消费能使我们幸福吗

自收入幸福悖论被提出以来,大量文献探究了收入和幸福感的关系,常忽略消费。本文利用CGSS2010的调查数据,致力于研究消费及其分类消费对我国居民幸福感的影响。研究发现：消费的绝对值和相对值对幸福感均存在显著的正向影响,但消费对幸福感的影响比收入小;消费对不同群体幸福感的影响存在一些差异,收入越低、受教育程度越低、经济社会阶层越低,消费对幸福感的影响越大;分类消费中服装、交通通讯、耐用消费品、文化休闲娱乐、人情送礼等支出对幸福感的影响较大。本文的研究结论为政府逐步提升人民消费水平和幸福感提供了可靠的决策依据。     

食品标签信息对消费者购买决策的影响研究——以婴幼儿食品为例

基于Hicks消费者剩余的CV形式,构建信息关注度、信息源信任度、信息充分度的Logit食品标签信息影响决策模型.通过在北京市发放问卷并回收、统计数据及分析,研究发现:消费者在购买婴幼儿食品时,对食品标签上的食品生产日期和保质期、营养成分、配料清单、食用指导注意等信息的关注度高,影响显著;对食品品牌、生产厂家及厂址、使用方法及贮藏、安全标识等信息关注度低,影响不显著;消费者对政府机构、婴幼儿医师和学者、亲朋及名人代言等信息源的信任度高,对购买决策影响显著;消费者对非政府机构和食品企业等信息源的信任度低,对购买决策影响不显著;食品标签信息充分度对消费者获得高质量信息、降低风险和提高决策准确度等起到的作用很大;消费者的性别、年龄、收入、受教育程度等对购买决策影响显著;相对于女性而言,食品标签信息对男性消费者的购买决策更有影响.     

Criteria for surrogate end points based on causal distributions

Summary.  When a treatment has a positive average causal effect (ACE) on an intermediate variable or surrogate end point which in turn has a positive ACE on a true end point, the treatment may have a negative ACE on the true end point due to the presence of unobserved confounders, which is called the surrogate paradox. A criterion for surrogate end points based on ACEs has recently been proposed to avoid the surrogate paradox. For a continuous or ordinal discrete end point, the distributional causal effect (DCE) may be a more appropriate measure for a causal effect than the ACE. We discuss criteria for surrogate end points based on DCEs. We show that commonly used models, such as generalized linear models and Cox's proportional hazard models, can make the sign of the DCE of the treatment on the true end point determinable by the sign of the DCE of the treatment on the surrogate even if the models include unobserved confounders. Furthermore, for a general distribution without any assumption of parametric models, we give a sufficient condition for a distributionally consistent surrogate and prove that it is almost necessary.     

Seasonal daily effect on the number of births in Israel

Previous studies have observed a 7-day cycle with the least number of births occurring on Sundays. This study was aimed at characterizing the daily effect on the number of births in Israel, where Sunday is a regular working day and Saturday is a nonworking day. It was hypothesized that fewer births would be noted on Saturday rather than Sunday and fewer births on Jewish holidays. The data consisted of a random sample of live births during 1975. A robust time series procedure was used for the data analysis. As expected, the number of births was much smaller on Saturdays than on other weekdays (10% reduction). The seasonal component for Sundays was greater than that for any other day due to births delayed from Saturday and scheduled induced births. The number of births was not reduced as much on Saturdays during spring and summer as during fall and winter. In addition, fewer births occurred on most Jewish holidays. However, an unexpected and extremely high number of births was noted on Yom Kippur and the day after. It is speculated that the strain and fasting during this holiday promoted labor induction. In 21 of the 52 weeks in the study period, the least number of births occurred on a Saturday, and the number of births was less than the mean of the remaining 6 days in 46 of these weeks. Similarly, in 17 of the 52 weeks, the greatest number of births occurred on a Sunday, and the Sunday births exceeded the mean of Monday-Friday births in 35 of these weeks. Thus, these results confirm the study hypothesis and are believed to reflect intervention in childbirth.     

基于SYS-GMM的中国人口结构变化与经济增长关系研究

本文利用中国29个省份20年的面板数据,探讨了人口结构变化对储蓄、投资和经济增长的影响。我们将人口结构指标引入传统的经济增长模型中,并用固定效应模型和SYS-GMM计量方法分别测算其对储蓄、投资和经济增长的效应。考虑到老年抚养比在经济增长方程的内生性,我们还将滞后30年的就业人数比率作为工具变量来识别老年抚养比对经济增长的因果效应。结果显示老年抚养率对储蓄、投资和经济增长存在显著的正相关。人口增长率对储蓄和投资有着负相关,但对经济增长没有影响。工作年龄人口比率对储蓄率存在负相关,对投资和经济增长存在正向影响但估计的回归系数不显著。在加入其它制度和人口变量之后回归结果依然稳定。     

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.     

