����ʼ�Ȩ���������ģ������Ӧ������λ������ľ�����Ȼͳ���ƶ�

本文对两个样本数据不完全的线性模型展开讨论, 其中线性模型协变量的观测值不缺失, 响应变量的观测值随机缺失(MAR). 我们采用逆概率加权填补方法对响应变量的缺失值进行补足, 得到两个线性回归模型``完全'样本数据, 在``完全'样本数据的基础上构造了响应变量分位数差异的对数经验似然比统计量. 与以往研究结果不同的是本文在一定条件下证明了该统计量的极限分布为标准, 降低了由于权系数估计带来的误差, 进一步构造出了精度更高的分位数差异的经验似然置信区间.     

部分线性函数多项式模型的联合探测

本文提出了一个新的部分线性函数多项式回归模型,该模型中响应变量依赖于一个p阶函数多项式和一些非函数型数据的协变量.函数多项式模型、函数线性模型和部分函数线性模型是该模型的特殊情形.本文提出了一个模型探测方法,它能同时探测部分线性函数多项式回归模型中哪些阶是重要的以及哪些非函数型变量是重要的.提出的方法能相合地识别真实的模型并有好的预测表现.数值模拟能清晰地证实我们的理论结果.     

部分线性函数多项式模型的联合探测北大核心CSCD

本文提出了一个新的部分线性函数多项式回归模型,该模型中响应变量依赖于一个p阶函数多项式和一些非函数型数据的协变量．函数多项式模型、函数线性模型和部分函数线性模型是该模型的特殊情形．本文提出了一个模型探测方法,它能同时探测部分线性函数多项式回归模型中哪些阶是重要的以及哪些非函数型变量是重要的．提出的方法能相合地识别真实的模型并有好的预测表现．数值模拟能清晰地证实我们的理论结果．     

随机插补下两线性模型中响应变量分位数差异的经验似然置信区间

设两个样本数据不完全的线性模型,其中协变量的观测值不缺失,响应变量的观测值随机缺失。采用随机回归插补法对响应变量的缺失值进行补足,得到两个线性回归模型的"完全"样本数据,在一定条件下得到两响应变量分位数差异的对数经验似然比统计量的极限分布为加权x_1~2,并利用此结果构造分位数差异的经验似然置信区间。模拟结果表明在随机插补下得到的置信区间具有较高的覆盖精度。     

固定设计函数型非参数回归模型的估计

本文研究一类固定设计函数型非参数回归模型回归算予的估计问题,其中,解释变量X是取值于某函数空间的函数型变量,响应变量y为实值随机变量,在误差是一弱平稳线性过程及适当的条件下,获得未知回归函数算子估计量的相合性及其收敛速度和渐近正态性,推广了现有文献中的相关结果.     

协变量随机缺失时边际模型的广义矩估计

多元响应变量是纵向设计和横截面设计中经常遇到的一个数据类型.边际模型是探索该类数据解释变量对响应变量平均影响的一个常用工具.边际模型的一个重要特点在于,即使没有指明响应变量之间的相关结构,仍然能基于该模型构造回归参数的相合估计.本文讨论了协变量随机缺失时,边际模型回归参数的广义矩估计问题.使用逆概率加权和多个不同基底工作相关结构,我们得到了一组估计方程;本文通过极小化该估计方程组对应的二次推断函数构造目标参数的估计量.我们证明了估计量的渐近正态性,并通过随机模拟和初中数学成绩的实例分析考察了估计量的有限样本表现.     

回响环境下的最优波束形成器设计

目前大多数波束形成器(beamformer)的设计方法在模型建立时都是为了得到期望的波束形成响应(beamformer response),如最小方差无失真响应(minimum variance distortionless response,MVDR)方法和线性约束最小方差(linearly constrained minimum variance,LCMV)方法.为了构造人们期望的波束形成响应,最简单的方法就是采用从目标声源点到波束形成器输出点的脉冲响应(impulse response),加上一个波束形成器群延迟(group delay).然而对于波束形成器群延迟的估计却是未知的,经验地,人们将其选择为波束形成器长度的一半.可是在回响环境下,这样的波束形成响应选择往往不是最优的.为了设计最优波束形成器,本文考虑波束形成响应与波束形成器的联合设计问题.首先,引入一个新的变量来表示波束形成响应,并且将波束形成响应与波束形成器联合设计问题建模成一个结构型约束凸优化(structured constrained convex optimization,SCCO)问题.其次,利用SCCO问题的结构可分性,引入交替方向法(alternating direction method of multipliers,ADMM)来加以求解.为了文章的完整性,本文还给出ADMM算法收敛性结果的一个简单证明框架.最后,数值实验结果表明,提出的波束形成器设计方法在回响环境下是有效的,并且比LCMV方法效果更好.     

