An efficient and fast algorithm for estimating the parameters of two-dimensional sinusoidal signals

In this paper we propose a computationally efficient algorithm to estimate the parameters of a 2-D sinusoidal model in the presence of stationary noise. The estimators obtained by the proposed algorithm are consistent and asymptotically equivalent to the least squares estimators. Monte Carlo simulations are performed for different sample sizes and it is observed that the performances of the proposed method are quite satisfactory and they are equivalent to the least squares estimators. The main advantage of the proposed method is that the estimators can be obtained using only finite number of iterations. In fact it is shown that starting from the average of periodogram estimators, the proposed algorithm converges in three steps only. One synthesized texture data and one original texture data have been analyzed using the proposed algorithm for illustrative purpose.     

面向在线定制的配置BOM及约束下在线配置过程研究

分析了顾客在线定制产品时重视的因素;提出了面向在线定制的配置BOM的概念,并且详细介绍了其具体内容;定义了在线配置过程的原则和配置依据;应用配置矩阵对零部件和零件属性的配置过程进行了详细的分析,同时给出了配置后产品的价格计算方法.在文章结论中,将本文提出的概念和方法与文献中提到的方法进行了仔细的比较,总结出其主要优势.     

家族企业权力配置对会计稳健性影响的实证分析

以2005~2014年福建省家族企业作为研究对象,考察家族企业的权力配置对企业会计稳健性的影响。通过对310个样本企业的实证分析发现,与家族企业实际控制人不担任董事长和总经理任何一个职位的企业相比,家族企业实际控制人担任董事长、总经理之一或者同时担任董事长和总经理的企业,会计稳健性更差;家族控制力越强,会计稳健性越差。研究结果表现,家族介入企业运营管理削弱了企业会计信息质量。     

Least Squares Consistent Estimates for Arbitrary Regression Functions Over an Abstract Space

In this article, we propose a new approach to sieve estimation for a general regression function when the dimension of the finite dimensional subspaces is a random quantity depending on the values of the observations.

The technique is introduced with the help of a simulation study on a functional linear model under extremely mild assumptions.

A sketch of the proof concerning the main statements is then given in the more general case when the regression function is not necessarily linear.     

Regularization and variable selection for infinite variance autoregressive models

Autoregressive models with infinite variance are of great importance in modeling heavy-tailed time series and have been well studied. In this paper, we propose a penalized method to conduct model selection for autoregressive models with innovations having Pareto-like distributions with index α∈(0,2)$\alpha \in (\mathrm{0,2})$. By combining the least absolute deviation loss function and the adaptive lasso penalty, the proposed method is able to consistently identify the true model and at the same time produce efficient estimators with a convergence rate of n^−1/α$n^{-1/\alpha }$. In addition, our approach provides a unified way to conduct variable selection for autoregressive models with finite or infinite variance. A simulation study and a real data analysis are conducted to illustrate the effectiveness of our method.     

On the correct regression function (in L2) and its applications when the dimension of the covariate vector is random

We derive the optimal regression function (i.e., the best approximation in the L₂ sense) when the vector of covariates has a random dimension. Furthermore, we consider applications of these results to problems in statistical regression and classification with missing covariates. It will be seen, perhaps surprisingly, that the correct regression function for the case with missing covariates can sometimes perform better than the usual regression function corresponding to the case with no missing covariates. This is because even if some of the covariates are missing, an indicator random variable δ$\delta$, which is always observable, and is equal to 1 if there are no missing values (and 0 otherwise), may have far more information and predictive power about the response variable Y than the missing covariates do. We also propose kernel-based procedures for estimating the correct regression function nonparametrically. As an alternative estimation procedure, we also consider the least-squares method.     

浅析语境化暗示配置方式对图像意义的影响

语境/语境化虽是许多学科的常用概念,但迄今鲜有著作能确切地界定它。最近,国外在话语分析方面的研究取得了不少进步和共识,其中最重要的一点就是将语境/语境化视作对客观世界在人们心理层面的选择性呈现。这一理论构想引出不少新研究话题,比如对语境化过程中选择性心理呈现具体细节的研究。作为对上述问题的初步探讨,通过分析一幅漫画中的意义语境化过程来说明语境化暗示配置方式对图像意义解读的影响,并在此基础上讨论了这种影响对话语分析实践的启示。     

增设恶意串通诉讼罪的立法构想

恶意串通诉讼行为不仅对司法机关正常活动有严重的危害,而且侵害了他人的财产权益,因而我国刑法应当增设恶意串通诉讼罪.恶意串通诉讼罪应属于妨害司法罪的一个具体犯罪,侵犯的客体具有双重性,既侵犯国家司法机关的正常活动,又侵犯公私财产所有权,但有主次之分;客观方面表现民事诉讼双方当事人暗中勾结,虚构民事法律关系和案件事实,提供虚假证据,在法院面前相互配合进行表演,蒙蔽法院,情节严重的行为;主体为一般主体,包括自然人和单位;主观方面是故意.在刑罚配置上,恶意串通诉讼罪的最低刑不应低于普通诈骗罪,而最高刑不应高于妨害司法的犯罪.     

印尼J区块层序地层与沉积相特征研究

利用层序地层学理论,对印尼J区块古近系—新近系的层序地层和沉积相特征进行了研究。古近系—新近系可以分为7个中期基准面旋回（SQ1SQ7）和17个短期基准面旋回。SQ1SQ7沉积时期,沉积环境从陆相河流环境逐渐过渡到三角洲环境和浅海环境,再到河流环境,为一完整的水进-水退旋回。中下部层序上升旋回占主体,属海进式沉积组合序列;上部层序下降旋回占主体,多为海退式沉积组合序列。结合层序特征,指出区块西部凸起带SQ5旋回的浊积砂体、基底风化壳、碳酸盐岩台地和东部斜坡带的下切谷为区块的有利勘探区带。研究结果对该盆地的石油勘探具有指导意义。     

代际学习：互联网时代社会学的新议题

数字革命的发生使20世纪下半叶以来备受关注的代沟问题又增添了新内涵,即数字鸿沟。老龄化社会与互联网时代的交叠引发了一种被称作代际学习的社会事实,代际学习过程正在导致一种新的代际形构,在从代际融合到社会整合这种静态结构层面,以及在从社会传承和社会更新这种动态发展维度,都呈现了诸多新的功能。而探究和认识这种新的代际形构及其功能正是社会学的学术使命所在,将研究成果运用于相关的社会政策、工作机制和实务领域,则是社会学的社会功能体现。