Estimation in Regressive Logistic Regression Analyses of Familial Data with Missing Outcomes

This paper examines a number of methods of handling missing outcomes in regressive logistic regression modelling of familial binary data, and compares them with an EM algorithm approach via a simulation study. The results indicate that a strategy based on imputation of missing values leads to biased estimates, and that a strategy of excluding incomplete families has a substantial effect on the variability of the parameter estimates. Recommendations are made which depend, amongst other factors, on the amount of missing data and on the availability of software.     

中国式分权、市场化进程与经济增长

本文从理论上阐释了中国式分权影响地区经济增长的内在逻辑,以及市场化进程作为门槛变量影响中国式分权之经济增长效应的理论依据,并基于中国省际层面的门槛面板模型实证检验了市场化进程的门槛效应。实证结果表明：中国式分权和地区市场化进程均正向影响地区经济增长;当市场化进程高于某一门槛值后,中国式分权对经济增长的促进作用会降低。其他各种稳健性检验均表明本文结论具有较强的稳健性。在当前中国经济进入新常态的现实背景下,本文研究结论具有重要的政策含义和现实意义。     

Copula-Based Models for the Power of Independence Tests

ABSTRACT

We consider independence tests and the methods to evaluate their efficiency. First, we observe that many of the most used independence tests are functions of the empirical copula, which is a sufficient statistic. Hence, the power of these tests, such as the tests based on Spearman's ρ, on Kendall's τ, and on Gini's γ, depend solely on the theoretical copula, and not on the marginal distributions. Then, we consider monotone dependence tests and we propose a parametric model to define the power function. Such a model is based on a path of copulas, from the copula of discordance to the copula of concordance, and can be characterized by the copula of the underlying joint distribution. Moreover, we introduce a consistent estimator of the path of copulas. Finally, we provide some examples of applications, and in particular, a bootstrap-plug-in estimator of the power curve, all useful for power comparison.     

Mixture models in measurement error problems, with reference to epidemiological studies

Summary. The paper focuses on a Bayesian treatment of measurement error problems and on the question of the specification of the prior distribution of the unknown covariates. It presents a flexible semiparametric model for this distribution based on a mixture of normal distributions with an unknown number of components. Implementation of this prior model as part of a full Bayesian analysis of measurement error problems is described in classical set-ups that are encountered in epidemiological studies: logistic regression between unknown covariates and outcome, with a normal or log-normal error model and a validation group. The feasibility of this combined model is tested and its performance is demonstrated in a simulation study that includes an assessment of the influence of misspecification of the prior distribution of the unknown covariates and a comparison with the semiparametric maximum likelihood method of Roeder, Carroll and Lindsay. Finally, the methodology is illustrated on a data set on coronary heart disease and cholesterol levels in blood.     

Construction of nearly uniform designs on irregular regions

The uniform design is a kind of important experimental design which has great practical value in production and living. Most existing literatures on this topic focus on the construction of uniform designs on regular regions. However, because of the complexity of practical situations, the irregular design region is more common in real life. In this paper, an algorithm is proposed to the construction of nearly uniform designs on irregular regions. The basic idea is to make use of uniform designs on a larger regular region with the irregular region being a subregion. Some theoretical justifications on the proposed algorithm are provided. Both the comparisons with the existing results and a real-life example show that our proposed algorithm is effective.     

Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes

ABSTRACT

This work presents advanced computational aspects of a new method for changepoint detection on spatio-temporal point process data. We summarize the methodology, based on building a Bayesian hierarchical model for the data and declaring prior conjectures on the number and positions of the changepoints, and show how to take decisions regarding the acceptance of potential changepoints. The focus of this work is about choosing an approach that detects the correct changepoint and delivers smooth reliable estimates in a feasible computational time; we propose Bayesian P-splines as a suitable tool for managing spatial variation, both under a computational and a model fitting performance perspective. The main computational challenges are outlined and a solution involving parallel computing in R is proposed and tested on a simulation study. An application is also presented on a data set of seismic events in Italy over the last 20 years.     

