参差重复频率分析和设计

雷达工作于脉冲工作状态时不可避免的会出现盲速现象。可通过参差重频的方法减少盲速带来的影响。在推导频率响应的基础上,该文采用最优化设计的方法进行参差周期的设计,详细考虑并推导得到了最优化搜索中几个重要参数,给出了其解析表达式。仿真结果和频率响应图表明,该方法得到的参差周期能将慢速扩大到原来盲速的数倍以上,有效地减少了盲速的影响。     

Aalen's Linear Model for Sampled Risk Set Data: A Large Sample Study

Borgan and Langholz (1997) describe a method for estimating the parameter functions in Aalen's linear hazard regression model from sampled risk set data. Using a counting process formulation and the martingale central limit theorem, we provide a study of the asymptotic distributional properties of the estimator. The results are applied to study the efficiencies of the nested case-control and counter-matched designs relative to a full cohort analysis.     

两类广义预测控制算法的统一推广

基于ＣＡＲＩＭＡ模型和ＡＲＭＡＸ模型的广义预测控制算法是两类基本的ＧＰＣ算法，给出了它们的统一格式，对ＧＰＣ算法的理论分析和实际应用有意义。     

譜分析和滤波兼容的模拟系統

本文利用序列的双重性原理对移动傅立叶变换作了深入的研究.根据频率采样理论,将模拟系统的传递函数H(ω)分解为若干子系统之和,并画出其结构框图,这个系统可以用来测量模拟信号的移动离散谱,也可用作低通、带通或梳状滤波器.最后还给出实现该系统的具体方框图.     

文献资源建设中的剔除环节──文献资源建设理论与实践研讨之二

剔除不仅具有清除陈旧失效文献、减轻工作人员负担、减少书库压力、缓解空间紧张的作用，在文献资源建设中还能发挥净化、优化功能；活化功能；控制功能；反馈功能；检验功能；复苏功能。这就使得剔除成为文献资源建设中不可缺少的重要环节。剔除没有绝对的统一的标准，一般只能以文献的内容、外形、文献的年龄以及使用情况为依据，综合归纳拟定出适用于自己馆的文献剔除标准和范围。剔除的方法多种多样，最好将几种方法结合使用，取长补短，效果会更好些。     

抗干扰、自适应、自组织、自恢复通信网技术

扼要介绍了抗干扰、自适应、自组织、自恢复通信网的组网原理和方法，提出了自组网算法，利用该算法完成了通信网网络拓扑结构的重组设计。     

Martingale generalizations and preservation of martingale properties

This article investigates circumstances under which seven important martingale properties are preserved by the following six martingale generalizations: quasimartingales, amarts, martingales in the limit, games fairer with time, progressive martingales, and eventual martingales.     

Asymptotic properties of a generalized regression-type predictor of a finite population variance in probability sampling

A system of predictors for estimating a finite population variance is defined and shown to be asymptotically design-unbiased (ADU) and asymptotically design-consistent (ADC) under probability sampling. An asymptotic mean squared error (MSE) of a generalized regression-type predictor, generated from the system, is obtained. The suggested predictor attains the minimum expected variance of any design-unbiased estimator when the superpopulation model is correct. The generalized regression-type predictor and the predictor suggested by Mukhopadhyay (1990) are compared.     

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).     

Optimal use of two auxiliary variables in double sampling

Double sampling scheme is used when cheap auxiliary variables may be measured to improve the estimation of a finite population parameter. Several estimators for population mean, ratio of means and variance are available, when two dependent samples are drawn. However, there are few proposals for the case of independent samples. In this paper both cases of dependent and independent samples are dealt with. A general approach for estimating a finite population parameter is given, showing that all the proposed estimators are particular cases of the same general class. The minimum variance bound for any estimator in this class is provided (at the first order of approximation). Furthermore, an optimal estimator which reaches this minimum is found.