On the efficiency of using the sample kurtosis in selecting optimal lpestimators

This paper examines the efficiency of thesample kurtosisin obtaining L_P estimates as an estimates of central tendency for symmetric distributions. Moreover, guidelines are established for determining an optimal value of P based on the kurtosis of the error distribution.     

Consistency of the normal equations

This note provides an alternative proof of consistency of the normal equations by appealing to well-known results in linear programming.     

Using an approximate L1 Estimator

This note investigates the efficiency of using near-best or approximate L₁ estimators as starting values in L₁ linear programming procedures. In particular, it is shown that the total computer time can often be reduced if one first computes the least squares estimator, β, and then adjust y to y - Xβ in Barrodale and Roberts’ improved algorithm.     

Useful generalized properties of L1 estimators

Recent results by G. Appa and C. Smith, as well as I. Barrodale and F. D. K. Roberts, underscore several properties exhibited for fitting a linear model to a set of observation points under the criterion of least sum of absolute deviations(commonly denoted as the L₁ criterion). This paper will generalize these properties to the non-full rank case and relax in a natural way some assumptions given by Appa and Smith.     

On the asymptotic bias of grenander’s mode estimator

Grenander introduced a direct estimator of the mode for a large class of densities. This note considers a large subclass of these densities for which Grenander’s estimator is asymptotically biased. Some of the distributions from this subclass include the F, gamma, and beta for which asymptotic expressions for the bias are given. To reduce the bias, it is recommended to choose larger values for one of the parameters of the estimator when the underlying distribution is nonsymmetric.     

On least absolute values estimation

The resistance of least absolute values (L₁) estimators to outliers and their robustness to heavy-tailed distributions make these estimators useful alternatives to the usual least squares estimators. The recent development of efficient algorithms for L₁ estimation in linear models has permitted their use in practical data analysis. Although in general the L₁ estimators are not unique, there are a number of properties they all share. The set of all L₁ estimators for a given model and data set can be characterized as the convex hull of some extreme estimators. Properties of the extreme estimators and of the L₁-estimate set are considered.     

真空纳米微电子结构自发射阴极的电磁场研究

介绍了一种改进的“透镜”算法。用该算法和相应的实验研究了两种自发射阳极的静电场特性（尖锥直径为１０~１ｎｍ）,其电场放大系数达到１０~２。并研究了在阴极尖锥附近的电场强度与尖锥结构的关系：分析了表面粗糙程度的影响。最后所得的计算机算法和程序适用于研究任意形状的纳米尖锥阴极。     

M31. Least squares solutions over interval restrictions

In this article, the three-parameter I.G. distribution is standardized with zero mean and unit variance. The third standard moment α₃ is employed as the shape parameter. Tables of the cumulative probability function are given as a function of the standardized variate z, and of the shape parameter, α₃. Various comparisons are made with the lognormal, Weibull, and gamma distributions.