Time-varying clustering of multivariate longitudinal observations

Abstract

We propose a statistical method for clustering multivariate longitudinal data into homogeneous groups. This method relies on a time-varying extension of the classical K-means algorithm, where a multivariate vector autoregressive model is additionally assumed for modeling the evolution of clusters' centroids over time. Model inference is based on a least-squares method and on a coordinate descent algorithm. To illustrate our work, we consider a longitudinal dataset on human development. Three variables are modeled, namely life expectancy, education and gross domestic product.     

Spatio-temporal improvised explosive device monitoring: improving detection to minimise attacks

The improvised explosive device (IED) is a weapon of strategic influence on today's battlefield. IED detonations occur predominantly on roads, footpaths, or trails. Therefore, locations are best described when constrained to the road network, and some spaces on the network are more dangerous at specific times of the day. We propose a statistical model that reduces the spatial location to one dimension and uses a cyclic time as a second dimension. Based on the Poisson process methodology, we develop normalised, inhomogeneous, bivariate intensity functions measuring the threat of attack to support resourcing decisions. A simulation and an analysis of attacks on a main supply route in Baghdad are given to illustrate the proposed methods. Additionally, we provide an overview of the growing demand for the analysis efforts in support of operations in Afghanistan and Iraq, and provide an extensive literature review of developments in counter-IED analysis.     

The relationship between osteoporotic fracture risk and a surrogate: Apparent discrepancies between analyses based on individual patient data and summary statistics

There is debate within the osteoporosis research community about the relationship between the risk of osteoporotic fracture and the surrogate measures of fracture risk. Meta‐regression analyses based on summary data have shown a linear relationship between fracture risk and surrogate measures, whereas analyses based on individual patient data (IPD) have shown a nonlinear relationship. We investigated the association between changes in a surrogate measure of fracture incidence, in this case a bone turnover marker for resorption assessed in the three risedronate phase III clinical programmes, and incident osteoporosis‐related fracture risk using regression models based on patient‐level and trial‐level information. The relationship between osteoporosis‐related fracture risk and changes in bone resorption was different when analysed on the basis of IPD than when analysed on the basis of a meta‐analytic approach (i.e., meta‐regression) using summary data (e.g., treatment effect based on treatment group estimates). This inconsistency in our findings was consistent with those in the published literature. Meta‐regression based on summary statistics at the trial level is not expected to reflect causal relationships between a clinical outcome and surrogate measures. Analyses based on IPD make possible a more comprehensive analysis since all relevant data on a patient level are available. Copyright © 2004 John Wiley & Sons Ltd.     

注意性告知与元认知策略对培训效果的影响研究

计算机审计培训是培养高素质审计人才的有效途径。注意性告知和元认知策略是影响计算机审计培训效果的重要因素。实证研究了注意性告知与元认知策略对培训效果的影响,研究发现:注意性告知与元认知策略都会对培训效果产生正向影响;注意性告知还会对元认知策略产生正向影响。此外,注意性告知通过元认知策略部分中介作用于培训效果。     

Simpson's Paradox in Survival Models

Abstract.  In the context of survival analysis it is possible that increasing the value of a covariate X has a beneficial effect on a failure time, but this effect is reversed when conditioning on any possible value of another covariate Y . When studying causal effects and influence of covariates on a failure time, this state of affairs appears paradoxical and raises questions about the real effect of X . Situations of this kind may be seen as a version of Simpson's paradox. In this paper, we study this phenomenon in terms of the linear transformation model. The introduction of a time variable makes the paradox more interesting and intricate: it may hold conditionally on a certain survival time, i.e. on an event of the type { T > t } for some but not all t , and it may hold only for some range of survival times.     

Decision‐making in drug development using a composite definition of success

Evidence‐based quantitative methodologies have been proposed to inform decision‐making in drug development, such as metrics to make go/no‐go decisions or predictions of success, identified with statistical significance of future clinical trials. While these methodologies appropriately address some critical questions on the potential of a drug, they either consider the past evidence without predicting the outcome of the future trials or focus only on efficacy, failing to account for the multifaceted aspects of a successful drug development. As quantitative benefit‐risk assessments could enhance decision‐making, we propose a more comprehensive approach using a composite definition of success based not only on the statistical significance of the treatment effect on the primary endpoint but also on its clinical relevance and on a favorable benefit‐risk balance in the next pivotal studies. For one drug, we can thus study several development strategies before starting the pivotal trials by comparing their predictive probability of success. The predictions are based on the available evidence from the previous trials, to which new hypotheses on the future development could be added. The resulting predictive probability of composite success provides a useful summary to support the discussions of the decision‐makers. We present a fictive, but realistic, example in major depressive disorder inspired by a real decision‐making case.