多因子量化模型在投资组合中的应用——基于LASSO与Elastic Net的比较研究

多因子模型一直是量化投资领域的重要方法,而如何选择有效因子并确定因子权重从而构建有效的投资组合是研究者重点讨论的议题。在研究打分法和普通最小二乘法确定因子权重的基础上,引入LASSO和弹性网(Elastic Net)两类前沿方法进行因子筛选并确定因子权重,利用沪深300指数成份股进行回测,研究结果表明,Elastic Net方法比OLS和LASSO方法更能够筛选出有效因子,并构建出有效的投资组合,从而帮助投资者获得更高的超额收益。该模型对量化投资策略的设计具有重要的实际意义。     

灰色关联分位评价方法与多维相对贫困测度

为了提升乡村振兴阶段相对贫困人口的识别准确性,文章聚焦贫困的多维属性,利用灰色关联理论构建了一类新的具有动态特征的多维贫困识别方法。该方法将被测主体视为空间中的几何形状,利用几何特征对比替代传统方法中的双重临界值,降低了临界值选取对识别结果的主观影响,以分位数指标为基础实现了对相对贫困的动态识别。同时拓展了灰色关联评价模型,讨论了识别结果的加总与分解,建立了多维贫困发生率、多维贫困分布指数与多维贫困深度指数。最后利用中国家庭追踪调查（CFPS）2018年的数据对中国多维相对贫困情况进行分析,结果表明,我国农村的多维贫困发生率远高于城市,但是贫困分布和贫困深度差距不大,各贫困维度的贡献率在不同区域和城乡范围内表现出不同的特征。     

农村家庭多维贫困测度与分析

多维贫困理论与方法更有助于对贫困的精准识别和量化.本文构建了新型多维贫困指标体系,利用中国家庭追踪调查(CFPS)数据,引人人工神经网络方法,测度并分解了农村家庭多维贫困的广度、深度和强度水平.研究结论表明:随着贫困维度的增加,多维贫困的广度、深度和强度指数下降,表明农村家庭不易发生多维极端贫困;农村家庭多维贫困指数(MPI)呈西高东低态势,表明农村家庭多维贫困具有典型的区域分布特征.此外,多维贫困指数分解结果显示,收入、金融和教育等因素是我国农村家庭致贫的主因.其中,东部地区金融因素影响最大,而中西部地区则为收入因素.研究结论为贫困的识别和精准扶贫提供了政策依据.     

基于结构方程模型的合成信度及其使用问题研究

信度是问卷、量表、测验质量保障的核心要素,基于因素分析理论的合成信度估计方法近年来被讨论,并被统计学者推荐使用。合成信度比常用的信度计算方法α系数更为准确,且可用于多维测验和题目残差间存在相关的测验。通过文献分析及实例演示,分析了合成信度的特点和优点;同时,重点讨论了通过结构方程模型软件计算合成信度应注意的数据类型、数据分布、样本量以及模型收敛和拟合程度等问题,并比较了常用结构方程模型软件对合成信度计算的支持情况。     

中国多维相对贫困测度及其分布动态演进

文章基于中国31个省份2013—2019年多维相对贫困的相关数据，采用AF方法、Dagum基尼系数和Kernel密度估计方法对中国多维相对贫困状况及其分布动态进行分析。研究发现：我国扶贫减贫效果显著，2019年重度多维相对贫困省份已经消除，多维相对贫困指数分解结果表明，经济维度的减贫效果最为明显，社会发展维度次之，而生态环境维度在减贫过程中尚未得到足够重视；中国多维相对贫困具有明显的空间非均衡性，总体基尼系数跨度较大并且下降明显，多维相对贫困的总体差异在缩小；中国多维相对贫困状况的总体发展态势与三大地区的发展态势具有空间上的一致性。总体来看，我国多维相对贫困的整体状况有所改善，绝对差距也在缩小。     

