The State of American Boyhood

The existence of a “boy crisis” in the United States is a topic of public policy debate. This study examines the state of American boyhood, using not only the commonly reviewed indicators of school achievement but also mental health, premature death, injury, delinquency, and arrests. Boys are in trouble in many areas: low rates of literacy, low grades and engagement in school, high dropout from school, and dramatically higher rates of placement in special education, suicide, premature death, injuries, and arrests. Girls, however, suffer from other problems, especially depression, suicidal ideation and attempts, and eating disorders, and are less likely to achieve at the very highest levels in mathematics and science. This study argues that both boys and girls suffer from characteristic problems, but the issues affecting boys are serious and neglected.

Estimation of parameters in heavy-tailed distribution when its second order tail parameter is known

Estimating parameters in heavy-tailed distribution plays a central role in extreme value theory. It is well known that classical estimators based on the first order asymptotics such as the Hill, rank-based and QQ estimators are seriously biased under finer second order regular variation framework. To reduce the bias, many authors proposed the so-called second order reduced bias estimators for both first and second order tail parameters. In this work, estimation of parameters in heavy-tailed distributions are studied under the second order regular variation framework when the second order parameter in the distribution tail is known. This is motivated in large part by a recent work by the authors showing that the second order tail parameter is known for a large class of popular random difference equations (for example, ARCH models). The focus is on least squares estimators that generalize rank-based and QQ estimators. Though other possible estimators are also briefly discussed, the least squares estimators are most simple to use and perform best for finite samples in Monte Carlo simulations.     

Proper Bayes minimax estimators of the normal mean matrix with common unknown variances

This paper addresses the problem of estimating a matrix of the normal means, where the variances are unknown but common. The approach to this problem is provided by a hierarchical Bayes modeling for which the first stage prior for the means is matrix-variate normal distribution with mean zero matrix and a covariance structure and the second stage prior for the covariance is similar to Jeffreys’ rule. The resulting hierarchical Bayes estimators relative to the quadratic loss function belong to a class of matricial shrinkage estimators. Certain conditions are obtained for admissibility and minimaxity of the hierarchical Bayes estimators.     

A Bayesian predictive inference for small area means incorporating covariates and sampling weights

The main goal in small area estimation is to use models to ‘borrow strength’ from the ensemble because the direct estimates of small area parameters are generally unreliable. However, model-based estimates from the small areas do not usually match the value of the single estimate for the large area. Benchmarking is done by applying a constraint, internally or externally, to ensure that the ‘total’ of the small areas matches the ‘grand total’. This is particularly useful because it is difficult to check model assumptions owing to the sparseness of the data. We use a Bayesian nested error regression model, which incorporates unit-level covariates and sampling weights, to develop a method to internally benchmark the finite population means of small areas. We use two examples to illustrate our method. We also perform a simulation study to further assess the properties of our method.     

A note on estimating the false discovery rate under mixture model

In this note, we focus on estimating the false discovery rate (FDR) of a multiple testing method with a common, non-random rejection threshold under a mixture model. We develop a new class of estimates of the FDR and prove that it is less conservatively biased than what is traditionally used. Numerical evidence is presented to show that the mean squared error (MSE) is also often smaller for the present class of estimates, especially in small-scale multiple testings. A similar class of estimates of the positive false discovery rate (pFDR) less conservatively biased than what is usually used is then proposed. When modified using our estimate of the pFDR and applied to a gene-expression data, Storey's q-value method identifies a few more significant genes than his original q-value method at certain thresholds. The BH like method developed by thresholding our estimate of the FDR is shown to control the FDR in situations where the p  -values have the same dependence structure as required by the BH method and, for lack of information about the proportion _π0${\pi }_0$ of true null hypotheses, it is reasonable to assume that _π0${\pi }_0$ is uniformly distributed over (0,1).     

高考填报志愿的综合决策模型

本文对高考填报志愿系统进行定量分析,采用模糊AHP方法设计了评价模型、灰色预测方法给出了预测模型,结合实例论述了模糊AHP以及灰色预测的基本过程,对填报高考志愿这个主客观信息综合集成的复杂过程具有一定的指导意义。     

Estimation of covariance matrix via the sparse Cholesky factor with lasso

In this paper, we discuss a parsimonious approach to estimation of high-dimensional covariance matrices via the modified Cholesky decomposition with lasso. Two different methods are proposed. They are the equi-angular and equi-sparse methods. We use simulation to compare the performance of the proposed methods with others available in the literature, including the sample covariance matrix, the banding method, and the L₁-penalized normal loglikelihood method. We then apply the proposed methods to a portfolio selection problem using 80 series of daily stock returns. To facilitate the use of lasso in high-dimensional time series analysis, we develop the dynamic weighted lasso (DWL) algorithm that extends the LARS-lasso algorithm. In particular, the proposed algorithm can efficiently update the lasso solution as new data become available. It can also add or remove explanatory variables. The entire solution path of the L₁-penalized normal loglikelihood method is also constructed.     

基于多元统计分析的建设工程项目投资估算方法研究

在建设工程项目生命周期的多次计价中,投资估算是最粗略的,但却又是对整个项目影响最大的。然而,由于其所处的阶段比较靠前,且工程执行过程的不可预见性,使得项目投资估算的编制显得十分困难,因此,找到一种切实可行的建设工程项目快速投资估算方法就有着非常深远的意义。故应用多元回归分析方法,借助于R软件,在大量历史数据的基础上,建立回归模型并拟合回归方程,使用Box—Cox变换对该回归方程进行多次修正,通过历史数据与模型预测数据的对照,验证了其模型的可靠性。     

高频数据驱动的连续时间模型估计

现代金融经济学中连续时间模型能够更方便地描述重要经济变量的动态过程如股价、汇率和利率等。为连续时间模型提出了一种高频数据驱动的二阶段估计方法,增强了连续时间扩展模型的弹性和可操作性。以Vasicek模型为例给出了该方法的应用实例,首先在第一阶段使用实现波动率方法估计出模型的扩散项参数,然后使用实际数据的稳态分布的前向方程估计漂移项参数。此方法对模型初始设定和优化算法依赖程度低,结果较为稳定可靠。     

空间数据分析的发展

空间统计学是研究空间问题的一门学科,它是应用数学快速发展的一个分支。尽管传统的数据分析中有许多很好的方法,但却不能完全地套用于空间数据的分析。空间模型的估计不仅与各种回归形式的假设有关,而且还与空间相关、空间异质的特性有关。从空间模型及推断、适应性估计、非参数回归、空间不相关性检验几个方面研究了空间数据分析方法的发展以及未来的趋势。