Buffon's needle and the estimation of migration distances

A procedure is suggested for estimating migration distances from data on the proportion of migrants crossing regional boundaries. The method makes use of Buffon's needle, a problem in geometrical probability from the eighteenth century that was originally used as an empirical means of estimating π. The procedure is described for various scenarios that differ in their assumptions about region shape, the spatial distribution of population, and the distribution of migration distances. An application to migration distances in the United States is given, and additional attention is given to the estimation of intraregional migration distances.     

Covariate Selection for the Semiparametric Additive Risk Model

Abstract.  This paper considers covariate selection for the additive hazards model. This model is particularly simple to study theoretically and its practical implementation has several major advantages to the similar methodology for the proportional hazards model. One complication compared with the proportional model is, however, that there is no simple likelihood to work with. We here study a least squares criterion with desirable properties and show how this criterion can be interpreted as a prediction error. Given this criterion, we define ridge and Lasso estimators as well as an adaptive Lasso and study their large sample properties for the situation where the number of covariates p is smaller than the number of observations. We also show that the adaptive Lasso has the oracle property. In many practical situations, it is more relevant to tackle the situation with large p compared with the number of observations. We do this by studying the properties of the so-called Dantzig selector in the setting of the additive risk model. Specifically, we establish a bound on how close the solution is to a true sparse signal in the case where the number of covariates is large. In a simulation study, we also compare the Dantzig and adaptive Lasso for a moderate to small number of covariates. The methods are applied to a breast cancer data set with gene expression recordings and to the primary biliary cirrhosis clinical data.     

L-白氨酸拆分手性药物的毛细管电泳实验

将教学与科研相结合,以科研成果充实和更新实验教学内容.借助一个快速、简单、实用的毛细管电泳实验,帮助学生了解毛细管电泳分析技术中的一些基本概念及其实际应用情况.     

乱针绣艺术元素在旗袍设计中的应用探究

传统的中国女装一直与刺绣艺术有着不解之缘,而当代旗袍的设计,更是将乱针绣艺术元素得到充分的应用。本文在对乱针绣艺术及旗袍礼服刺绣装饰进行阐述的基础上,对乱针绣艺术元素在旗袍设计中的创新应用效果进行探究。     

Model selection in high-dimensional noisy data: a simulation study

In many practical applications, high-dimensional regression analyses have to take into account measurement error in the covariates. It is thus necessary to extend regularization methods, that can handle the situation where the number of covariates p largely exceed the sample size n, to the case in which covariates are also mismeasured. A variety of methods are available in this context, but many of them rely on knowledge about the measurement error and the structure of its covariance matrix. In this paper, we set the goal to compare some of these methods, focusing on situations relevant for practical applications. In particular, we will evaluate these methods in setups in which the measurement error distribution and dependence structure are not known and have to be estimated from data. Our focus is on variable selection, and the evaluation is based on extensive simulations.     

Simple and effective number-of-bins circumference selectors for a histogram

Two very effective data-based procedures which are simple and fast to compute are proposed for selecting the number of bins in a histogram. The idea is to choose the number of bins that minimizes the circumference (or a bootstrap estimate of the expected circumference) of the frequency histogram. Contrary to most rules derived in the literature, our method is therefore not dependent on precise asymptotic analyses. It is shown by means of an extensive Monte-Carlo study that our selectors perform well in comparison with recently suggested selectors in the literature, for a wide range of density functions and sample sizes. The behaviour of one of the proposed rules is also illustrated on real data sets.     

Inference after variable selection using restricted permutation methods

When confronted with multiple covariates and a response variable, analysts sometimes apply a variable‐selection algorithm to the covariate‐response data to identify a subset of covariates potentially associated with the response, and then wish to make inferences about parameters in a model for the marginal association between the selected covariates and the response. If an independent data set were available, the parameters of interest could be estimated by using standard inference methods to fit the postulated marginal model to the independent data set. However, when applied to the same data set used by the variable selector, standard (“naive”) methods can lead to distorted inferences. The authors develop testing and interval estimation methods for parameters reflecting the marginal association between the selected covariates and response variable, based on the same data set used for variable selection. They provide theoretical justification for the proposed methods, present results to guide their implementation, and use simulations to assess and compare their performance to a sample‐splitting approach. The methods are illustrated with data from a recent AIDS study. The Canadian Journal of Statistics 37: 625–644; 2009 © 2009 Statistical Society of Canada     

领主属宾句的轻动词集合并探讨

生成语法对领主属宾句的研究由来已久,以管约论为支撑的轻动词说虽然具有一定的解释力,但已远远落后于生成语法理论的快速发展。在当前的最简方案框架中,探针触发内部合并,选择器则可以看做是外部合并的触发机制。轻动词在运算中以抽象的语义类型特征和语义指向特征为选择器,不仅可以解释v-V集合并的触发,也可以解释领主属宾句的生成原因。     

3600/40矿棉板成型机设计

矿棉板已成为公共建筑吊顶材料的首选,但其关键设备成型机还主要依靠进口。自主开发的3600/40矿棉板成 型机,采取湿法长网抄取成型,多点伺服电气控制,配有高浓网前箱、针形辊、上真空辊、蒸气喷洒、印花辊等先进装置,料 板厚度自动可调,具有产量大,易操作,适应低容重和高矿棉两种配方工艺,具有能耗低、质量稳定、节水等优点,可完全 替代进口。     

Maximum Test versus Adaptive Tests for the Two-Sample Location Problem

For the non-parametric two-sample location problem, adaptive tests based on a selector statistic are compared with a maximum and a sum test, respectively. When the class of all continuous distributions is not restricted, the sum test is not a robust test, i.e. it does not have a relatively high power across the different possible distributions. However, according to our simulation results, the adaptive tests as well as the maximum test are robust. For a small sample size, the maximum test is preferable, whereas for a large sample size the comparison between the adaptive tests and the maximum test does not show a clear winner. Consequently, one may argue in favour of the maximum test since it is a useful test for all sample sizes. Furthermore, it does not need a selector and the specification of which test is to be performed for which values of the selector. When the family of possible distributions is restricted, the maximin efficiency robust test may be a further robust alternative. However, for the family of t distributions this test is not as powerful as the corresponding maximum test.