Multivariate Measures of Well-Being and an Analysis of Inequality in the Michigan Data

We demonstrate a multidimensional approach for combining several indicators of well-being, including the traditional money-income indicators. This methodology avoids the difficult and much criticized task of computing imputed incomes for such indicators as net worth and schooling. Inequality in the proposed composite measures is computed using relative inequality indexes that permit simple analysis of both the contribution of each welfare indicator (and its factor components) and within and between components of total inequality when the population is grouped by income levels, age, gender, or any other criteria. The analysis is performed on U.S. data using the Michigan Survey of Income Dynamics.     

Fuzzy measures of the incidence of relative poverty and deprivation: a multi-dimensional perspective

In this paper we present a methodology for the study of multi-dimensional aspects of poverty and deprivation. The conventional poor/non-poor dichotomy is replaced by defining poverty as a matter of degree, determined by the place of the individual in the income distribution. The fuzzy poverty measure proposed is in fact also expressible in terms of the generalised Gini measure. The same methodology facilitates the inclusion of other dimensions of deprivation into the analysis: by appropriately weighting indicators of deprivation to reflect their dispersion and correlation, we can construct measures of non-monetary deprivation in its various dimensions. These indicators illuminate the extent to which purely monetary indicators are insufficient in themselves in capturing the prevalence of deprivation. An important contribution of the paper is to identify rules for the aggregation of fuzzy sets appropriate for the study of poverty and deprivation. In particular, we define a ‘composite’ fuzzy set operator which takes into account whether the sets being aggregated are of a ‘similar’ or a ‘dissimilar’ type. These rules allow us to meaningfully combine income and the diverse non-income deprivation indices at the micro-level and construct what we have termed ‘intensive’ and ‘extensive’ indicators of deprivation. We note that mathematically the same approach can be carried over to the study of persistence of poverty and deprivation over time.     

Local principal curves

Principal components are a well established tool in dimension reduction. The extension to principal curves allows for general smooth curves which pass through the middle of a multidimensional data cloud. In this paper local principal curves are introduced, which are based on the localization of principal component analysis. The proposed algorithm is able to identify closed curves as well as multiple curves which may or may not be connected. For the evaluation of the performance of principal curves as tool for data reduction a measure of coverage is suggested. By use of simulated and real data sets the approach is compared to various alternative concepts of principal curves.     

Poverty as a System: Human Contestability Approach to Poverty Measurement

Since Sen's (1976) paper on poverty measurement, a substantial literature, both theoretical and empirical, has emerged. There have been several recent efforts to drive poverty measures based on different approaches and axioms. These poverty indices are based on head count ratio, poverty gaps and distribution of income. These are very narrow in approach and suffer from several drawbacks. However, the purpose of the present paper is to introduce a new poverty measure based on a holistic and system modelling approach. Based on Chopra's human contestability (Chopra, 2003, 2007) approach to poverty, this new approach to measuring poverty has been developed using a structure equation model based on Kanji's business excellence model (Kanji, 2002) to create the proposed poverty model. We construct a latent variable structural equation model to measure the contestability excellence within certain boundaries of the societal system. It will provide us with a measurement of poverty in a society or community in terms of human contestability. A higher human contestability index will indicate the lower poverty within the society. Strengths and weakness as of various components will also indicate that a characteristic of the individual requires extra society or government support to remove poverty. However, there remains considerable disagreement on the best way to achieve this.     

构建贫困购买力平价的两个关键点

目前，国际上出现了一种全新的测量贫困程度的方法——贫困购买力平价法。将这种新方法与原有的贫困线和贫困指数等进行对比分析，并尝试性地归纳出构建贫困PPP的两个关键点——汇总方法和数据收集。同时，汇总方法和数据收集也是构建贫困PPP的难点。     

A new way to order independent components

A relatively newer computational technique adopted by statisticians is known as independent component analysis (ICA) which is used to analyze complex multidimensional data with the objective to separate it into components that are independent to each other. Quite often the main interest for conducting ICA is to identify a small number of significant independent components (ICs) to replace the original complex dimensions with. For this, determining the order of identified ICs is a pre-requisite. The area is not unaddressed but it does deserve a careful revisiting. This is the subject matter of the paper which introduces a new method to order ICs. The proposed method is based upon regression approach. It compares the magnitude of the mixing coefficients and regression coefficients of the regression of the original series on ICs. Their compatibility determines the order.     

