Bayesian estimation of rare sensitive attribute

Randomized response models have been used to estimate a population proportion of a sensitive attribute. A randomized device is typically employed to protect respondent's privacy in a survey. In addition, an unrelated question is asked to improve the statistical efficiency. In this article, we propose Bayesian estimation of rare sensitive attribute using randomized response technique, which includes a rare unrelated attribute. Two cases are considered, the proportion of a rare unrelated attribute is known and unknown. A simulation study is conducted to assess the performance of the models using mean absolute error and coverage probability. The results show that the performance depends on the parameters and is robust to priors.     

基于在线评论数据的产品需求趋势挖掘

随着经济水平的提高和物质生活的丰富，消费者的需求变化也越来越快。能否迎合市场需求的变化是企业产品成功的关键。随着社交媒体的发展，消费者为了分享购物体验发表了许多在线评论信息，其中蕴含着消费者的需求变化。本文在产品特征提取和属性情感分析的基础上，构建了垃圾评论识别模型。然后，利用时间序列分析模型预测下阶段的产品属性关注度和情感计算。最后结合历史数据的变化趋势，分析产品属性的重要性和市场满意情况。利用汽车论坛上的汽车评论数据对本文提出的研究模型进行了验证。研究结果可以为企业制定营销策略以及产品改进与创新提供决策支持。     

A Dynamic Model of Demand for Houses and Neighborhoods

This paper develops a dynamic model of neighborhood choice along with a computationally light multi‐step estimator. The proposed empirical framework captures observed and unobserved preference heterogeneity across households and locations in a flexible way. We estimate the model using a newly assembled data set that matches demographic information from mortgage applications to the universe of housing transactions in the San Francisco Bay Area from 1994 to 2004. The results provide the first estimates of the marginal willingness to pay for several non‐marketed amenities—neighborhood air pollution, violent crime, and racial composition—in a dynamic framework. Comparing these estimates with those from a static version of the model highlights several important biases that arise when dynamic considerations are ignored.     

论基于利益分析的“环境优先”原则

从可计量、可对比角度而言,“环境优先”常被人们模糊地界定为环境利益优先于经济利益。环境利益和经济利益均应“分别兼具”人身与财产两方面的价值属性,即人身性环境利益与财产性环境利益,人身性经济利益与财产性经济利益。人身性环境利益与人身性经济利益同等重要,财产性环境利益与财产性经济利益之间也无所谓谁优先或者谁不优先。基于此,利益视角下的“环境优先”实质上是应指人身性环境利益优先于财产性经济利益。     

基于模糊理论评估连续梁桥地震破坏状况

为了研究不同强度等级地震作用下连续梁桥的破坏模式及破坏程度,采用多级模糊综合评判方法建立连续梁桥的综合评估模型。根据连续梁桥的特点,利用连续梁桥各单元及各构件的破坏程度建立多个因素集,根据各因素的权重和评价标准,对多个因素集进行逐级评判,根据最大隶属度原则得到地震作用下连续梁桥的破坏情况,并通过等级参数评判法对不同地震强度下连续梁桥的破坏情况进行等级评判,得到连续梁桥破坏情况与地震强度之间的关系。最后,基于多级模糊综合评判方法对一座连续梁桥实例进行评判     

An empirical investigation of the box-cox model and a nonlinear least squares alternative

In this paper we present a generalized functional form estimator, recently developed by jeffrey Wooldridge; and then we compare it empirically to the popular Box-Cox (BC) estimator using three data sets. We begin by briefly reviewing the drawbacks of the BC estimator. We Then introduce the nonlinear lest squares (NLS) alternative of Wooldridge which retains the desirable qualities of the BC estimator without the associated theoretical problems. We continue by applying both the BC and the NLS models to data from three classic hedonic regression studies and then compare the estimation resuts-point estimates, inferences and fitted values. The estimations include a wage rate equation, and two computer hedonic regression equations, one using data from a classic study by Gregory Chow and the other using an IBM data set that formed the basis of the new official BLS computer price index.     

混合学习法前向网络多属性储层参数预测

应用前向网络描述地震属性和储层参数间的非线性映射关系时，经典的误差反向传播算法存在收敛速度慢，易陷入局部极值等诸多不足。研究了融合粒子群优化算法和误差反向传播算法的混合学习法前向网络多属性储层参数预测技术。粒子群优化算法是一种群体随机搜索演化计算技术，具有较快的收敛速度和较强的全局搜寻能力；误差反向传播算法本质上是梯度下降算法，注重局部搜索。混合学习法为两种学习算法交替执行，首先以粒子群优化算法训练网络，当误差能量在规定的迭代次数内不再发生变化时，采用误差反向传播算法实现局部寻优。理论函数逼近测试和实际储层参数预测实验说明了混合学习法具有学习时间短、求解效率高、可靠性强的优点，具有良好的应用前景。     

私人刑事调查的特征与类别考

私人刑事调查作为自我保护与私力救济的重要手段,具有重要现实价值。通过分析归纳,认为其具有实施主体的非国家公权力化、主要手段的非强制化和直接目的的私力救济化等特征。在主要类别上,它分为私家侦探刑事调查、律师私下刑事调查和其他私主体刑事调查三大类。     

住宅特征价格分析——以广州为例

以广州二手交易的596个高层楼盘、3703个交易样本为研究基础，采用特征价格法测度了影响广州市住宅价格的主要因素，它们分别是与商业中心的距离、与山江公园的距离、与地铁的距离、噪音、户型间隔、装修档次、朝向、楼层、楼龄、阳台数量10项因素，并给出了影响因素的隐含价值。     

基于Vague集和属性综合评价的股票投资价值分析方法

对二级市场上同一行业内的不同股票进行投资价值分析是一个系统综合评价问题。本文从评价指标的选择、单项指标的评价、多项指标的综合评价和属性识别准则四部分来阐述属性测度理论在股票投资价值分析中的应用。特别需要指出的是,股票投资价值评价指标体系的建立和指标权重的确定应用了Vague集的理论和方法。最后,我们应用属性综合评价方法对电子元器件制造业的公司股票进行投资价值分析,结果验证了该方法的实用性和正确性。