对部分线性模型用惩罚最小二乘估计时最优光滑化的注记

部分线性模型也就是响应变量关于一个或者多个协变量是线性的, 但对于其他的协变量是非线性的关系\bd 对于部分线性模型中的参数和非参数部分的估计方法, 惩罚最小二乘估计是重要的估计方法之一\bd 对于这种估计方法, 广义交叉验证法提供了一种确定光滑参数的方法\bd 但是, 在部分线性模型中, 用广义交叉验证法确定光滑参数的最优性还没有被证明\bd 本文证明了利用惩罚最小二乘估计对于部分线性模型估计时, 用广义交叉验证法选择光滑参数的最优性\bd 通过模拟验证了本文中所提出的用广义交叉验证法选择光滑参数具有很好的效果, 同时, 本文在模拟部分比较了广义交叉验证和最小二乘交叉验证的优劣.     

删失数据下的半参数变系数部分线性回归模型

为了分析删失数据,该文考虑变系数部分线性模型,此模型允许协变量对响应变量存在非线性影响.响应变量与协变量之间关系的统计模型通过线性结构来拟合是非常重要而且有益.对于删失数据,常用的统计方法不能直接应用于此模型.该文首先提出一类数据变换用以建立无偏条件期望.然后利用profile最小二乘方法,给出了模型中参数分量和非参数分量的profile最小二乘估计,并建立了这些估计的渐近正态性.最后通过数值例子来说明该文所提出的方法的有效性.     

广义线性模型极大似然估计的强相合性与渐近正态性

本文研究了若干重要类型的离散响应变量广义线性模型,在sum from i=1 to n ZiZi'的最小特征根大于cnα(对某个c>0,α>0)等条件下证明了回归参数向量的极大似然估计的强相合性与渐近正态性,其中设计阵序列{||Zn||}可以为无界序列.     

Optimization approach to the design of frequency sampling filters

Many digital signal processing applications require linear phase filtering. For applications that require narrow-band linear phase filtering, frequency sampling filters can implement linear phase filters more efficiently than the commonly used direct convolution filter. In this paper, a technique is developed for designing linear phase frequency sampling filters. A frequency sampling filter approximates a desired frequency response by interpolating a frequency response through a set of frequency samples taken from the desired frequency response. Although the frequency response of a frequency sampling filter passes through the frequency samples, the frequency response may not be well behaved between the specific samples. Linear programming is commonly used to control the interpolation errors between frequency samples. The design method developed in this paper controls the interpolation errors between frequency samples by minimizing the mean square error between the desired and actual frequency responses in the stopband and passband. This design method describes the frequency sampling filter design problem as a constrained optimization problem which is solved using the Lagrange multiplier optimization method. This results in a set of linear equations which when solved determine the filter's coefficients.This work was partially funded by The National Supercomputing Center for Energy and the Environment, University of Nevada Las Vegas, Las Vegas, Nevada and by NSF Grant MIP-9200581.     

$$ \mathcal{P} $$-optimal robust designs for multiresponse approximately linear regression

This paper deals with the problem of P-optimal robust designs for multiresponse approximately linear regression models. Each response is assumed to be only approximately linear in the regressors, and the bias function varies over a given L2--neighbourhood. A kind of bivariate models with two responses is taken as an example to illustrate how to get the expression of the design measure.     

Can a linear value function explain choices? An experimental study

We investigate in a simple bi-criteria experimental study, whether subjects are consistent with a linear value function while making binary choices. Many inconsistencies appeared in our experiment. However, the impact of inconsistencies on the linearity vs. non-linearity of the value function was minor. Moreover, a linear value function seems to predict choices for bi-criteria problems quite well. This ability to predict is independent of whether the value function is diagnosed linear or not. Inconsistencies in responses did not necessarily change the original diagnosis of the form of the value function. Our findings have implications for the design and development of decision support tools for Multiple Criteria Decision Making problems.     

Partial sum process to check regression models with multiple correlated response: With an application for testing a change-point in profile data

We consider regression models with multiple correlated responses for each design point. Under the null hypothesis, a linear regression is assumed. For the least-squares residuals of this linear regression, we establish the limit of the partial sums. This limit is a projection on a certain subspace of the reproducing Kernel Hilbert space of a multivariate Brownian motion. Based on this limit, we propose a significance test of Kolmogorov-Smirnov type to test the null hypothesis and show that this result can be used to study a change-point problem in the case of linear profile data (panel data). We compare our proposed method, which does not rely on any distributional assumptions, with the likelihood ratio test in a simulation study.     