Panel regression models for measuring multidimensional poverty dynamics

This work concerns the study of poverty dynamics and the analysis of the influencing socio-demographic factors. A fuzzy and multidimensional approach has been chosen in order to define two different poverty measures. A panel regression model has been estimated and particular attention has been paid to the treatment of the unobservable heterogeneity among longitudinal units. The specified model combines autoregression with variance components. The empirical analysis has been conducted using the data set of the British Household Panel Survey (BHPS) from 1991 to 1997. This work was co-financed by Murst funds for the projects “Occupazione e disoccupazione in Italia: misura e analisi dei comportamenti”. The paper is the result of the common work of all the authors; in particular G. Betti has written Sects. 2,5.1 and 5.3.1; A. D’Agostino has written sections 4, 5.2 and 5.4; L. Neri has written Sects. 1, 3, 5.3.2 and 6.     

中国养老金体系待遇充足性的多维度评估

养老金制度的首要目标是防止老年贫困和实现一定水平的收入替代,从而养老金的“待遇充足”至关重要。本文借鉴国际上关于养老金充足性的多维度评估思路,结合中国实际,构建了中国养老金体系待遇充足性的三维度、六指标评价体系。在此基础上,基于国家统计局公开的数据以及全国综合社会调查（CGSS）提供的微观数据进行测算分析,发现我国养老金体系在收入水平、防止贫困和性别差异三个维度上的表现并不理想,各地区养老金充足性在不同时间上存在差异。进而提出我国应在不同层次养老金制度设计中强化收入替代功能、防止老年贫困和缩小性别差异等政策建议。     

我国农村贫困的动态转化、持续时间与状态依赖研究——基于收入贫困与多维贫困的双重视角

本文使用中国健康与营养调查2000—2015年跟踪数据,比较分析了农村家庭收入贫困与多维贫困的长期变动情况。在此基础上,使用Cox比例风险与动态Probit模型实证研究了跨期贫困的动态转化概率、状态依赖及其影响因素等问题。研究发现,农村多维贫困发生率下降幅度比收入贫困发生率下降幅度高出近20个百分点,但未脱贫的多维贫困家庭比收入贫困家庭具有更明显的贫困适应性。随着贫困持续时间的增加,无论是收入贫困还是多维贫困,中断当前贫困状态的可能性都下降了。进一步分析发现,子代职业地位、子代教育、城镇化水平和交通便捷度等因素显著降低了贫困家庭的贫困适应性与状态依赖,而贫困补贴对部分贫困家庭产生了补贴依赖效应,从而一定程度上促进了其贫困适应性。本研究在理论上丰富了有关贫困动态性的探讨,为有效破解低收入群体的贫困状态依赖提供了经验证据。     

贫困测度指标及其评价

一、引言一般来说,研究贫困问题主要涉及三个方面:一是贫困的识别;二是贫困程度测定;三是反贫困效果判定以及相应的战略选择。贫困识别问题主要指贫困的基本范畴及贫困线的合理确定,关系到贫困集合的准确界定;贫困程度的测定主要指贫困指数的定义和计算,关系到贫困程度大小的判     

教育改善贫困效应的地区异质性研究

教育对于增加收入、改善贫困无疑是一个重要的途径。在目前多维贫困越来越受到重视的情况下,本文在收入贫困和多维贫困两种贫困识别标准下,分析了教育改善贫困的效应在地区之间的异质性,考察了该效应是否受地区经济发展的影响。根据分层LOGIT模型的估计,本文认为地区经济发展水平是发挥教育改善收入贫困效应的重要舞台,对于多维贫困的改善效应则不明显。本文得到的一个重要扶贫政策建议是要发挥教育改善贫困的效应,必须注重精准扶贫与发展地区经济相结合;但仅仅发展经济,不一定能保证教育对多维贫困的改善效应,减轻多维贫困需要更加重视公共产品供给和民生保证。     

