone hazard rates, is especially inappropriate if the data indicate bathtub-shaped hazard rates.In this paper, a new model based on the use of data transformation is presented for modeling bathtub-shaped hazard rates. Parameter estimation methods are studied for this new (transformation) approach. Examples and results of comparisons between the new model and other bathtub-shaped models are shown to illustrate the applicability of this new model.     

A Transformation Characterizing the Normal Distribution

In this article we propose a new transformation of random variables (RVs) which characterizes the normal distribution. It allows us to transform n i.i.d. normal RVs whose mean and variance are unknown into new n ? 2 i.i.d. new normal variables with zero mean while maintaining the same unknown variance. This belongs to the class of transformations designed to reduce the number of unknown parameters or remove them altogether.

Some historical remarks concerning methods for removing parameters in the normal distribution are given and two possible applications of the new transformation are described.     

Adaptively transformed mixed‐model prediction of general finite‐population parameters

For estimating area‐specific parameters (quantities) in a finite population, a mixed‐model prediction approach is attractive. However, this approach strongly depends on the normality assumption of the response values, although we often encounter a non‐normal case in practice. In such a case, transforming observations to make them suitable for normality assumption is a useful tool, but the problem of selecting a suitable transformation still remains open. To overcome the difficulty, we here propose a new empirical best predicting method by using a parametric family of transformations to estimate a suitable transformation based on the data. We suggest a simple estimating method for transformation parameters based on the profile likelihood function, which achieves consistency under some conditions on transformation functions. For measuring the variability of point prediction, we construct an empirical Bayes confidence interval of the population parameter of interest. Through simulation studies, we investigate the numerical performance of the proposed methods. Finally, we apply the proposed method to synthetic income data in Spanish provinces in which the resulting estimates indicate that the commonly used log transformation would not be appropriate.     

Transformations and the Income Tax

Many of the more useful and powerful nonparametric procedures may be presented in a unified manner by treating them as rank transformation procedures. Rank transformation procedures are ones in which the usual parametric procedure is applied to the ranks of the data instead of to the data themselves. This technique should be viewed as a useful tool for developing nonparametric procedures to solve new problems.     

Kurtosis modelling by means of the J-transformation

Summary: The H–family of distributions or H–distributions, introduced by Tukey (1960; 1977), are generated by a single transformation of the standard normal distribution and allow for leptokurtosis represented by the parameter h. Alternatively, Haynes et al. (1997) generated leptokurtic distributions by applying the K–transformation to the normal distribution. In this study we propose a third transformation, the so–called J–transformation, and derive some properties of this transformation. Moreover, so-called elongation generating functions (EGFs) are introduced. By means of EGFs we are able to visualize the strength of tail elongation and to construct new transformations. Finally, we compare the three transformations towards their goodness–of–fit in the context of financial return data.     

ON THE FOURTH ROOT TRANSFORMATION OF CHI-SQUARE

We show that, within the family of power transformations of a Chisquare variable, the square and fourth roots minimize Pearson's index of kurtosis. Two new transtormations of the fourth root, a symmetrized-truncated version and its linear combination with the square root are also studied. The first transformation shows a considerable improvement over the fourth root while the second one turns out to be even more accurate than Hilferty-Wilson's cube root transformation.