KS,χ~2检验方法的K分布杂波参数估计研究

提出了一种基于矩估计的KS,χ2检验方法,用于对K分布杂波进行参数估计。该方法从拟合最优的角度进行参数估计,结合矩估计将二维优化转化为一维优化问题。仿真结果表明,该参数估计方法相比于二四阶矩估计方法有明显的改善,KS与分数阶矩法相比,仿真效果较好。χ2法在小v值时与分数阶矩法相当,当v增大时,性能下降。     

自组织理论对技术发展极限问题的启示

技术的发展从内史和外史的角度来说都会遇到发展极限的问题,自组织理论对技术发展极限问题的认识有重要的启示:技术系统的涨落对技术发展的极限实现维持与突破;技术系统的开放性对技术发展的极限实现促进与制约;技术系统的序参量对技术发展的极限实现役使和规导。正确认识技术发展的极限问题,对于促进技术的完善与革新,对于更好地规范技术的社会价值取向有重要的意义。     

Ultrastructural elliptical models

Dolby's (1976) ultrastructural model with no replications is investigated within the class of the elliptical distributions. General asymptotic results are given for the sample covariance matrix S in the presence of incidental parameters. These results are used to study the asymptotic behaviour of some estimators of the slope parameter, unifying and extending existing results in the literature. In particular, under some regularity conditions they are shown to be consistent and asymptotically normal. For the special case of the structural model, some asymptotic relative efficiencies are also reported which show that generalized least squares and the method of moment estimators can be highly inefficient under nonnormality.     

A robust approach to longitudinal data analysis

The author introduces robust techniques for estimation, inference and variable selection in the analysis of longitudinal data. She first addresses the problem of the robust estimation of the regression and nuisance parameters, for which she derives the asymptotic distribution. She uses weighted estimating equations to build robust quasi‐likelihood functions. These functions are then used to construct a class of test statistics for variable selection. She derives the limiting distribution of these tests and shows its robustness properties in terms of stability of the asymptotic level and power under contamination. An application to a real data set allows her to illustrate the benefits of a robust analysis.     

INFERENCE ABOUT THE MEAN OF A POISSON DISTRIBUTION IN THE PRESENCE OF A NUISANCE PARAMETER

In this note we examine the problem of estimating the mean of a Poisson distribution when a nuisance parameter is present. Using a condition of Cox (1958) about ancillarity in the presence of a nuisance parameter, we justify that inference about the parameter should be carried out using the conditional distribution given the appropriate ancillary statistics. A small simulation study has been done to compare the performance of the conditional likelihood approach and the standard likelihood approach.     

Estimation of parameters in survey sampling: Optimality

Some recent works on estimation in survey sampling are analyzed and extended from the perspective of the theory of optimal estimating functions.     

Inference about permutation parameter in large samples

The estimation problem of a permutation parameter on the basis of a random sample of increasing size is considered. A necessary and sufficient condition for the existence of an estimator, asymptotically fully efficient for two different distributions families, is derived. We also study the application of this result to cyclic groups of order two and three.     

Multiparameter estimation of discrete exponential distributions

Let X₁, …, X_p be independent random variables, all having the same distribution up to a possibly varying unspecified parameter, where each of the p distributions belongs to the family of one parameter discrete exponential distributions. The problem is to estimate the unknown parameters simultaneously. Hudson (1978) shows that the minimum variance unbiased estimator (MVUE) of the parameters is inadmissible under squared error loss, and estimators better than the MVUE are proposed. Essentially, these estimators shrink the MVUE towards the origin. In this paper, we indicate that estimators shifting the MVUE towards a point different from the origin or a point determined by the observations can be obtained.     

Some characterizations of distributions of the exponential-type

We consider a particular subclass of the two-parameter exponential family with natural parameters γ₁, γ₂ and characterize those distributions of the family having a ratio of the mean value and the variance that is a linear function of γ₁ by the form of the moment generating function. As special cases we find the normal and the gamma distributions.     

Invariance Theorems for Fisher Information

In multivariate and multi-parameter contexts, new expressions for Fisher Information are derived using the copula representation of the joint distribution of random variables. Invariance of Fisher Information to margins of the joint distribution is then demonstrated.