A simple,automatic and adaptive bivariate density estimator based on conditional densities

The standard approach to non-parametric bivariate density estimation is to use a kernel density estimator. Practical performance of this estimator is hindered by the fact that the estimator is not adaptive (in the sense that the level of smoothing is not sensitive to local properties of the density). In this paper a simple, automatic and adaptive bivariate density estimator is proposed based on the estimation of marginal and conditional densities. Asymptotic properties of the estimator are examined, and guidance to practical application of the method is given. Application to two examples illustrates the usefulness of the estimator as an exploratory tool, particularly in situations where the local behaviour of the density varies widely. The proposed estimator is also appropriate for use as a pilot estimate for an adaptive kernel estimate, since it is relatively inexpensive to calculate.     

改革开放以来中国经济发展及1997—2010年经济发展预测(三)——中国地区经济发展的政策思路

预测“九五”至2010年中国地区经济发展,分析区域经济发展的两大特征,总结影响地区经济发展差别的基本规律,提出促进中国区域经济协调发展的政策选择。     

Fit and Skill in Employee Selection: Insights from a Study of Headhunters

We draw upon evidence from a qualitative study of headhunters to provide insights into the character and importance of candidate fit and skill for the selection of a broad range of white-collar employees. Headhunters suggest that the fit of a job candidate is assessed at two levels, one corresponding with a general compatibility with organization-level norms, culture, and strategy, the other corresponding more closely with traits and characteristics of the person or persons with whom the job candidate actually interviews. Skill—a factor which is largely neglected by those who tout the importance of fit—also plays an important and independent role in employee selection. Stalls that influence the selection of employees from a pool of candidates tend to be highly specific if not idiosyncratic, and take the form of what headhunters call hot buttons. We conclude by discussing the conceptualization, causes, and implications of fit; we also consider how the importance of fit and hot buttons challenges the explanatory logic of standard accounts of labor-market success.     

On optimal testing for the equality of equicorrelation: An example of loss in power

Sen Gupta (1988) considered a locally most powerful (LMP) test for testing nonzero values of the equicorrelation coefficient of a standard symmetric multivariate normal distribution. This paper constructs analogous tests for the symmetric multivariate normal distribution. It shows that the new test is uniformly most powerful invariant even in the presence of a nuisance parameter, σ². Further applications of LMP invariant tests to several equicorrelated populations have been considered and an extension to panel data modeling has been suggested.     

综合选择指数中的变量选择

本文首次从变量选择的角度把ＡＩＣ准则应用于动物性状的综合选择指数之中,从而获得了最佳的综合选择指数。     

Feature selection based on distance correlation: a filter algorithm

Feature selection (FS) is one of the most powerful techniques to cope with the curse of dimensionality. In the study, a new filter approach to feature selection based on distance correlation is presented (DCFS, for short), which keeps the model-free advantage without any pre-specified parameters. Our method consists of two steps: hard step (forward selection) and soft step (backward selection). In the hard step, two types of associations, between univariate feature and the classes and between group feature and the classes, are involved to pick out the most relevant features with respect to the target classes. Due to the strict screening condition in the first step, some of the useful features are likely removed. Therefore, in the soft step, a feature-relationship gain (like feature score) based on the distance correlation is introduced, which is concerned with five kinds of associations. We sort the feature gain values and implement the backward selection procedure until the errors stop declining. The simulation results show that our method becomes more competitive on several datasets compared with some of the representative feature selection methods based on several classification models.     

论东欧新马克思主义的理论定位

东欧新马克思主义指的是第二次世界大战后在东欧国家兴起的一种马克思主义流派,其历史大体上分为两个时期:20世纪50-70年代,东欧新马克思主义作为稳定的学术派别存在,具体地说,包括南斯拉夫实践派、布达佩斯学派以及波兰和捷克斯洛伐克等国的新马克思主义;70年代后.东欧新马克思主义者作为个体融入到西方学术界,但其学术创作仍然有着东欧新马克思主义的深刻烙印.东欧新马克思主义与通常意义上的西方马克思主义以及70年代后欧美新马克思主义共同构成了20世纪新马克思主义的基本格局,其中,东欧新马克思主义鲜明的理论特色在于:其一,对马克思思想独特的、深刻的阐述;其二,对社会主义理论与实践、历史与命运的反思和对社会主义改革的理论设计;其三,对现代性的独特的理论反思.     

A Method to Handle Zero Counts in the Multinomial Model

In the context of an objective Bayesian approach to the multinomial model, Dirichlet(a, …, a) priors with a < 1 have previously been shown to be inadequate in the presence of zero counts, suggesting that the uniform prior (a = 1) is the preferred candidate. In the presence of many zero counts, however, this prior may not be satisfactory either. A model selection approach is proposed, allowing for the possibility of zero parameters corresponding to zero count categories. This approach results in a posterior mixture of Dirichlet distributions and marginal mixtures of beta distributions, which seem to avoid the problems that potentially result from the various proposed Dirichlet priors, in particular in the context of extreme data with zero counts.