On the Rounded Integer-Valued Autoregressive Process

In recent years, there has been a growing interest in modelling integred-valued time series. In this article, we propose a modified and generalized version of the first order rounded integer-valued autoregressive RINAR(1) model, originally introduced by Kachour and Yao (2009 Kachour , M. , Yao , J. F. ( 2009 ). First-order rounded integer-valued autoregressive (RINAR(1)) process . Journal of Time Series Analysis 30 ( 4 ): 417 – 448 .[Crossref], [Web of Science ®] , [Google Scholar]). Indeed, this class can be considered as an alternative of classical models based on the thinning operators. Using a Markov chain method, conditions for stationarity and the existence of moments are investigated. Least squares estimator of the model parameters is considered and its consistence is established. Finally, we describe the price change data using a model of the new class.     

Separate Ratio Estimators for the Population Variance in Stratified Random Sampling

We propose separate ratio estimators for population variance in stratified random sampling. We obtain mean square error equations and compare proposed estimators about efficiency with each other. By these comparisons, we find the conditions which make proposed estimators more efficient than others. It has been shown that proposed classes of estimators are more efficient than usual unbiased estimator. We find that separate ratio estimators are more efficient than combined ratio estimators for population variance. The theoretical results are supported by a numerical illustration with original data. A simulation study is also carried out to investigate empirical performance of estimators.     

Statistical Analysis of Parameter Estimation for 2-D Harmonics in Multiplicative and Additive Noise

This article considers the problem of parameter estimation for two dimensional (2-D) multi-component harmonics in non zero-mean multiplicative and additive noise. The least squares estimators (LSEs) are proposed to estimate the coherent model parameters, and some statistical results of the LSEs are obtained, including strong consistency, strong convergence rate, and asymptotic normality. Furthermore, the LSEs-based estimators are proposed to estimate the noncoherent model parameters, and the strong consistency and the asymptotic normality are also proved. Finally, some numerical experiments are performed to see how the asymptotic results work for finite sample sizes.     

基于贝叶斯Wishart波动模型的原油市场与股市动态相依性研究

针对时变相关系数矩阵在多变量随机波动模型的估计问题,构建了贝叶斯动态相关Wishart波动模型。在CC-MSV模型的基础上,设置精度矩阵服从Wishart分布,使得模型的相关系数矩阵具有时变特征。通过模型的统计结构分析,选择参数先验分布,设计相应的Gibbs-MTM-ARMS混合算法,据此估计模型参数;并利用上证综合指数、标普500指数与原油期货价格数据进行实证分析。研究结果表明：模型能够有效地刻画原油市场与股票市场的动态相依性;金融危机期间,股票市场与原油市场的相关性较强,并且难以判断正负方向;金融危机后,中国股票市场与原油市场呈现极微弱的相关性,而美国股票市场与原油市场的正相关性较为明显。     

双寡头电力市场垂直合作减排的随机微分对策模型

考虑单电网公司与双发电商所组成的渠道结构,构建了发电商投资减排、电网公司投资消纳的优势互补的异质型垂直合作减排的随机微分对策模型,先后考察并比较了分散决策和集成决策下的反馈均衡结果。在此基础上,讨论了利润共享契约下系统增量利润的分配问题。研究表明：对于分散决策,电网公司选择性承担发电商的减排费用;两种决策下的发电商减排和电网公司购电价格以及分散决策下的减排补贴与发电商之间的减排竞争强度相关;在一定条件和范围内,合作博弈有利于提高电网公司购电电价,同时为此所带来的风险增大。     

“I Learned That the Aging Population Isn't That Much Different From Me”: The Final Outcomes of a Gero-Ed BEL Project

This article describes a Council on Social Work Education (CSWE) Gero-Ed Center BEL Project's activities and reports its final outcomes. An oral history interview in paired human behavior and practice skills courses addressed gerontological social work competencies focused on assessing and addressing values and biases regarding aging, and the ability to relate concepts and theories of aging to practice. Significant increases in perceived proficiency in these competencies occurred, as did significant decreases in negative attitudes toward older people and working with older adults. Qualitative data supported these results. Findings suggest social work educators utilize a combination of classroom-based and experiential learning to maximize student development.     

The relative efficiency of the restricted estimators in linear regression models

This paper deals with the problem of multicollinearity in a multiple linear regression model with linear equality restrictions. The restricted two parameter estimator which was proposed in case of multicollinearity satisfies the restrictions. The performance of the restricted two parameter estimator over the restricted least squares (RLS) estimator and the ordinary least squares (OLS) estimator is examined under the mean square error (MSE) matrix criterion when the restrictions are correct and not correct. The necessary and sufficient conditions for the restricted ridge regression, restricted Liu and restricted shrunken estimators, which are the special cases of the restricted two parameter estimator, to have a smaller MSE matrix than the RLS and the OLS estimators are derived when the restrictions hold true and do not hold true. Theoretical results are illustrated with numerical examples based on Webster, Gunst and Mason data and Gorman and Toman data. We conduct a final demonstration of the performance of the estimators by running a Monte Carlo simulation which shows that when the variance of the error term and the correlation between the explanatory variables are large, the restricted two parameter estimator performs better than the RLS estimator and the OLS estimator under the configurations examined.     

刘秀的文学创作及其成就

汉光武帝刘秀,本人为太学生出身,博览经学,具有较高的文化素养,擅长应用文撰作,其诏令及交际文牍颇注重文辞,较少＂浮词虚语＂。严可均《全后汉文》辑有刘秀113篇文字。由于其伟大历史功绩的遮掩,后人较少注意其文学撰作方面的才能及其成就,但他的一百余篇诏令及文牍足以使他在汉代文学史上占据一席之地。     

Nonparametric Kernel Methods with Errors‐in‐Variables: Constructing Estimators,Computing them,and Avoiding Common Mistakes

Estimating a curve nonparametrically from data measured with error is a difficult problem that has been studied by many authors. Constructing a consistent estimator in this context can sometimes be quite challenging, and in this paper we review some of the tools that have been developed in the literature for kernel‐based approaches, founded on the Fourier transform and a more general unbiased score technique. We use those tools to rederive some of the existing nonparametric density and regression estimators for data contaminated by classical or Berkson errors, and discuss how to compute these estimators in practice. We also review some mistakes made by those working in the area, and highlight a number of problems with an existing R package decon .