Integrated Square Error Asymptotics for Supersmooth Deconvolution

Abstract.  We derive the asymptotic distribution of the integrated square error of a deconvolution kernel density estimator in supersmooth deconvolution problems. Surprisingly, in contrast to direct density estimation as well as ordinary smooth deconvolution density estimation, the asymptotic distribution is no longer a normal distribution but is given by a normalized chi-squared distribution with 2 d.f. A simulation study shows that the speed of convergence to the asymptotic law is reasonably fast.     

一种瞬变步长LMS自适应滤波算法

基于规一化起动、平滑递推和ＰＬＳ算法导出了一种新的变步长ＬＭＳ算法。文中分析了起动后的平稳连接和提高起始收敛速度的条件，证明了步长因子μ（ｋ）的瞬变特性及其对减小失调噪声的作用。计算机仿真效果较好，支持了理论分析。     

电磁逆散射问题中的泛函方法

用于电磁逆散射问题中关于未知介质分布的非线性积分方程经离散化后，其相应的矩阵方程通常是病态方程。对于病态的矩阵方程，其解或是不存在或是不唯一。这取决于该矩阵方程是超定的还是欠定的。对于这样的不适定问题，可用一些泛函方法使其转化为适定问题。文中介绍了用于电磁逆散射问题中的最小二乘方法、奇异值分解方法、正则化方法和最大熵方法，并对各种方法的优缺点给出评价。     

除环上一个矩阵秩的恒等式的修正

本文修正了除环上一个矩阵秩的恒等式，推广、改进了屠伯项教授的相应结果．     

单李代数不可约表示的标记

对于n秩单李代数g,当采用Dynkin关于素根的分类时,其不可约表示可以用n个非负整数Λ_(αi)标记,也可通过初等表示的权用数组li来标记。利用Cartan逆矩阵,我们给出了计算Λ_(α_i)与li之间关系的方法。对于B_n、C_n、D_n和F_4,利用Cartan逆矩阵证明了这些li是与采用Cartan关于素根的分类时的Λi是一致的。     

CONVERGENT SERIES EXPRESSIONS FOR INVERSE MOMENTS OF QUADRATIC FORMS IN NORMAL VARIABLES

Using relatively recent results from multivariate distribution theory, a direct approach to evaluating the inverse moments of a quadratic form in normal variables is proposed. Convergent infinite series expressions involving the invariant polynomials of matrix argument are obtained. The solution also depends upon a positive scalar which is arbitrarily chosen. For the solution to converge an upper bound upon this scalar is derived.     

Irregular Identification,Support Conditions,and Inverse Weight Estimation

In weighted moment condition models, we show a subtle link between identification and estimability that limits the practical usefulness of estimators based on these models. In particular, if it is necessary for (point) identification that the weights take arbitrarily large values, then the parameter of interest, though point identified, cannot be estimated at the regular (parametric) rate and is said to be irregularly identified. This rate depends on relative tail conditions and can be as slow in some examples as n^−1/4. This nonstandard rate of convergence can lead to numerical instability and/or large standard errors. We examine two weighted model examples: (i) the binary response model under mean restriction introduced by Lewbel (1997) and further generalized to cover endogeneity and selection, where the estimator in this class of models is weighted by the density of a special regressor, and (ii) the treatment effect model under exogenous selection (Rosenbaum and Rubin (1983)), where the resulting estimator of the average treatment effect is one that is weighted by a variant of the propensity score. Without strong relative support conditions, these models, similar to well known “identified at infinity” models, lead to estimators that converge at slower than parametric rate, since essentially, to ensure point identification, one requires some variables to take values on sets with arbitrarily small probabilities, or thin sets. For the two models above, we derive some rates of convergence and propose that one conducts inference using rate adaptive procedures that are analogous to Andrews and Schafgans (1998) for the sample selection model.     

Connected Substitutes and Invertibility of Demand

We consider the invertibility (injectivity) of a nonparametric nonseparable demand system. Invertibility of demand is important in several contexts, including identification of demand, estimation of demand, testing of revealed preference, and economic theory exploiting existence of an inverse demand function or (in an exchange economy) uniqueness of Walrasian equilibrium prices. We introduce the notion of “connected substitutes” and show that this structure is sufficient for invertibility. The connected substitutes conditions require weak substitution between all goods and sufficient strict substitution to necessitate treating them in a single demand system. The connected substitutes conditions have transparent economic interpretation, are easily checked, and are satisfied in many standard models. They need only hold under some transformation of demand and can accommodate many models in which goods are complements. They allow one to show invertibility without strict gross substitutes, functional form restrictions, smoothness assumptions, or strong domain restrictions. When the restriction to weak substitutes is maintained, our sufficient conditions are also “nearly necessary” for even local invertibility.     

Sliced Average Variance Estimation for Censored Data

We consider a nonlinear censored regression problem with a vector of predictors. With censoring, high-dimensional regression analysis becomes much more complicated. Since censoring can cause severe bias in estimation, modification to adjust such bias is needed to be made. Based on the weight adjustment, we develop the modification of sliced average variance estimation for estimating the lifetime central subspace without requiring a prespecified parametric model. Our proposed method preserves as much regression information as possible. Simulation results are reported and comparisons are made with the sliced inverse regression of Li et al. (1999 Li , K. C. , Wang , J. L. , Chen , C. H. ( 1999 ). Dimension reduction for censored regression data . Ann. Statist. 27 : 1 – 23 . [Google Scholar]).     

Inverse Regression in Binary Response LDV Model

Sliced inverse regression, a link-free and distribution-free method, is applied to binary response limited dependent variable models. An inverse regression property of binary response LDV model is found. Based on this property, if the distributions of X _j (j = 1, 2,…, p) satisfy the linearity condition, then β can be estimated up to a positive multiplicative scalar without any assumptions on the distribution of error ε. Moreover, the estimator can be proved to be asymptotically normal based on which testing hypotheses are considered. Simulations results are reported.