中国城镇居民家庭储蓄水平队列效应分析

利用中国家庭收入调查（ CHIP）数据对城镇居民家庭的储蓄率进行了队列效应的实证研究。分析结果表明，属于不同队列的城镇家庭在储蓄水平上都呈现出相似的“U”型年龄分布，即中年家庭（40－50岁）储蓄率低，年轻家庭（20－40岁）和退休家庭储蓄率较高，这一现象与其他国家的实证研究结果有显著不同。本文认为，中国城镇居民储蓄率的“U”型年龄分布主要是由人口与经济的高速增长带来的：人口高速增长带来的队列规模效应使得面对更大竞争压力的年轻人口储蓄率较高，而经济水平持续增长带来的消费水平增长预期使得接近退休家庭储蓄水平重新上升。     

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.     

A new generalization of the Weibull-geometric distribution with bathtub failure rate

In this paper, the researchers attempt to introduce a new generalization of the Weibull-geometric distribution. The failure rate function of the new model is found to be increasing, decreasing, upside-down bathtub, and bathtub-shaped. The researchers obtained the new model by compounding Weibull distribution and discrete generalized exponential distribution of a second type, which is a generalization of the geometric distribution. The new introduced model contains some previously known lifetime distributions as well as a new one. Some basic distributional properties and moments of the new model are discussed. Estimation of the parameters is illustrated and the model with two known real data sets is examined.     

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 analysis of binary response data in longitudinal factorial studies

Binary data are commonly used as responses to assess the effects of independent variables in longitudinal factorial studies. Such effects can be assessed in terms of the rate difference (RD), the odds ratio (OR), or the rate ratio (RR). Traditionally, the logistic regression seems always a recommended method with statistical comparisons made in terms of the OR. Statistical inference in terms of the RD and RR can then be derived using the delta method. However, this approach is hard to realize when repeated measures occur. To obtain statistical inference in longitudinal factorial studies, the current article shows that the mixed-effects model for repeated measures, the logistic regression for repeated measures, the log-transformed regression for repeated measures, and the rank-based methods are all valid methods that lead to inference in terms of the RD, OR, and RR, respectively. Asymptotic linear relationships between the estimators of the regression coefficients of these models are derived when the weight (working covariance) matrix is an identity matrix. Conditions for the Wald-type tests to be asymptotically equivalent in these models are provided and powers were compared using simulation studies. A phase III clinical trial is used to illustrate the investigated methods with corresponding SAS® code supplied.     

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.     

Psychophysiological Reactivity in Child Sexual Abuse

Sexual abuse has physiological and emotional implications. The purpose of this study is to evaluate the neurobiological sequels of childhood sexual trauma by monitoring physiological variables among sexually abused girls and women compared to controls. We assessed posttrauma and traumatic life events of 35 females sexually abused in their childhood (age range 7–51 years) and 25 control females (age range 7–54 years). Electroencephalography, frontalis electromyography, electrodermal activity, and heart rate parameters were recorded while watching sets of pictures representing neutral and trauma-suggestive stimuli. A minority of participants met the Diagnostic and Statistical Manual of Mental Disorders criteria for post-traumatic stress disorder. Abused females displayed significant elevations in heart rate, electromyography, and electroencephalography while viewing allusive stimuli and elevated heart rate while viewing neutral stimuli. The dysfunctional regulation of the physiological stress system associated with child sexual abuse may endanger the victims with various stress and anxiety disorders.     

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.     

An International Comparative Study on Women's Pension Rights and Their Economic Risks

This article examined the theoretical meanings of pension rights and analyzed their effects on women's economic risks in developed countries. First, based on the status of a citizen, worker, parent, and spouse, this study investigated how pension benefits are guaranteed as a citizen regardless of work history, the degree to which women's disadvantageous situations in the labor market and unpaid work are compensated by public pension as workers and parents, and how marital status is treated in the different pension systems. Second, analysis of the effect of pension rights showed that individual rights is a significant factor to prevent economic risks of elderly women. Derived rights did not seem to secure the economic welfare of elderly women, at least in a comparative context. This finding suggested that developing individual rights, rather than derived rights, is the way to guarantee long‐term elderly women's economic welfare.     

The exponentiated reduced Kies distribution: Properties and applications

Here we consider an exponentiated version of the reduced Kies distribution and discuss some of its properties. The parameters of the distribution are estimated by the method of maximum likelihood and illustrated with the help of certain real-life data sets. Asymptotic behavior of the maximum likelihood estimators of the parameters of the distribution is also studied by using certain simulated data sets.