ON SELECTING VARIABLES AND ASSESSING THEIR PERFORMANCE IN LINEAR DISCRIMINANT ANALYSIS

Linear discriminant analysis between two populations is considered in this paper. Error rate is reviewed as a criterion for selection of variables, and a stepwise procedure is outlined that selects variables on the basis of empirical estimates of error. Problems with assessment of the selected variables are highlighted. A leave-one-out method is proposed for estimating the true error rate of the selected variables, or alternatively of the selection procedure itself. Monte Carlo simulations, of multivariate binary as well as multivariate normal data, demonstrate the feasibility of the proposed method and indicate its much greater accuracy relative to that of other available methods.     

Regression Estimation Using Multivariate Failure Time Data and a Common Baseline Hazard Function Model

Recent ‘marginal’ methods for the regression analysis of multivariate failure time data have mostly assumed Cox (1972)model hazard functions in which the members of the cluster have distinct baseline hazard functions. In some important applications, including sibling family studies in genetic epidemiology and group randomized intervention trials, a common baseline hazard assumption is more natural. Here we consider a weighted partial likelihood score equation for the estimation of regression parameters under a common baseline hazard model, and provide corresponding asymptotic distribution theory. An extensive series of simulation studies is used to examine the adequacy of the asymptotic distributional approximations, and especially the efficiency gain due to weighting, as a function of strength of dependency within cluster, and cluster size. This revised version was published online in July 2006 with corrections to the Cover Date.     

A Range-Based Multivariate Stochastic Volatility Model for Exchange Rates

In this paper we present a parsimonious multivariate model for exchange rate volatilities based on logarithmic high-low ranges of daily exchange rates. The multivariate stochastic volatility model decomposes the log range of each exchange rate into two independent latent factors, which could be interpreted as the underlying currency specific components. Owing to the empirical normality of the logarithmic range measure the model can be estimated conveniently with the standard Kalman filter methodology. Our results show that our model fits the exchange rate data quite well. Exchange rate news seems to be currency specific and allows identification of currency contributions to both exchange rate levels and exchange rate volatilities.     

A cohort analysis of female labor participation rates in the U.S. and Japan

Aggregate data of female labor participation rates in U.S. and Japan, classified by period and by age, are decomposed into age, period, and cohort effects using innovative Bayesian cohort models that were developed to overcome the identification problem in cohort analysis. The main findings are that in both countries, age effects are the largest and period effects are the smallest; in both countries, age effects are roughly consistent with life-cycle movements expected by labor economics, but the negative effects of marriage and/or childbearing on women?’s labor supply in Japan are much larger than those observed in the U.S.; and in both countries, upward movements of cohort effects during 1930s–1960s were found. However, cohort effects are larger for the U.S. than for Japan. All the cohort results are roughly consistent with the marriage squeeze hypothesis and the Easterlin hypothesis.     

降息政策与储蓄增长

自 1996年来 ,为了刺激消费 ,扩大内需 ,促进经济的复苏与繁荣 ,中国人民银行连续八次降低金融机构人民币存贷款利率 ,但我国储蓄依然居高不下 ,降息的政策效果并不明显。该文认为 ,全球经济萧条是外因 ,而经济主体 ,如居民、企业、银行、政府的行为才是导致降息低效的根本原因 ,因此 ,解决这一现实问题需要全社会共同努力 ,将降息与其它政策配套使用才能行之有效。     

Crime and the city: Domestic burglary and the built environment in Tehran

Environmental criminologists have correlated crime patterns with the environmental and physical layout of places where crimes occur. This paper reports on some of the findings derived from a major research carried out in Tehran, focusing on the link between built environment and residential burglary. After a brief review of crime prevention approaches in the built environment and their theoretical basis, the paper examines how burglars select their targets, and investigates the significance of geographical distance and target hardening for crime prevention. It shows the limitations of distance and target hardening as deterrents, their potential impact on deepening the city's socio-spatial divide, and their negative implications for the quality of the urban environment.     

浅论环境犯罪危险犯

环境犯罪危险犯从类型上来看，大致可以分为两类：一类是具体危险犯和抽象危险犯，一类是故意危险犯和过失危险犯。当前，西方发达国家多已对环境犯罪危险犯有了完备的刑事立法，现代的环境立法价值取向的转变以及环境犯罪的特点决定了确立环境犯罪危险犯具有必要性，因此，我国也应尽早对环境犯罪危险犯进行刑事立法。     

Trouble en Route: Drug Trafficking and Clientelism in Rio de Janeiro Shantytowns

This article examines the ways that endemic drug trafficking has affected local level politics in Rio de Janeiro, Brazil. Drawing on ethnographic analysis of political exchanges in the 1998 national elections in three favelas (shantytowns), I argue that drug trafficking has changed the practice of clientelism. Evidence and analysis in this paper will show that the persistence of drug trafficking in the city has led to the emergence of a two-tiered clientelist system in which politicians make exchanges with traffickers who then, in turn, provide some benefits to favela residents in return for their votes. This results in an arrangement that provides votes to politicians and limited assistance to the poor but does little to build the legitimacy of the political system.

Irregular Identification,Support Conditions,and Inverse Weight Estimation

In weighted moment condition models, we show a subtle link between identification and estimability that limits the practical usefulness of estimators based on these models. In particular, if it is necessary for (point) identification that the weights take arbitrarily large values, then the parameter of interest, though point identified, cannot be estimated at the regular (parametric) rate and is said to be irregularly identified. This rate depends on relative tail conditions and can be as slow in some examples as n^−1/4. This nonstandard rate of convergence can lead to numerical instability and/or large standard errors. We examine two weighted model examples: (i) the binary response model under mean restriction introduced by Lewbel (1997) and further generalized to cover endogeneity and selection, where the estimator in this class of models is weighted by the density of a special regressor, and (ii) the treatment effect model under exogenous selection (Rosenbaum and Rubin (1983)), where the resulting estimator of the average treatment effect is one that is weighted by a variant of the propensity score. Without strong relative support conditions, these models, similar to well known “identified at infinity” models, lead to estimators that converge at slower than parametric rate, since essentially, to ensure point identification, one requires some variables to take values on sets with arbitrarily small probabilities, or thin sets. For the two models above, we derive some rates of convergence and propose that one conducts inference using rate adaptive procedures that are analogous to Andrews and Schafgans (1998) for the sample selection model.     

我国民间借贷的风险防范研究

我国民间借贷所存在的融资优势是：市场定位适当;效率高、期限短;信息渠道畅通,准入条件较低;对中小企业发展有推动作用。近年来民间借贷发展很快,出现了一系列问题,其中一些问题的出现直接扰乱了金融秩序,尤其以企业为主导进行的民间借贷活动最为明显。存在的风险是：资金大规模流向房地产行业,潜在威胁大;大规模融资易导致非法集资;高利率可能导致中小企业偿债困难。防范民间借贷风险的对策建议是：将民间借贷纳入统一的监管体系;探讨分类法律监管模式,规范中小企业民间融资;加强公众教育,抵制非法集资;建立健全民间借贷风险监督预警机制;引导民间资本选择正确的投资方向,规避投资风险。保护好民间资金市场,积极合理的引导,可以使大规模的民间借贷资金能够为我国实体经济的发展做出贡献。