A Dynamic Latent Model for Poverty Measurement

The main purpose of this paper is the longitudinal analysis of the poverty phenomenon. By interpreting poverty as a latent variable, we are able to resort to the statistical methodology developed for latent structure analysis. In particular, we propose to use the mixture latent Markov model which allows us to achieve two goals: (i) a time-invariant classification of households into homogenous groups, representing different levels of poverty; (ii) the dynamic analysis of the poverty phenomenon which highlights the distinction between transitory and permanent poverty situations. Furthermore, we exploit the flexibility provided by the model in order to achieve the measurement of poverty in a multidisciplinary framework, using several socio-economic indicators as covariates and identifying the main relevant factors which influence permanent and transitory poverty. The analysis of the longitudinal data of the Survey on Households Income and Wealth of the Bank of Italy provides the identification of two groups of households which are characterized by different dynamic features. Moreover, the inclusion of socio-economic covariates such as level of education, employment status, geographical area and residence size of the household head shows a direct association with permanent poverty.     

Relative Price Changes and Inequality in the Size Distribution of Various Components of Income

This article uses a comprehensive model of economic inequality to examine the impact of relative price changes on inequality in the marginal distributions of various income components in which the marginal distributions are derived from a multidimensional joint distribution. The multidimensional joint distribution function is assumed to be a member of the Pearson Type VI family; that is, it is assumed to be a beta distribution of the second kind. The multidimensional joint distribution is so called because it is a joint distribution of components of income and expenditures on various commodity groups. Gini measures of inequality are devised from the marginal distributions of the various income components. The inequality measures are shown to depend on the parameters of the multidimensional joint distribution. It is then shown that the parameters of the multidimensional joint distribution depend on the relative prices of various commodity groups and several other specified exogenous variables. Thus, knowledge of how changes in relative prices affect the parameters of the multidimensional joint distribution is deductively equivalent to knowledge of how changes in relative prices affect inequality in the marginal distributions of various components of income. It is found that relative price changes have a statistically significant impact on inequality in various components of income.     

EEG in the diagnostics of Alzheimer’s disease

Dementia caused by Alzheimer’s disease (AD) is worldwide one of the main medical and social challenges for the next years and decades. An automated analysis of changes in the electroencephalogram (EEG) of patients with AD may contribute to improving the quality of medical diagnoses. In this paper, measures based on uni- and multi-variate spectral densities are studied in order to measure slowing and, in greater detail, reduced synchrony in the EEG signals. Hereby, an EEG segment is interpreted as sample of a (weakly) stationary stochastic process. The spectral density was computed using an indirect estimator. Slowing was considered by calculating the spectral power in predefined frequency bands. As measures for synchrony between single EEG signals, we analyzed coherences, partial coherences, bivariate and conditional Granger causality; for measuring synchrony between groups of EEG signals, we considered coherences, partial coherences, bivariate and conditional Granger causality between the respective first principal components of each group, and dynamic canonic correlations. As measure for local synchrony within a group, the amount of variance explained by the respective first principal component of static and dynamic principal component analysis was investigated. These measures were exemplarily computed for resting state EEG recordings from 83 subjects diagnosed with probable AD. Here, the severity of AD is quantified by the Mini Mental State Examination score.     

A general class of poverty measures

Measurement of poverty in the spirit of Sen essentially consists of two steps, namely (1) the identification of the poor and (2) the aggregation of the income distribution of the poor into an indicator called poverty measure. We contribute to the second step from a mathematical and statistical point of view by defining a class of poverty measures and investigating its properties including sensitivity with respect to changes in the income distribution. Various poverty orderings are defined and their relation to stochastic dominance is investigated. Finally estimation of the class of poverty measures from individual and grouped data is considered.     

