Elasticity function of a discrete random variable and its properties

Elasticity (or elasticity function) is a new concept that allows us to characterize the probability distribution of any random variable in the same way as characteristic functions and hazard and reverse hazard functions do. Initially defined for continuous variables, it was necessary to extend the definition of elasticity and study its properties in the case of discrete variables. A first attempt to define discrete elasticity is seen in Veres-Ferrer and Pavía (2014a). This paper develops this definition and makes a comparative study of its properties, relating them to the properties shown by discrete hazard and reverse hazard, as both defined in Chechile (2011). Similar to continuous elasticity, one of the most interesting properties of discrete elasticity focuses on the rate of change that this undergoes throughout its support. This paper centers on the study of the rate of change and develops a set of properties that allows us to carry out a detailed analysis. Finally, it addresses the calculation of the elasticity for the resulting variable obtained from discretizing a continuous random variable, distinguishing whether its domain is in real positives or negatives.     

Empirical Bayes estimation in continuous one-parameter exponential families under associated samples

In this paper, we study the empirical Bayes (EB) estimation in continuous one-parameter exponential families under negatively associated (NA) samples and positively associated (PA) samples. Under certain regularity conditions, it is shown that the convergence rates of proposed EB estimators under NA or PA samples are the same as those of EB estimators under independent observations, which significantly improve the existing results in EB estimation under associated samples.     

农村养老制度对家庭人力资本积累影响研究述评

自新增长理论形成以来，将养老保障制度与人力资本积累相联系进行研究成为社会保障、社会学等领域的研究热点之一。无论是国外的理论研究还是实证研究均表明，养老保障制度对于人力资本积累的影响作用是十分明显的。但我国目前理论界直接对养老保障制度与人力资本积累之间关系展开研究的尚属少数。因此，关于养老保障制度对家庭人力资本积累的影响研究的广度和深度均有待发掘。     

菲利普斯曲线在中国的有效性研究

从基本的菲利普斯曲线理论出发,提出了新的适应性预期模型,并以此为基础建立附加预期的菲利普斯曲线方程,进而估计出我国近年来的自然失业率水平,由此证明了菲利普斯曲线在我国的有效性,并描述了预期因素对实际通货膨胀水平的影响。最后结合实证分析结果提出解决我国通货膨胀和失业问题的建议,如保证货币政策的连贯性、加强信息披露、解决结构性失业问题等。     

制造系统生产单元碳排放核算模型

制造业是我国国民经济的支柱产业，但也是环境污染物排放的主要来源、能源消耗主体和温室气体排放大户。制造系统高能耗、高物耗、高碳排放的加工过程是造成制造业碳排放量大的重要原因。生产单元作为制造系统的加工的主体，其碳排放量的核算是确定整个制造系统乃至制造业碳排放总量的关键。本文首先对生产单元的原材料、电能、辅助物料及废屑处理所引起的碳排放进行分析，确定生产单元的碳排放源；其次，产品合格率的不同会造成生产单元输入及输出的成品/半成品数量的差异，进而影响生产单元单位产品的碳排放量，在此基础上，综合考虑了原材料、电能、辅助物料及废屑处理的碳排放情况，构建了给定工艺流程下生产单元碳排放核算模型。最后，结合一汽车排气装置加工实例，分别核算加工过程中九个生产单元的碳排放量，验证了模型的可行性。     

初次收入分配对我国居民旅游消费影响研究

文章选取初次分配工资分配率、城镇居民基尼系数、各收入阶层旅游消费占比作为分析指标，经分析研究所得结论为：低、中收入阶层旅游消费占比与分配率负相关，分配率所代表的初次收入分配总量性影响对中收入阶层旅游消费影响较大，中、高收入阶层旅游消费占比都与城镇居民基尼系数正相关，其中城镇居民基尼系数代表的初次收入分配结构性影响对高收入阶层旅游消费影响较大；并针对各阶层的特殊性对各收入阶层旅游消费发展提出相应政策建议.     

Machine Learning Vasicek Model Calibration with Gaussian Processes

In this article, we calibrate the Vasicek interest rate model under the risk neutral measure by learning the model parameters using Gaussian processes for machine learning regression. The calibration is done by maximizing the likelihood of zero coupon bond log prices, using mean and covariance functions computed analytically, as well as likelihood derivatives with respect to the parameters. The maximization method used is the conjugate gradients. The only prices needed for calibration are zero coupon bond prices and the parameters are directly obtained in the arbitrage free risk neutral measure.     

Aggregating classifiers via Rademacher–Walsh polynomials

Methods are proposed to combine several individual classifiers in order to develop more accurate classification rules. The proposed algorithm uses Rademacher–Walsh polynomials to combine M (≥2) individual classifiers in a nonlinear way. The resulting classifier is optimal in the sense that its misclassification error rate is always less than, or equal to, that of each constituent classifier. A number of numerical examples (based on both real and simulated data) are also given. These examples demonstrate some new, and far-reaching, benefits of working with combined classifiers.     

On additive-multiplicative hazards model

In survival analysis and reliability theory, a fundamental problem is the study of lifetime properties of a live organism or system. In this regard, there have been considered and studied several models based on different concepts of ageing such as hazard rate and mean residual life. In this paper, we consider an additive-multiplicative hazard model (AMHM) and study some reliability and ageing properties of the proposed model. We then specify the bivariate models whose conditionals satisfy AMHM. Several properties of the proposed bivariate model are investigated and adequacy of the model is evaluated based on a real data set.     

A Semiparametric Regression Model for Longitudinal Data with Non‐stationary Errors

Motivated by the need to analyze the National Longitudinal Surveys data, we propose a new semiparametric longitudinal mean‐covariance model in which the effects on dependent variable of some explanatory variables are linear and others are non‐linear, while the within‐subject correlations are modelled by a non‐stationary autoregressive error structure. We develop an estimation machinery based on least squares technique by approximating non‐parametric functions via B‐spline expansions and establish the asymptotic normality of parametric estimators as well as the rate of convergence for the non‐parametric estimators. We further advocate a new model selection strategy in the varying‐coefficient model framework, for distinguishing whether a component is significant and subsequently whether it is linear or non‐linear. Besides, the proposed method can also be employed for identifying the true order of lagged terms consistently. Monte Carlo studies are conducted to examine the finite sample performance of our approach, and an application of real data is also illustrated.