无人机载3mm波脉冲雷达信号处理系统

介绍了一个采用专用ＤＳＰ芯片ＰＤＳＰ１６５１０以及大规模ＰＬＤ芯片ＰＬＥＸ１０Ｋ实现的无人机载３ｍｍ波脉冲雷达信号处理系统。并对机载雷达普遍存在的主杂波抑制问题,提出一种在回波视频信号上抑制主杂波的方法。该系统具有处理速度快、分辨率高和灵活的可编程方式的特点,以及重量轻、体积小、可靠性高的工程实用性,并给出了具体实验测试结果。     

Toeplitz矩阵的性质及其应用

运用矩阵方法,研究了特殊的Toeplitz矩阵A和n阶幂零矩阵在相似变换下的广义逆,得到了Toeplitz矩阵的一些性质,并用这些性质刻画了幂零矩阵A在相似变换下的广义逆的表达形式.     

教师课堂教学质量综合评价方法研究

教师课堂教学质量综合评价以课堂教学质量评价指标体系为基础,综合评价方法主要包括指标加权平均法和模糊综合评价法。难点是评价指标的设计和量化处理,权集的构造。对评价结果的分析可采用绝对评价(等级判断),相对评价,动态评价(个体差异的评价)。     

Remove unwanted variation retrieves unknown experimental designs

Remove unwanted variation (RUV) is an estimation and normalization system in which the underlying correlation structure of a multivariate dataset is estimated from negative control measurements, typically gene expression values, which are assumed to stay constant across experimental conditions. In this paper we derive the weight matrix which is estimated and incorporated into the generalized least squares estimates of RUV-inverse, and show that this weight matrix estimates the average covariance matrix across negative control measurements. RUV-inverse can thus be viewed as an estimation method adjusting for an unknown experimental design. We show that for a balanced incomplete block design (BIBD), RUV-inverse recovers intra- and interblock estimates of the relevant parameters and combines them as a weighted sum just like the best linear unbiased estimator (BLUE), except that the weights are globally estimated from the negative control measurements instead of being individually optimized to each measurement as in the classical, single measurement BIBD BLUE.     

Bayesian and likelihood inference for cure rates based on defective inverse Gaussian regression models

Failure time models are considered when there is a subpopulation of individuals that is immune, or not susceptible, to an event of interest. Such models are of considerable interest in biostatistics. The most common approach is to postulate a proportion p of immunes or long-term survivors and to use a mixture model [5]. This paper introduces the defective inverse Gaussian model as a cure model and examines the use of the Gibbs sampler together with a data augmentation algorithm to study Bayesian inferences both for the cured fraction and the regression parameters. The results of the Bayesian and likelihood approaches are illustrated on two real data sets.     

Analysis of covariance under inverse Gaussian model

This paper considers the problem of analysis of covariance (ANCOVA) under the assumption of inverse Gaussian distribution for response variable. We develop the essential methodology for estimating the model parameters via maximum likelihood method. The general form of the maximum likelihood estimator is obtained in color closed form. Adjusted treatment effects and adjusted covariate effects are given, too. We also provide the asymptotic distribution of the proposed estimators. A simulation study and a real world application are also performed to illustrate and evaluate the proposed methodology.     

Moment-Implied Densities: Properties and Applications

Suppose one uses a parametric density function based on the first four (conditional) moments to model risk. There are quite a few densities to choose from and depending on which is selected, one implicitly assumes very different tail behavior and very different feasible skewness/kurtosis combinations. Surprisingly, there is no systematic analysis of the tradeoff one faces. It is the purpose of the article to address this. We focus on the tail behavior and the range of skewness and kurtosis as these are key for common applications such as risk management.     

Simulation and quality of a synthetic close-to-reality employer–employee population

It is of essential importance that researchers have access to linked employer–employee data, but such data sets are rarely available for researchers or the public. Even in case that survey data have been made available, the evaluation of estimation methods is usually done by complex design-based simulation studies. For this aim, data on population level are needed to know the true parameters that are compared with the estimations derived from complex samples. These samples are usually drawn from the population under various sampling designs, missing values and outlier scenarios. The structural earnings statistics sample survey proposes accurate and harmonized data on the level and structure of remuneration of employees, their individual characteristics and the enterprise or place of employment to which they belong in EU member states and candidate countries. At the basis of this data set, we show how to simulate a synthetic close-to-reality population representing the employer and employee structure of Austria. The proposed simulation is based on work of A. Alfons, S. Kraft, M. Templ, and P. Filzmoser [{\em On the simulation of complex universes in the case of applying the German microcensus}, DACSEIS research paper series No. 4, University of Tübingen, 2003] and R. Münnich and J. Schürle [{\em Simulation of close-to-reality population data for household surveys with application to EU-SILC}, Statistical Methods & Applications 20(3) (2011c), pp. 383–407]. However, new challenges are related to consider the special structure of employer–employee data and the complexity induced with the underlying two-stage design of the survey. By using quality measures in form of simple summary statistics, benchmarking indicators and visualizations, the simulated population is analysed and evaluated. An accompanying study on literature has been made to select the most important benchmarking indicators.     

Efficiency Bounds for Missing Data Models With Semiparametric Restrictions

This paper shows that the semiparametric efficiency bound for a parameter identified by an unconditional moment restriction with data missing at random (MAR) coincides with that of a particular augmented moment condition problem. The augmented system consists of the inverse probability weighted (IPW) original moment restriction and an additional conditional moment restriction which exhausts all other implications of the MAR assumption. The paper also investigates the value of additional semiparametric restrictions on the conditional expectation function (CEF) of the original moment function given always observed covariates. In the program evaluation context, for example, such restrictions are implied by semiparametric models for the potential outcome CEFs given baseline covariates. The efficiency bound associated with this model is shown to also coincide with that of a particular moment condition problem. Some implications of these results for estimation are briefly discussed.