Finite mixtures approach to ecological regression

During past few years great attention has been devoted to the analysis of disease incidence and mortality rates, with an explicit focus on modelling geographical variation of rates observed in spatially adjacent regions. The general aim of these contributes has been both to highlight clusters of regions with homogeneous relative risk and to determine the effects of observed and unobserved risk factors related to the analyzed disease. Most of the proposed modelling approaches can be derived as alternative specifications of the components of a general convolution model (Molliè, 1996). In this paper, we consider the semiparametric approach discussed by Schlattmann and Böhning (1993); in particular, we focus on models with an explicit spatially structured component (see Biggeri et al., 2000), and propose alternative choices for the structure of the spatial component.     

VARIABLE SELECTION AND INTERPRETATION OF COVARIANCE PRINCIPAL COMPONENTS

In practice, when a principal component analysis is applied on a large number of variables the resultant principal components may not be easy to interpret, as each principal component is a linear combination of all the original variables. Selection of a subset of variables that contains, in some sense, as much information as possible and enhances the interpretations of the first few covariance principal components is one possible approach to tackle this problem. This paper describes several variable selection criteria and investigates which criteria are best for this purpose. Although some criteria are shown to be better than others, the main message of this study is that it is unwise to rely on only one or two criteria. It is also clear that the interdependence between variables and the choice of how to measure closeness between the original components and those using subsets of variables are both important in determining the best criteria to use.     

Testing differentially methylated regions through functional principal component analysis

DNA methylation is an epigenetic modification that plays an important role in many biological processes and diseases. Several statistical methods have been proposed to test for DNA methylation differences between conditions at individual cytosine sites, followed by a post hoc aggregation procedure to explore regional differences. While there are benefits to analyzing CpGs individually, there are both biological and statistical reasons to test entire genomic regions for differential methylation. Variability in methylation levels measured by Next-Generation Sequencing (NGS) is often observed across CpG sites in a genomic region. Evaluating meaningful changes in regional level methylation profiles between conditions over noisy site-level measurements is often difficult to implement with parametric models. To overcome these limitations, this study develops a nonparametric approach to detect predefined differentially methylated regions (DMR) based on functional principal component analysis (FPCA). The performance of this approach is compared with two alternative methods (GIFT and M3D), using real and simulated data.KEYWORDS: Functional principal component, epigenetics, DNA methylation, next-generation sequencing     

Kernel partial correlation: a novel approach to capturing conditional independence in graphical models for noisy data

Graphical models capture the conditional independence structure among random variables via existence of edges among vertices. One way of inferring a graph is to identify zero partial correlation coefficients, which is an effective way of finding conditional independence under a multivariate Gaussian setting. For more general settings, we propose kernel partial correlation which extends partial correlation with a combination of two kernel methods. First, a nonparametric function estimation is employed to remove effects from other variables, and then the dependence between remaining random components is assessed through a nonparametric association measure. The proposed approach is not only flexible but also robust under high levels of noise owing to the robustness of the nonparametric approaches.     

Some remarks on measurement models in the structural equation model: an application for socially responsible food consumption

Considering the structural equation model (SEM), usually the main researches are based on the structural model rather than on the measurement one. So, this context implies some problems: construct misspecification, identification and validation. Starting from the most recent articles in terms of these issues, we achieve – and formalize through two tables – a general framework that could help researchers select and assess both formative and reflective measurement models with special attention on statistical implications. To show this general framework, we present a survey on customer behaviours for socially responsible food consumption. The survey was carried out by delivering a questionnaire administered to a representative sample of 332 families. In order to detect the main aspects impacting consumers’ preferences, a factor analysis has been performed. Then the general framework has been used to select and assess the measurement models in SEM. The estimation of the SEM has been worked out by partial least squares. The significance of the indicators has been tested using bootstrap. As far as we know, it is the first time that a model for the analysis of the consumers’ behaviour for social responsibility is formalized through a SEM.