变量选择方法及其在健康食品市场研究中的应用探究

数据挖掘是在大数据中提取客观规律的方法与艺术，如何准确与快速地提取合适的特征变量是其研究的关键问题之一。在模拟分析比较各种数据挖掘算法和提取变量效果的基础上，通过对健康食品市场进行实证研究，指出目前数据挖掘算法存在的不足及发展前景。     

中国近代进化哲学论纲

19世纪末,达尔文进化论传入中国,其社会影响远大于科学影响,哲学思考远甚于科学研究.但这种解读不是释义上的流误,而是一种对中国近代社会的深刻反思.正是这种反思.使中国哲学告别了封建时代的古典形态,形成了颇具中国气派、中国风格的中国近代进化哲学.它探索和回答了当时正处于内优外患的中国向何处去这一重大社会历史问题,用物竞天择、善变应天的思想观念唤起国人救亡图存的激情和信念,推演出中国近代波澜壮阔的社会运动.同时,它还为马克思主义哲学在中国的传播准备了思想条件.     

On the inferential use of predictive sample reuse

The predicitive sample reuse (PSR) data analysis technique proposed by Geisser and Eddy (1979) is applied to the analysis of categorical data. This application yiclds a new approach which has a number of advantages over classical methods for analysis of such data. A PSR technique for comparing linear or nonlinar regressino relationships, for two or more populations, and a PSR ailernative to certain nonparaemetri statistical tesis are also proposed.     

Interview Costs in the Secretary Problem

The effect of interview costs on the optimal selection strategy and on the chance of success in secretary problems with order k selection rules, both for a finite number of applicants and in the limiting case, is examined. Probabilistic reasoning is used and numerical examples given.     

Almost nonparametric inference for repeated measures in mixture models

We consider ways to estimate the mixing proportions in a finite mixture distribution or to estimate the number of components of the mixture distribution without making parametric assumptions about the component distributions. We require a vector of observations on each subject. This vector is mapped into a vector of 0s and 1s and summed. The resulting distribution of sums can be modelled as a mixture of binomials. We then work with the binomial mixture. The efficiency and robustness of this method are compared with the strategy of assuming multivariate normal mixtures when, typically, the true underlying mixture distribution is different. It is shown that in many cases the approach based on simple binomial mixtures is superior.     

Designing selection experiments with bernoulli populations

This paper is concerned with a fixed size subset selection problem for Bernoulli populations in the framework of the indifference zone approach. The goal is to select s populationswhich contain at least c of those with the t largest success probabilities. In order to control the probability of correct selection over the preference zone extensive tables of exact minimum sample sizes have been prepared to implement the single-stage procedure generalized from the well-known Sobel-Huyett procedure. It is shown how the tables can also be employed to design certain closedsequential procedures. These procedures curtail the sampling process of the single-stage procedureand may differ in their sampling rules. Two procedures working with play-the-winner rules are described in detail     

Selection of regress or va riables when E(Y) is an unknown honlinear function

We consider the problem of deciding which of a set of p independent variables x₁ X₂J x_s we are to regard as being functionally involved in the mean of a dependent normal random variable Y and estimating E( Y) in terms of the chosen x's. This mean is an unknown function (assumed to be doubly differentiable) of some or all of the x's, so that the problem is of wide relevance. We approximate to the hypersurface in two different ways, and select within each approximation:

(a)For the situation where the mean of Y is assumed to be a linear function of the x's, we use ono of the optimum methods of selection.

(b)More generally, in the space of the X's the function will be approximately linear in a relatively small region. Accordingly this p-dimensional space is subdivided into smaller regions by a clustering procedure, and a hyperplane if fitted with in each region to aproximate to the unknown responce surface.An adaption of an optimum-regressor-selection procedure is then used to assist in the selection of the regressors

Approximate F tests are given to choose between models, including deciding how many x's to retain. Alternatively: the application of Akaike's Extended Maximum Likelihood Principle provides another way of choosing between the models and of selecting regressor variables. The methods are applied to data on glass manufacture.     

Model Selection Using Conditional Densities

In this article we propose a new method to select a discrete model f(x; θ), based on the conditional density of a sample given the value of a sufficient statistic for θ. The main idea is to work with a broad family of discrete distributions, called the family of power series distribution, for which there is a common sufficient statistic for the parameter of interest. The proposed method uses the maximum conditional density in order to select the best model.

We compare our proposal with the usual methodology based on Bayes factors. We provide several examples that show that our proposal works fine in most instances. Bayes factors are strongly dependent on the prior information about the parameters. Since our method does not require the specification of a prior distribution, it provides a useful alternative to Bayes factors.     

A modified area under the ROC curve and its application to marker selection and classification

The area under the ROC curve (AUC) can be interpreted as the probability that the classification scores of a diseased subject is larger than that of a non-diseased subject for a randomly sampled pair of subjects. From the perspective of classification, we want to find a way to separate two groups as distinctly as possible via AUC. When the difference of the scores of a marker is small, its impact on classification is less important. Thus, a new diagnostic/classification measure based on a modified area under the ROC curve (mAUC) is proposed, which is defined as a weighted sum of two AUCs, where the AUC with the smaller difference is assigned a lower weight, and vice versa. Using mAUC is robust in the sense that mAUC gets larger as AUC gets larger as long as they are not equal. Moreover, in many diagnostic situations, only a specific range of specificity is of interest. Under normal distributions, we show that if the AUCs of two markers are within similar ranges, the larger mAUC implies the larger partial AUC for a given specificity. This property of mAUC will help to identify the marker with the higher partial AUC, even when the AUCs are similar. Two nonparametric estimates of an mAUC and their variances are given. We also suggest the use of mAUC as the objective function for classification, and the use of the gradient Lasso algorithm for classifier construction and marker selection. Application to simulation datasets and real microarray gene expression datasets show that our method finds a linear classifier with a higher ROC curve than some other existing linear classifiers, especially in the range of low false positive rates.     

巫蛊、信仰与秩序的形成——以湘西M乡“中蛊”现象为例

本文从湘西自治州M乡的"中蛊"现象出发,重点讨论巫蛊信仰、巫蛊功能以及巫蛊规则等问题。通过田野调查及个案研究发现,巫蛊信仰应该被认为是一种内心的确信,这构成了巫蛊文化的心理基础。巫蛊具有社会整合和社会控制功能,巫蛊功能的发挥一方面依靠作为心理基础的巫蛊信仰,另一方面依靠具体的巫蛊规则。巫蛊规则符合"另类规范"的特征,应该被视为一种典型的"另类规范","另类规范"对于地方"小传统"社会秩序的形成及维系起着极为重要的作用。