A family of estimators of population variance in two-occasion rotation patterns

Abstract

In this article, we have considered the problem of estimation of population variance on current (second) occasion in two occasion successive (rotation) sampling. A class of estimators of population variance has been proposed and its asymptotic properties have been discussed. The proposed class of estimators is compared with the sample variance estimator when there is no matching from the previous occasion and the Singh et al. (2013) estimator. Optimum replacement policy is discussed. It has been shown that the suggested estimator is more efficient than the Singh et al. (2013) estimator and a usual unbiased estimator when there is no matching. An empirical study is carried out in support of the present study.     

光纤转角、位移传感器的理论研究

提出了“光纤桥”新概念,利用“光纤桥”构成了测量转角、位移的光纤传感器。利用准高斯光束假设,详细地分析、计算了系统参数对传感器性能的影响,理论上预见了测角精度可达1”,测角范围±6′。     

SIMPCA: a framework for rotating and sparsifying principal components

We propose an algorithmic framework for computing sparse components from rotated principal components. This methodology, called SIMPCA, is useful to replace the unreliable practice of ignoring small coefficients of rotated components when interpreting them. The algorithm computes genuinely sparse components by projecting rotated principal components onto subsets of variables. The so simplified components are highly correlated with the corresponding components. By choosing different simplification strategies different sparse solutions can be obtained which can be used to compare alternative interpretations of the principal components. We give some examples of how effective simplified solutions can be achieved with SIMPCA using some publicly available data sets.     

Estimation of generalized linear latent variable models

Summary.  Generalized linear latent variable models (GLLVMs), as defined by Bartholomew and Knott, enable modelling of relationships between manifest and latent variables. They extend structural equation modelling techniques, which are powerful tools in the social sciences. However, because of the complexity of the log-likelihood function of a GLLVM, an approximation such as numerical integration must be used for inference. This can limit drastically the number of variables in the model and can lead to biased estimators. We propose a new estimator for the parameters of a GLLVM, based on a Laplace approximation to the likelihood function and which can be computed even for models with a large number of variables. The new estimator can be viewed as an M -estimator, leading to readily available asymptotic properties and correct inference. A simulation study shows its excellent finite sample properties, in particular when compared with a well-established approach such as LISREL. A real data example on the measurement of wealth for the computation of multidimensional inequality is analysed to highlight the importance of the methodology.     

Small area estimation for longitudinal surveys

Over the last few years many studies have been carried out in Italy to identify reliable small area labour force indicators. Considering the rotated sample design of the Italian Labour Force Survey, the aim of this work is to derive a small area estimator which borrows strength from individual temporal correlation, as well as from related areas. Two small area estimators are derived as extensions of an estimation strategies proposed by Fuller (1990) for partial overlap samples. A simulation study is carried out to evaluate the gain in efficiency provided by our solutions. Results obtained for different levels of autocorrelation between repeated measurements on the same outcome and different population settings show that these estimators are always more reliable than the traditional composite one, and in some circumstances they are extremely advantageous.The present paper is financially supported by Murst-Cofin (2001) Lutilizzo di informazioni di tipo amministrativo nella stima per piccole aree e per sottoinsiemi della popolazione (National Coordinator Prof. Carlo Filippucci).     

南宋词科取士与制文之体关系论略

两宋的词科考试作为科举考试之一种,其制度的设立对两宋骈文的发展有着不可低估的作用。南宋文人由词科入等而入西掖、北门,进而执掌内外之制的现象颇为常见,朝廷诰命、典章巨制的写作从初习词科的模拟之作成为视草代言之体,由此也生发出一些较北宋而言更为复杂的文体观念。词科与制文的关系主要体现在:应试词科衍生出一批进卷、行卷行为,并出现了大量的类书,促进了四六文的兴盛,影响了馆阁的制书写作;对典故的谙习、制度的熟稔与制文体式的遵循是应试词科的基本要求,词科者入等者在馆职写作中也承续了词科文体。词科与制文的关系如此密切,以至宁宗嘉定以后,轻视宏词科的直接后果,是导致了两制之文的衰落。词科的发展也引起时人对"词科习气"的批评,而所谓的"词科习气",与南宋骈文对北宋骈文的变异有关。另外,由词科入等或曾应举过词科的士人中,有不少人也有着兼学理学的倾向,他们也试图在朝廷的公文写作中复返汉代遗风。     

考虑空气阻力和地球自转的高次效应的落体运动

运用逐次逼近理论 ,通过反复迭代求得了在线性阻力和科氏力作用下落体运动的高次近似解     

相位旋转的速率为2的空时分组码

速率为2的空时分组码(R2-STBC)能达到全速率全分集,然而其译码复杂度与调制阶数的平方成正比。为了降低译码复杂度,提出了一种基于相位旋转的、速率为2的空时分组码(PR-R2-STBC),发送端根据反馈信息将部分发送信号旋转一定的角度,实现了信号在传输过程中两两正交,从而使得其译码复杂度与调制阶数成正比。仿真结果显示,与R2-STBC相比,该编码仅需反馈7 bit就极大地降低了译码复杂度。     

基于旋转变换的多角度单人脸标准化算法新探

提出一种基于旋转变换的多角度单人脸标准化算法.首先计算出人脸的平面旋转角度,然后利用双线性插值的思想,推导出一种便于编程、速度快的插值方法进行旋转变换,将其矫正为正面端正人脸.     

基于时间序列分析方法的连续性抽样调查研究

针对连续性抽样调查中如何利用过去各期的调查信息来提高现期抽样估计精度的问题,引入时间序列分析方法,分别考虑连续性抽样调查中重复样本和重叠样本等不同情况,建立了不同情况下的时间序列模型,利用成熟的时间序列分析方法给出了总体特征的线性组合估计量。由于时间序列分析方法能够充分利用以往各期的调查信息,从而能够给出精度更高的估计量。