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.     

对中国经济运行状况的分析

从经济的投资、消费和出口数据看,中国经济增速确实在下行。为了保持中国经济8%的增速,我们已付出了很高的代价;经过35年的高速增长,在各行各业产能基本过剩的情况下,中国经济增速正在下移。观察1950年代后日本、中国台湾和香港地区经济发展的过程,其经济高速增长后,也是一个台阶又一个台阶地下来。中国不能再靠高投资来推动经济增长。     

A lost family-planning regime in eighteenth-century Ceylon

Based on Dutch colonial registers (thombos), this paper reconstructs fertility for two districts in Ceylon, 1756–68. It overcomes challenges in data quality by establishing the outer bounds of plausible estimates in a series of scenarios. Among these, total fertility rates (TFRs) averaged 5.5 in one district, but only 2.7 in the other. These figures exclude the victims of infanticide, a custom noted in European travelogues between about 1660 and 1820. Sex ratios among children differed depending on the number of older siblings, and overall, 27?per cent of girls are missing in one district and 57?per cent in the other. There was little significant variation either in the TFR or the sex ratio by socio-economic status, suggesting that poverty was not a key factor in motivating infanticides. Instead, we argue that at least parts of Ceylon had a forward-looking culture of family planning in the eighteenth century, which was lost in subsequent decades.     

M-estimation for functional linear regression

This paper studies M-estimation in functional linear regression in which the dependent variable is scalar while the covariate is a function. An estimator for the slope function is obtained based on the functional principal component basis. The global convergence rate of the M-estimator of unknown slope function is established. The convergence rate of the mean-squared prediction error for the proposed estimators is also established. Monte Carlo simulation studies are conducted to examine the finite-sample performance of the proposed procedure. Finally, the proposed method is applied to analyze the Berkeley growth data.