Control Chart for Monitoring Autocorrelated Process with Multiple Exogenous Inputs

We propose to use AR-Sieve Bootstrap in the construction of a control chart of an autocorrelated process influenced by multiple exogenous inputs. The control charts are compared with Exponentially Weighted Moving Average (EWMA) control chart through a simulation study. AR-Sieve bootstrap control limits are narrower than EWMA control limits. While the proposed method yields a higher rate of false alarms, it is quick in detecting even minimal structural changes.     

空气污染治理与公众健康———来自“大气十条”政策的证据

为探究《大气污染防治行动计划》 (以下简称“大气十条”)的健康效应,本文基于中国健康与营养调查(CHNS)数据及省级面板数据,利用基于多项Logit模型的双重差分法(Logit-DID),从微观与宏观两个维度考察“大气十条”政策的健康改善效应及其传导机制,并进一步通过异质性分析探讨环境健康不平等的内在原因 研究发现:“大气十条”实施带来显著的健康效应,该政策不仅能有效控制与空气污染高度相关疾病的发病率和死亡占比,还能够间接降低受空气污染影响较小的疾病病情。其作用机理主要通过控制PM2.5浓度改善公众健康,同时该政策对二氧化硫(SO2)、氮氧化物、烟(粉)尘等其 他污染物浓度的下降起到了协同作用。进一步分析显示环境福利不公平的现象依然存在,“大气十条”政 策显著降低了疾病高发人群的发病率,且对女性、城镇居民和低、高年龄段等疾病高发人群的健康影响更显著。本文为政府构建环境健康公平发展路径,推进“健康中国”战略提供了经验证据和政策借鉴。     

Sparse group lasso for multiclass functional logistic regression models

Sparsity-inducing penalties are useful tools for variable selection and are also effective for regression problems where the data are functions. We consider the problem of selecting not only variables but also decision boundaries in multiclass logistic regression models for functional data, using sparse regularization. The parameters of the functional logistic regression model are estimated in the framework of the penalized likelihood method with the sparse group lasso-type penalty, and then tuning parameters for the model are selected using the model selection criterion. The effectiveness of the proposed method is investigated through simulation studies and the analysis of a gene expression data set.     

New innovational outlier unit root test with a break at an unknown time

The Perron test which is based on a Dickey–Fuller test regression is a commonly employed approach to test for a unit root in the presence of a structural break of unknown timing. In the case of an innovational outlier (IO), the Perron test tends to exhibit spurious rejections in finite samples when the break occurs under the null hypothesis. In the present paper, a new Perron-type IO unit root test is developed. It is shown in Monte Carlo experiments that the new test does not over-reject the null hypothesis. Even for the case of a level and slope break for trending data, the empirical size is near its nominal level. The test distribution equals the case of a known break date. Furthermore, the test is able to identify the true break date very accurately even for small breaks. As an application serves the Nelson–Plosser data set.     

An m-estimation-based model selection criterion with a data-oriented penalty

In Wu and Zen (1999), a linear model selection procedure based on M-estimation is proposed, which includes many classical model selection criteria as its special cases, and it is shown that the selection procedure is strongly consistent for a variety of penalty functions. In this paper, we will investigate its small sample performances for some choices of fixed penalty functions. It can be seen that the performance varies with the choice of the penalty. Hence, a randomized penalty based on observed data is proposed, which preserves the consistency property and provides improved performance over a fixed choice of penalty functions.     

Size distortion of asymmetric unit root tests in the presence of level shifts

The finite-sample size properties of momentum-threshold autoregressive (MTAR) asymmetric unit root tests are examined in the presence of level shifts under the null hypothesis. The original MTAR test using a fixed threshold is found to exhibit severe size distortion when a break in level occurs early in the sample period, leading to an increased probability of an incorrect inference of asymmetric stationarity. For later breaks the test is also shown to suffer from undersizing. In contrast, the use of consistent-threshold estimation results in a test which is relatively robust to level shifts.     

1906-1908年的郑孝胥与预备立宪公会

考虑到我国未来年度的OD分布预测中的路网构成变化、区域经济布局变动、区域经济增长速度差异等一些具有时变性和特殊性因素，采用在“四阶段法”运量预测基础上改进而成的“三阶段法”进行高速铁路短期客运量预测。首先采用组合预测模型进行趋势运量预测，然后采用多元LOGIT模型进行方式分担，最后采用弹性系数诱增模型进行诱增运量预测。以京沪高铁为例，采用“三阶段法”预测了2014年和2015年京沪高铁本线及跨线单向客流量。     

Personal networks typologies: A structural approach

Building typologies allows to compare networks on multiple dimensions, and to approach a generalization grounded on empirical data. In this article, we present a typology of personal networks only based on indicators related to the structure of relations between alters. It is designed from very detailed data on young French people who were involved in a longitudinal study. Our typology mobilizes a small number of indicators to discriminate the types that compose it. In so doing, we intend to make it applicable to various surveys.     

Deriving Behavior Model Parameters from Survey Data: Self‐Protective Behavior Adoption During the 2009–2010 Influenza A(H1N1) Pandemic

In this paper, we demonstrate how public opinion surveys can be designed to collect information pertinent to computational behavior modeling, and we present the results of a public opinion and behavior survey conducted during the 2009–2010 H1N1 influenza pandemic. The results are used to parameterize the Health Belief Model of individual health‐protective decision making. Survey subjects were asked questions about their perceptions of the then‐circulating influenza and attitudes towards two personal protective behaviors: vaccination and avoidance of crowds. We empirically address two important issues in applying the Health Belief Model of behavior to computational infectious disease simulation: (1) the factors dynamically influencing the states of the Health Belief Model variables and (2) the appropriateness of the Health Belief Model in describing self‐protective behavior in the context of pandemic influenza.     

Did small and medium enterprises maintain better jobs during the early years of the recession? Job quality multidimensional evidence from Spain

While the literature has indeed confirmed a general tendency linking small and medium enterprises (SMEs) to a dynamic of greater job creation, there is little available evidence on what has happened to job quality since the financial crisis. Through a representative sample of 5311 employees in 2008 (first year of job destruction) and 4925 employees in 2010 (last year for which data were available), and using a two-stage structural equation model, this article empirically analyses the multidimensional determinants of job quality, by enterprise-size class, in Spain. The research has revealed three main results. First, job quality in Spain improved in all enterprises, regardless of their size, during the early years of the recession. Second, the greatest improvements were found in SMEs. Although job quality was already better in SMEs than in large enterprises in 2008, the differences between them subsequently widened. Third, this accelerated divergence was explained by the following dimensions: working conditions, work intensity, health and safety at work, and work–life balance. These dimensions were much more positive in SMEs. Employment-related public policy should therefore focus more specifically on SMEs. There are two reasons for this. First, despite the recession, SMEs have shown themselves to be key factors in the explanation of job quality. Second, by making changes to their value generation model, they could continue to drive the creation of better quality jobs.