多维极化的测度:新的方法及其应用

鉴于现有多维极化测度方法未能体现维度之间的相关性,本文引入余弦相似度,并基于相对剥夺理论提出了新的多维极化测度方法。该方法还可以进行静态和动态分解,能够分析每个维度及其变动对多维极化的贡献率。运用中国家庭追踪调查(CFPS)数据,本文从收入、财产、交通通信支出和受教育水平四个维度实证分析了我国城乡居民家庭之间的多维极化。实证结果表明,城乡长期多维极化呈现明显下降趋势,其中教育、交通通信支出和收入三个维度的极化效应是主要原因,而财产极化效应不利于多维极化的下降。     

中国农村老年人多维贫困的测量与识别研究——基于收入贫困与多维贫困视角

老龄化背景下农村老年人贫困问题逐步显化。基于2015年中国健康与养老追踪调查(China Health and Retirement Longitudinal Study,CHARLS)数据,在测算收入贫困的基础上,采用AF方法测算了农村老人的客观多维贫困指数(MPI-I)和包括主观福利的多维贫困指数(MPI-II),涉及收入、教育、健康、生活水平和主观福利5个维度。研究发现,MPI-II测量的主、客观多维贫困发生率最高且主观福利贡献最大;MPI-I测量的客观多维贫困次之,收入贫困发生率最低。进一步,对收入贫困和多维贫困样本进行交叉识别,收入贫困的农村老人中有96%同时陷入多维贫困,多维贫困样本中一半以上为非收入贫困。这说明虽然收入始终是农村老人多维贫困识别的重要因素,但以主观福利为代表的非收入因素影响更大。     

正则化Beta回归及其应用

随着计算机的飞速发展,极大地便利了数据的获取和存储,很多企业积累了大量的数据,同时数据的维度也越来越高,噪声变量越来越多,因此在建模分析时面临的重要问题之一就是从高维的变量中筛选出少数的重要变量。针对因变量取值为(0,1)区间的比例数据提出了正则化Beta回归,研究了在LASSO、SCAD和MCP三种惩罚方法下的极大似然估计及其渐进性质。统计模拟表明MCP的方法会优于SCAD和LASSO,并且随着样本量的增大,SCAD的方法也将优于LASSO。最后,将该方法应用到中国上市公司股息率的影响因素研究中。     

新时期农村多维贫困的识别与测度

文章根据全面建成小康社会后新时期贫困表现的新特征,制定了考虑家庭规模影响的“小康贫困线”及“三保障”评估的临界值,以识别新时期的农村相对贫困人口。基于江西省624户农村样本,构建了包含收入标准、三保障标准、食物营养结构、社会关系、精神状态等7个维度的识别体系,对样本农户进行多维贫困测度。结果表明：大多数家庭都是一维贫困,其中,劳动力、收入、精神状况维度贫困发生率较高;建档立卡农户贫困贡献率高于非建档立卡农户;维度分解结果表明,收入、劳动力、健康和教育四个维度的贡献显著。     

中国家庭多维贫困的统计测度

为全面反映居民贫困现状,需要对多个维度的贫困信息进行综合,计算多维贫困指数。对Alkire等（2008）的等权剥夺矩阵采用了非等权设计,改进了多维贫困指数测度方法,并将基于贫困发生率、贫困强度与贫困深度的多维贫困指数统一到一个计算过程中,提高了计算效率。文章利用1997—2006三年的CHNS数据,在饮用水、收入、教育、健康保险、电器等五个维度,时中国家庭多维贫困进行了测量。实证结果表明：随着时间的推移,中国家庭整体的贫困状况有所改善,没有家庭存在5个维度的贫困;维度分解表明饮用水、收入、教育对多维贫困指数的贡献最大。     

Normalization rules for item response models: Simulation results

ABSTRACT

This research examines the statistical methodology that is used to estimate the parameters in item response models. An integral part of an item response model is the normalization rule that is used to identify the distributional parameters. The main result shown here is that only Verhelst–Glas normalizations that arbitrarily set one difficulty and one dispersion parameter to unity are consistent with the basic assumptions underlying the two-parameter logistic model. Failure to employ this type of normalization will lead to scores that depend on the item composition of the test and differential item difficulty (DIF) will compromise the validity of the estimated ability scores when different groups are being compared. It is also shown that some of the tests for DIF fail when the data are generated by an IRT model with a random effect. Most of the results are based on simulations of a four item model. Because the data generation mechanism is known, it is possible to determine the effect on ability scores and parameter estimates when different normalizations or different distribution parameter values are used.     

