The effects of teenage fertility on young adult childbearing

Numerous studies of fertility behavior find that an early age at first birth increases the rate of subsequent childbearing. Typically, however, these studies do not account for the possibility of serial correlation in the unobserved determinants of fertility. Using 1979–1992 individual-level data from the National Longitudinal Survey of Youth, this paper employs the Method of Simulated Moments to estimate panel probit models of annual birth outcomes. The panel probit models account for several alternative sources of serial correlation. Estimation reveals that once serial correlation is taken into account, the subsequent fertility effects of early childbearing are either statistically eliminated or reversed. JEL classification: J13 Received March 3, 1995 / Accepted February 23, 1996     

论张承志小说中的英雄美人模式

在张承志的许多小说中,都有一种由年轻男主人公和他身边的年轻女性构成的英雄美人模式。前期小说中,英雄期望在对外部世界的征服中成为主流英雄,美人是他英雄表演的欣赏者和崇拜者;后期小说中,英雄则陷入了英雄失路的绝望境地,美人是给他温暖、抚慰和救助的女神。张承志在小说中一再构建英雄美人模式,体现了他强烈的拟英雄认同感。     

模拟情景训练在《企业管理》教学中的应用

<企业管理>教学中,一个突出问题是如何更好地培养、提高学生的实践技能.开展模拟情景训练式教学,使学生身临其境地分析处理各类管理问题,能够较好地解决教学中理论联系实际的难题.     

提高非直喷柴油机热力学模型模拟精度的途径

本文探讨了非直喷式柴油机零维热力学模型计算精度的有关问题．指出了合理选择主、副室的代用燃烧函数和正确确定主、副室间连接通道瞬态流量系数，是提高模拟计算精度的有效途径；并在S195涡流室柴油机上得到实际验证．     

论南朝拟公文体俳谐文

南北朝时期,一些南朝文人模拟公文形式创作了许多俳谐文。这些俳谐文的出现并非偶然现象,可以从当时特定的时代背景下文体的发展变化、文人的思想观念等方面找到其原因。从创作目的上看,其中有的具有明显的现实针对性,有的纯为解颐之资,但在有意无意间也反映出现实的内容。在艺术风格和创作技巧上,它们独具特色,而又与当时整个文坛的风气有一致之处。     

Marginal maximum a posteriori estimation using Markov chain Monte Carlo

Markov chain Monte Carlo (MCMC) methods, while facilitating the solution of many complex problems in Bayesian inference, are not currently well adapted to the problem of marginal maximum a posteriori (MMAP) estimation, especially when the number of parameters is large. We present here a simple and novel MCMC strategy, called State-Augmentation for Marginal Estimation (SAME), which leads to MMAP estimates for Bayesian models. We illustrate the simplicity and utility of the approach for missing data interpolation in autoregressive time series and blind deconvolution of impulsive processes.     

Near-optimal designs for dual channel microarray studies

Summary.  Much biological and medical research employs microarray studies to monitor gene expression levels across a wide range of organisms and under many experimental conditions. Dual channel microarrays are a common platform and allow two samples to be measured simultaneously. A frequently used design uses a common reference sample to make conditions across different arrays comparable. Our aim is to formulate microarray experiments in the experimental design context and to use simulated annealing to search for near-optimal designs. We identify a subclass of designs, the so-called interwoven loop designs, that seems to have good optimality properties compared with the near-optimal designs that are found by simulated annealing. Commonly used reference designs and dye swap designs are shown to be highly inefficient.     

遗传退火进化算法在背包问题中的应用

从增强算法收敛性和减少参数依赖性的角度出发，提出应用遗传退火进化算法求解背包问题，遗传退火进化算法结合了遗传算法和模拟退火算法的优点，并有效地克服了各自的弱点，使其在优化性能、优化效率和可靠性方面具有明显的优越性．阐明了用该算法求解背包问题的具体实现过程，并通过实际数值计算和结果比较表明，该算法优于遗传算法和模拟退火算法．     

Optimal and Robust Designs for Estimating the Concentration Curve and the AUC

The problem of interest is to estimate the concentration curve and the area under the curve (AUC) by estimating the parameters of a linear regression model with an autocorrelated error process. We introduce a simple linear unbiased estimator of the concentration curve and the AUC. We show that this estimator constructed from a sampling design generated by an appropriate density is asymptotically optimal in the sense that it has exactly the same asymptotic performance as the best linear unbiased estimator. Moreover, we prove that the optimal design is robust with respect to a minimax criterion. When repeated observations are available, this estimator is consistent and has an asymptotic normal distribution. Finally, a simulated annealing algorithm is applied to a pharmacokinetic model with correlated errors.     

A classification spline machine for building a credit scorecard

In constructing a scorecard, we partition each characteristic variable into a few attributes and assign weights to those attributes. For the task, a simulated annealing algorithm has been proposed. A drawback of simulated annealing is that the number of cutpoints separating each characteristic variable into attributes is required as an input. We introduce a scoring method, called a classification spline machine (CSM), which determines cutpoints automatically via a stepwise basis selection. In this paper, we compare performances of CSM and simulated annealing on simulated datasets. The results indicate that the CSM can be useful in the construction of scorecards.