A consistent parameter estimation in the three-parameter lognormal distribution

In this work, we propose a consistent method of estimation for the parameters of the three-parameter lognormal distribution. We then discuss some properties of these estimators and show by means of a Monte Carlo simulation study that the proposed estimators perform better than some other prominent estimators in terms of bias and root mean squared error. Finally, we present two real-life examples to illustrate the method of estimation proposed.     

The Effects of Safety Discrimination Training and Frequent Safety Observations on Safety-Related Behavior

The intent of the present study was to assess the effects of discrimination training only and in combination with frequent safety observations on five participants' safety-related behavior in a simulated office setting. The study used a multiple-baseline design across safety-related behaviors. Across all participants and behavior, safety improved from 53% during the baseline phase to 74% during the training phase, and improved further to 89% during the training plus observation phase. The results show that both discrimination training only and in combination with frequent observations improves safety-related behavior with a medium effect size. The study also measured participants' accuracy of observation, which was the extent of their correct classification of safe and at-risk behavior. Participants' accuracy of observation was above 80% and had little correlation with safety-related behavior. Based on the various measures, conducting observations appears to serve more than a skill acquisition function for the safety-related behavior.     

The asymptotic distribution of numbers of observations near order statistics

In this paper, we discuss the limiting behavior of numbers of observations near an order statistic. We then derive an expression for the joint distribution of the numbers of observations that fall into the open right a-vicinity and left b-vicinity of k  th and (n-r)$(n-r)$th order statistics, respectively, from a sample of size n and establish the result that they are asymptotically independent under suitable conditions.     

门槛效应、经济增长与环境质量

根据“环境库兹涅茨假说”,随着经济的增长,环境质量先恶化后改善,变化过程中存在着所谓的“门槛效应”。本文采用Hansen（2000）提出的“门槛回归”方法,以108个国家和地区作为横截面数据,对影响“环境库兹涅茨曲线”的多个因素进行了门槛效应分析。结果表明：一个国家或地区的收入水平、工业发展水平和贸易开放程度都存在着“门槛效应”。我们发现,当贸易开放程度作为门槛变量时,可将“低收入-高工业”的国家分成两组。而在“低收入-低工业”、“高收入-高工业”和“高收入-低工业”的国家和地区中不再有“门槛效应”的存在。     

研发投入对企业绩效的影响研究 ——基于企业金融化水平门限回归

本文选取1435家A股上市公司2011—2018年度的面板数据,以企业金融化水平作为门限变量,研究企业研发投入对企业绩效的非线性影响关系。研究结果表明:(1)企业金融化行为给研发投入对企业绩效的促进作用带来严重的时滞效应,研发投入对当年企业绩效不存在促进作用;(2)研发投入对未来一年企业绩效有双门限效应,二者呈倒N型关系,在第二区间内研发投入促进未来一年企业绩效;研发投入对未来二年企业绩效有单门限效应,在第一区间内研发投入促进未来二年企业绩效;(3)在适度的企业金融化水平区间内,研发投入才会促进未来企业绩效。本文指出上市公司金融化水平最优区间占比分布具有区域、行业和企业性质异质性,并进一步基于实证研究结果提出了相应的政策建议,有利于企业合理管理研发投入和防止企业脱实向虚。     

转基因产品低水平混杂问题研究

转基因产品低水平混杂(LLP)是随着转基因作物商业化而出现在进出口贸易中的一个现实问题,也是当前多边贸易谈判中的一个前沿议题,近年来已成为各国政府共同的政策关注点。作为转基因作物研发、种植和农产品贸易大国,我国农业产业的多个方面都将受到转基因产品LLP的影响。文章分析了转基因产品LLP的演进逻辑,探讨了现行LLP定义的政策含义;从技术和政策层面出发,论证了进口国对转基因产品的特殊关注是LLP问题出现的根本原因,提出了加强双边合作与互认的解决思路;从进出口两方面分析了LLP问题和LLP风险管理策略的缺失对一国农业产业产生的影响,并针对其经济风险评估和政策制定提出了建议。     

我国GDP周期波动的非对称性研究方法与统计检验

论述了阈值自回归模型(TAR)和冲量阈值自回归(M-TAR)理论方法及其检验统计量的构造,并应用Bootstrap自助法来获得检验统计量的渐近P-值或渐近临界值.运用该方法分析了我国GDP增长率周期波动的"深"和"尖"两方面的非对称性特征,研究发现我国GDP增长率周期波动呈现较强的"深"型波动,而"失"的特征并不明显.同时通过对价格指数的非对称性研究,发现价格指数周期波动并不存在非对称性,因此以价格名义变量来解释我国GDP增长率周期波动的非对称性是不成立的.     

THE EFFECTS OF CORRELATION AMONG OBSERVATIONS ON THE ACCURACY OF APPROXIMATING THE DISTRIBUTION OF SAMPLE MEAN BY ITS ASYMPTOTIC DISTRIBUTION1

In this paper, we investigate the effects of correlation among observations on the accuracy of approximating the distribution of sample mean by its asymptotic distribution. The accuracy is investigated by the Berry-Esseen bound (BEB), which gives an upper bound on the error of approximation of the distribution function of the sample mean from its asymptotic distribution for independent observations. For a given sample size (n₀) the BEB is obtained when the observations are independent. Let this be BEB. We then find the sample size (n_*) required to have BEB below BEB₀, when the observations are dependent. Comparison of n_* with n₀ reveals the effects of correlation among observations on the accuracy of the asymptotic distribution as an approximation. It is shown that the effects of correlation among observations are not appreciable if the correlation is moderate to small but it can be severe for extreme correlations.     

Further theoretical results and a comparison between two methods for approximating eigenvalues of perturbed covariance matrices

Covariance matrices, or in general matrices of sums of squares and cross-products, are used as input in many multivariate analyses techniques. The eigenvalues of these matrices play an important role in the statistical analysis of data including estimation and hypotheses testing. It has been recognized that one or few observations can exert an undue influence on the eigenvalues of a covariance matrix. The relationship between the eigenvalues of the covariance matrix computed from all data and the eigenvalues of the perturbed covariance matrix (a covariance matrix computed after a small subset of the observations has been deleted) cannot in general be written in closed-form. Two methods for approximating the eigenvalues of a perturbed covariance matrix have been suggested by Hadi (1988) and Wang and Nyquist (1991) for the case of a perturbation by a single observation. In this paper we improve on these two methods and give some additional theoretical results that may give further insight into the problem. We also compare the two improved approximations in terms of their accuracies.     

The Identification of Multiple Outliers in ARIMA Models

Abstract

There are three main problems in the existing procedures for detecting outliers in ARIMA models. The first one is the biased estimation of the initial parameter values that may strongly affect the power to detect outliers. The second problem is the confusion between level shifts and innovative outliers when the series has a level shift. The third problem is masking. We propose a procedure that keeps the powerful features of previous methods but improves the initial parameter estimate, avoids the confusion between innovative outliers and level shifts and includes joint tests for sequences of additive outliers in order to solve the masking problem. A Monte Carlo study and one example of the performance of the proposed procedure are presented.