Minimax and maximin efficient designs for estimating the location-shift parameter of parallel models with dual responses

Minimax designs and maximin efficient designs for estimating the location-shift parameter of a parallel linear model with correlated dual responses over a symmetric compact design region are derived. A comparison of the behavior of efficiencies between the minimax and maximin efficient designs relative to locally optimal designs is also provided. Both minimax or maximin efficient designs have advantage in terms of estimating efficiencies in different situations.     

Strange Laminates

Laminates made of composite anisotropic layers have a large varieties of different possible elastic responses. Still remaining in the classical field of linear elasticity, it is possible to obtain, with laminates, elastic behaviors that are very strange and completely unusual not only for classical materials, like metallic alloys, but also for the same laminates, when one bounds to consider their usual applications and stacking sequences. We show in this paper some strange combinations of plies giving rise to laminates possessing unusual elastic responses. The aim of the paper is threefold; first of all, to show that, contrary to what commonly thought, linear elasticity can still surprise, especially when anisotropy and heterogeneity enter the game. Second, that designers of composite structures can perhaps find a wealth of unexpected solutions that can be very interesting in some cases: it is sufficient to abandon the very restrictive rules commonly used in laminates design. Third, that an appropriate mathematical method, in this case, the polar formalism of Verchery, can open the way to new results, otherwise almost impossible to be found. Copyright © 2012 John Wiley & Sons, Ltd.     

Optimization of aircraft structural components by using nature-inspired algorithms and multi-fidelity approximations

In this work, a flat pressure bulkhead reinforced by an array of beams is designed using a suite of heuristic optimization methods (Ant Colony Optimization, Genetic Algorithms, Particle Swarm Optimization and LifeCycle Optimization), and the Nelder-Mead simplex direct search method. The compromise between numerical performance and computational cost is addressed, calling for inexpensive, yet accurate analysis procedures. At this point, variable fidelity is proposed as a tradeoff solution. The difference between the low-fidelity and high-fidelity models at several points is used to fit a surrogate that corrects the low-fidelity model at other points. This allows faster linear analyses during the optimization; whilst a reduced set of expensive non-linear analyses are run “off-line,” enhancing the linear results according to the physics of the structure. Numerical results report the success of the proposed methodology when applied to aircraft structural components. The main conclusions of the work are (i) the variable fidelity approach enabled the use of intensive computing heuristic optimization techniques; and (ii) this framework succeeded in exploring the design space, providing good initial designs for classical optimization techniques. The final design is obtained when validating the candidate solutions issued from both heuristic and classical optimization. Then, the best design can be chosen by direct comparison of the high-fidelity responses.     

通带可调相位 FIR 数字滤波器及其设计

常见的 FIR 数字滤波器大致可分为两类:一类是线性相位滤波器;另一类是极小相位滤波器,这两种类型的滤波器,其相位响应均不可调.第一种滤波器,其群延迟为((N-1)T)/2,其中 N 为滤波器长度,T 为采样周期.以 N=60,T=0.2秒的线性相位滤波器为例,它的相位响应为     

Community detection-based deep neural network architectures: A fully automated framework based on Likert-scale data

Deep neural networks (DNNs) have emerged as a state-of-the-art tool in very different research fields due to its adaptive power to the decision space since they do not presuppose any linear relationship between data. Some of the main disadvantages of these trending models are that the choice of the network underlying architecture profoundly influences the performance of the model and that the architecture design requires prior knowledge of the field of study. The use of questionnaires is hugely extended in social/behavioral sciences. The main contribution of this work is to automate the process of a DNN architecture design by using an agglomerative hierarchical algorithm that mimics the conceptual structure of such surveys. Although the train had regression purposes, it is easily convertible to deal with classification tasks. Our proposed methodology will be tested with a database containing socio-demographic data and the responses to five psychometric Likert scales related to the prediction of happiness. These scales have been already used to design a DNN architecture based on the subdimension of the scales. We show that our new network configurations outperform the previous existing DNN architectures.     

Generalization of simulation results practicality of statistical methods

The major purpose of this paper is to evaluate the practical use of statistical techniques in both the generalization or analysis of simulation results, and the design of simulation experiments. This problem is investigated with the help of a real-life system, namely the container terminus of ECT in Rotterdam. This system is modeled by a simulation program. The relationship between the simulation response and its input variables is modeled by a linear regression model: metamodel or auxiliary model. The paper summarizes regression analysis including generalized least squares which might be used for simulation responses with non-constant variances. The validity of the postulated regression metamodel is tested statistically: F- and t-statistics. The selection of the situations to be simulated, is done through experimental design methodology, permitting both quantitative and qualitative factors. The statistical techniques apply not only to simulation but also to real-life experiments.