A new heteroscedastic regression to analyze mass loss of wood in civil construction in Brazil

A heteroscedastic regression based on the odd log-logistic Marshall–Olkin normal (OLLMON) distribution is defined by extending previous models. Some structural properties of this distribution are presented. The estimation of the parameters is addressed by maximum likelihood. For different parameter settings, sample sizes and some scenarios, various simulations investigate the performance of the heteroscedastic OLLMON regression. We use residual analysis to detect influential observations and to check the model assumptions. The new regression explains the mass loss of different wood species in civil construction in Brazil.     

我国行政事业单位国有资产管理探析

行政事业单位资产是我国国有资产的重要组成部分,在履行政府职能、加强公共管理、实施宏观调控、推动经济建设等方面发挥着重要作用。随着经济体制改革的深化,行政事业单位国有资产管理体制改革成为不可回避的一个紧迫问题。这涉及到行政事业单位国有资产的监管问题、如何发挥行政事业单位国有资产的社会效益问题、怎样防止国有资产的流失问题等。     

中国经济发展中的深层次问题

中国经济正面临何去何从的方向性问题:是进一步深化改革,抓大放小,无为而治,让市场发挥越来越多的作用;还是国进民退,让政府主导经济,发挥更多的作用?答案无疑是前者。基于以上判断,中国经济的长期深层次根本问题在于:深层次制度障碍未破除,深层次市场化改革滞后,政府、社会与市场的治理边界界定不清晰、不合理。这就导致了三重三轻的不科学发展观:重政府轻市场、重国富轻民富、重发展轻服务。要解决这些深层次问题,下一步改革的关键在于深入推进政府职能的两个根本性转变:第一,从发展型政府向服务型政府转变;第二,从全能型政府向有限型政府转变。     

高校大学生行为缺失的表现与对策

近些年,大学生行为缺失现象日益突出,本文对当前大学生行为缺失现象加以分析,对当代大学生存在的各种行为缺失现象进行归纳,找出这些现象产生的根源,并为防止这些现象的发生提出相应对策。     

再说的词汇化及相关问题

词汇化、语法化在共时和历时两个层面上都是一个连续、动态的发展序列.在这个序列中,涉及到词汇化的内部层次和语法化的先后进程.针对再说及一组相关形式(包括语法词、固化短语、照应模式等)的调查说明:再说经历了词汇化、语法化交叠发生,词化后继续语法化的发展历程;成为词汇词是再说词汇化的终点,但并不是它语法化的终点.     

我国政府不能放弃组织公有经济的职能

政府参与组织实体经济,是社会经济发展规律的内在要求,被社会主义市场经济的实践证明更能促进社会生产力的发展,由建立以公有制为主体的基本经济制度所决定。为此,我国政府要主动担责,不怕被问责,不怕被指责。     

The empirical Bayes estimators of the parameter of the Poisson distribution with a conjugate gamma prior under Stein's loss function

For the hierarchical Poisson and gamma model, we calculate the Bayes posterior estimator of the parameter of the Poisson distribution under Stein's loss function which penalizes gross overestimation and gross underestimation equally and the corresponding Posterior Expected Stein's Loss (PESL). We also obtain the Bayes posterior estimator of the parameter under the squared error loss and the corresponding PESL. Moreover, we obtain the empirical Bayes estimators of the parameter of the Poisson distribution with a conjugate gamma prior by two methods. In numerical simulations, we have illustrated: The two inequalities of the Bayes posterior estimators and the PESLs; the moment estimators and the Maximum Likelihood Estimators (MLEs) are consistent estimators of the hyperparameters; the goodness-of-fit of the model to the simulated data. The numerical results indicate that the MLEs are better than the moment estimators when estimating the hyperparameters. Finally, we exploit the attendance data on 314 high school juniors from two urban high schools to illustrate our theoretical studies.     

损失厌恶、后悔厌恶与沉没成本效应的关联性分析

以往研究认为沉没成本效应的产生与损失厌恶和后悔厌恶相关,但对其相互关系的探讨较少考虑货币性沉没成本和非货币性沉没成本的不同影响.文章选取证券监管者和证券市场投资者作为被试对象,通过有情境因素的调查问卷对损失厌恶、后悔厌恶与沉没成本效应之间的关联性进行验证.实证结果发现,与证券市场投资者相比,证券监管者的损失厌恶倾向要显著更低,而二者的后悔厌恶和沉没成本效应则无显著差异;证券监管者的数据结果表明,后悔厌恶与沉没成本效应之间存在显著的相关性,而损失厌恶与沉没成本效应之间不存在显著的相关性;相比之下,证券市场投资者的数据结果则表明,损失厌恶与沉没成本之间存在显著的相关性,而后悔厌恶与沉没成本之间则不存在显著的相关性.     

Oracle Inequalities for Convex Loss Functions with Nonlinear Targets

This article considers penalized empirical loss minimization of convex loss functions with unknown target functions. Using the elastic net penalty, of which the Least Absolute Shrinkage and Selection Operator (Lasso) is a special case, we establish a finite sample oracle inequality which bounds the loss of our estimator from above with high probability. If the unknown target is linear, this inequality also provides an upper bound of the estimation error of the estimated parameter vector. Next, we use the non-asymptotic results to show that the excess loss of our estimator is asymptotically of the same order as that of the oracle. If the target is linear, we give sufficient conditions for consistency of the estimated parameter vector. We briefly discuss how a thresholded version of our estimator can be used to perform consistent variable selection. We give two examples of loss functions covered by our framework.     

Temporal Disaggregation: Methods,Information Loss,and Diagnostics

This research provides a generalized framework to disaggregate lower-frequency time series and evaluate the disaggregation performance. The proposed framework combines two models in separate stages: a linear regression model to exploit related independent variables in the first stage and a state–space model to disaggregate the residual from the regression in the second stage. For the purpose of providing a set of practical criteria for assessing the disaggregation performance, we measure the information loss that occurs during temporal aggregation while examining what effects take place when aggregating data. To validate the proposed framework, we implement Monte Carlo simulations and provide two empirical studies. Supplementary materials for this article are available online.