广义岭选择指数(英文)

把岭回归的原理和方法应用到家畜性状的选择指数中 ,提出了广义岭选择指数 ,理论和实践证明的这一指数形式在一定意义上优于传统的选择指数。也可以说 ,它在一定意义上丰富了选择指数的理论 ,但不能代替经典的选择指数     

基于尾部指数回归方法的CVaR估计以及实证研究

 在险价值VaR是一种非常重要的金融风险度量方法,近期也有很多关于动态VaR以及条件VaR (CVaR) 等方面的研究。根据金融资产的收益率具有重尾特征这一事实,本文假定金融资产收益率服从重尾分布,并假定重尾分布的尾部指数随着收益率发生变化。本文基于尾部指数回归模型对重尾分布的尾部指数进行估计,进而得到收益率尾部数据所服从的条件分布,并首次运用该方法对条件VaR进行估计。本文对沪深300指数进行了实证研究,得到CVaR的估计,并对估计得到的CVaR的预测效果作出检验,并与传统VaR估计方法进行了对比,实证结果发现本文的方法的预测效果更好。     

外商直接投资影响因素的偏最小二乘回归建模分析

改革开放的20多年来,我国的外商直接投资逐年增加,对我国经济的快速发展发挥了重要的作用。而影响外商直接投资的因素有很多,不同的因素变量之间往往存在多重共线性或近似多重共线性关系,使得分析问题的难度加大。本文运用新型的多元统计数据分析方法——偏最小二乘(PLS)回归方法(既克服了传统方法的不足,又有效地消除了因素变量之间的多重共线性),分析了影响我国外商直接投资的因素,以及其影响程度,为我国进一步引进外商直接投资提供了有益的定量依据。     

On making causal claims: A review and recommendations

Social scientists often estimate models from correlational data, where the independent variable has not been exogenously manipulated; they also make implicit or explicit causal claims based on these models. When can these claims be made? We answer this question by first discussing design and estimation conditions under which model estimates can be interpreted, using the randomized experiment as the gold standard. We show how endogeneity – which includes omitted variables, omitted selection, simultaneity, common-method variance, and measurement error – renders estimates causally uninterpretable. Second, we present methods that allow researchers to test causal claims in situations where randomization is not possible or when causal interpretation could be confounded; these methods include fixed-effects panel, sample selection, instrumental variable, regression discontinuity, and difference-in-differences models. Third, we take stock of the methodological rigor with which causal claims are being made in a social sciences discipline by reviewing a representative sample of 110 articles on leadership published in the previous 10 years in top-tier journals. Our key finding is that researchers fail to address at least 66% and up to 90% of design and estimation conditions that make causal claims invalid. We conclude by offering 10 suggestions on how to improve non-experimental research.     

The coefficient of determination and its adjusted version in linear regression models

This article presents a comparative study of the efficiency properties of the coefficient of determination and its adjusted version in linear regression models when disturbances are not necessarily normal.     

一种预测产油量的新模型——Poisson目标规划模型

本文将目标规划模型与预测油田产油量的Poisson旋回模型相结合,得到了一种新的预测模型——Poisson目标规划模型,并将此模型应用到实际预测中,效果令人满意.     

A New Analysis Strategy for Designs With Complex Aliasing

Abstract

Nonregular designs are popular in planning industrial experiments for their run-size economy. These designs often produce partially aliased effects, where the effects of different factors cannot be completely separated from each other. In this article, we propose applying an adaptive lasso regression as an analytical tool for designs with complex aliasing. Its utility compared to traditional methods is demonstrated by analyzing real-life experimental data and simulation studies.     

面板数据的可加分位回归模型研究与应用

本文在贝叶斯分析的框架下讨论了面板数据的可加模型分位回归建模方法。首先通过低秩薄板惩罚样条展开和个体效应虚拟变量的引进将非参数模型转换为参数模型,然后在假定随机误差项服从非对称Laplace分布的基础上建立了贝叶斯分层分位回归模型。通过对非对称Laplace分布的分解,论文给出了所有待估参数的条件后验分布,并构造了待估参数的 Gibbs抽样估计算法。计算机模拟仿真结果显示,新提出的方法相比于传统的可加模型均值回归方法在估计稳健性上明显占优。最后以消费支出面板数据为例研究了我国农村居民收入结构对消费支出的影响,发现对于农村居民来说,无论是高、中、低消费群体,工资性收入与经营净收入的增加对其消费支出的正向刺激作用更为明显。进一步,相比于高消费农村居民人群,低消费农村居民人群随着收入的增加消费支出上升速度较为缓慢。     

Data-Driven Model Choice in Multivariate Nonparametric Regression

The problem of choosing between different regression models, is investigated. A completely automatic model selection procedure is defined and asymptotic optimality results are shown. These results are stated in a general framework including as well nested as unnested situations, including as well i.i.d. as dependent samples, and without specifying any class of non-parametric smoothers. Because of the well-known curse of dimensionality, model selection is of particular interest in multivariate regression settings, and it will be discussed how the previous general model choice approach can be used in several different highdimensional situations.     

Confidence Interval Estimation in Ultrastructural Model

The present article proposes a methodology to construct confidence interval for slope parameter and joint confidence region for intercept term and slope parameter in an ultra-structural model with equation error and correlated measurement errors.