Comparing methods for estimating the abilities for the multidimensional models of mixed item types

The maximum likelihood (MLE), the weighted maximum likelihood (WMLE), and the maximum a posteriori (MAP or BMLE) have been widely used to estimate ability parameters in item response theory (IRT), and their precisions and biases have been studied and compared. Multidimensional IRT (MIRT) has been shown to provide better subscore estimates in both paper-and-pencil and computer adaptive tests; thus, it is very important to have an accurate score estimate for the MIRT model. The purpose of this article is to compare the performances of the three estimation methods in the MIRT framework for tests of mixed item types that have both dichotomous and polytomously scored items, and for tests of mixed structured items (simple structured and complex structured). It is found that all three methods perform well for all conditions. For all models studied (one-, two-, three-, and four- dimensional model), WMLE has smaller BIAS and higher reliabilities, but larger RMSE and SE. WMLE and MLE are closer to each other than to BMLE. However, for higher dimensions, BMLE is recommended, especially when there are correlations between the dimensions.     

Checking the Short‐Term and Long‐Term Hazard Ratio Model for Survival Data

Abstract. The short‐term and long‐term hazard ratio model includes the proportional hazards model and the proportional odds model as submodels, and allows a wider range of hazard ratio patterns compared with some of the more traditional models. We propose two omnibus tests for checking this model, based, respectively, on the martingale residuals and the contrast between the non‐parametric and model‐based estimators of the survival function. These tests are shown to be consistent against any departure from the model. The empirical behaviours of the tests are studied in simulations, and the tests are illustrated with some real data examples.     

LOPSIDED REASONING ON LOPSIDED TESTS AND MULTIPLE COMPARISONS

For those who have not recognized the disparate natures of tests of statistical hypotheses and tests of scientific hypotheses, one‐tailed statistical tests of null hypotheses such as ?≤ 0 or ?≥ 0 have often seemed a reasonable procedure. We earlier reviewed the many grounds for not regarding them as such. To have at least some power for detection of effects in the unpredicted direction, several authors have independently proposed the use of lopsided (also termed split‐tailed, directed or one‐and‐a‐half‐tailed) tests, two‐tailed tests with α partitioned unequally between the two tails of the test statistic distribution. We review the history of these proposals and conclude that lopsided tests are never justified. They are based on the same misunderstandings that have led to massive misuse of one‐tailed tests as well as to much needless worry, for more than half a century, over the various so‐called ‘multiplicity problems’. We discuss from a neo‐Fisherian point of view the undesirable properties of multiple comparison procedures based on either (i) maximum potential set‐wise (or family‐wise) type I error rates (SWERs), or (ii) the increasingly fashionable, maximum potential false discovery rates (FDRs). Neither the classical nor the newer multiple comparison procedures based on fixed maximum potential set‐wise error rates are helpful to the cogent analysis and interpretation of scientific data.     

COMPARISON OF FEATURE SIGNIFICANCE QUANTILE APPROXIMATIONS

Curve estimates and surface estimates often contain features such as inclines, bumps or ridges which may signify an underlying structural mechanism. However, spurious features are also a common occurrence and it is important to identify those features that are statistically significant. A method has been developed recently for recognising feature significance based on the derivatives of the function estimate. It requires simultaneous confidence intervals and tests, which in turn require quantiles for the maximal deviation statistics. This paper reviews and compares various approximations to these quantiles. Applying upcrossing‐probability theory to this problem yields better quantile approximations than the use of an independent blocks method.     

Methods to Distinguish Between Polynomial and Exponential Tails

Two methods to distinguish between polynomial and exponential tails are introduced. The methods are based on the properties of the residual coefficient of variation for the exponential and non‐exponential distributions. A graphical method, called a CV‐plot, shows departures from exponentiality in the tails. The plot is applied to the daily log‐returns of exchange rates of US dollar and Japanese yen. New statistics are introduced for testing the exponentiality of tails using multiple thresholds. They give better control of the significance level than previous tests. The powers of the new tests are compared with those of some others for various sample sizes.     

基于结构方程模型的农民工可雇佣性的决定因素研究——基于中部六省的调查

农民工是中国人力资源的重要组成部分。从智能、技能、体能和心能四个维度构建农民工可雇佣性模型,根据调查问卷收集数据,运用结构方程验证了农民工可雇佣性模型的合理性,研究了农民工可雇佣性及其各维度间的关系。结果表明,各维度对农民工可雇佣性影响程度依次为:技能、心能、智能和体能。农民工因年龄、文化程度不同,在可雇佣能力、技能、心能和智能方面存在显著性差异,但是在体能上没有显著差异。     

Discrete‐time survival trees

Tree‐based methods are frequently used in studies with censored survival time. Their structure and ease of interpretability make them useful to identify prognostic factors and to predict conditional survival probabilities given an individual's covariates. The existing methods are tailor‐made to deal with a survival time variable that is measured continuously. However, survival variables measured on a discrete scale are often encountered in practice. The authors propose a new tree construction method specifically adapted to such discrete‐time survival variables. The splitting procedure can be seen as an extension, to the case of right‐censored data, of the entropy criterion for a categorical outcome. The selection of the final tree is made through a pruning algorithm combined with a bootstrap correction. The authors also present a simple way of potentially improving the predictive performance of a single tree through bagging. A simulation study shows that single trees and bagged‐trees perform well compared to a parametric model. A real data example investigating the usefulness of personality dimensions in predicting early onset of cigarette smoking is presented. The Canadian Journal of Statistics 37: 17‐32; 2009 © 2009 Statistical Society of Canada     

Modelling Accelerated Degradation Data Using Wiener Diffusion With A Time Scale Transformation

Engineering degradation tests allow industry to assess the potential life span of long-life products that do not fail readily under accelerated conditions in life tests. A general statistical model is presented here for performance degradation of an item of equipment. The degradation process in the model is taken to be a Wiener diffusion process with a time scale transformation. The model incorporates Arrhenius extrapolation for high stress testing. The lifetime of an item is defined as the time until performance deteriorates to a specified failure threshold. The model can be used to predict the lifetime of an item or the extent of degradation of an item at a specified future time. Inference methods for the model parameters, based on accelerated degradation test data, are presented. The model and inference methods are illustrated with a case application involving self-regulating heating cables. The paper also discusses a number of practical issues encountered in applications. This revised version was published online in July 2006 with corrections to the Cover Date.     

Non‐parametric Bayesian Intensity Model: Exploring Time‐to‐Event Data on Two Time Scales

Time‐to‐event data have been extensively studied in many areas. Although multiple time scales are often observed, commonly used methods are based on a single time scale. Analysing time‐to‐event data on two time scales can offer a more extensive insight into the phenomenon. We introduce a non‐parametric Bayesian intensity model to analyse two‐dimensional point process on Lexis diagrams. After a simple discretization of the two‐dimensional process, we model the intensity by a one‐dimensional piecewise constant hazard functions parametrized by the change points and corresponding hazard levels. Our prior distribution incorporates a built‐in smoothing feature in two dimensions. We implement posterior simulation using the reversible jump Metropolis–Hastings algorithm and demonstrate the applicability of the method using both simulated and empirical survival data. Our approach outperforms commonly applied models by borrowing strength in two dimensions.