部分线性模型中估计的收敛速度

考虑回归模型（Ⅰ）：其中（ｘ＿ｉ，ｔ＿ｉ）是固定非随机设计点列，ｘ＿ｉ＝（ｘ＿（ｉｌ），…，ｘ＿（ｉｐ））＇β＝（β＿１，…，β＿ｐ）＇（ｐ＞１），ｇ是定义在［０，１］上的未知函数，β是未知待估参数，０＜ｔ＿ｉ＜１，ｅ＿ｉ是ｉ．ｉ．ｄ．随机误差，且Ｅｅ＿ｉ＝０，Ｅｅ＝σ￣２＜∞。基于ｇ的估计取一类非参数权估计（包括常见的核估计和近邻估计），我们讨论了β的最小二乘估计及ｇ的估计的最优强弱收敛速度。     

Estimation of the Density of Hypoelliptic Diffusion Processes with Application to an Extended Itô's Formula

We prove a uniform bound for the density, p _t (x), of the solution at time t(0, 1] of a 1-dimensional stochastic differential equation, under hypoellipticity conditions. A similar bound is obtained for an expression involving the distributional derivative (with respect to x) of p _t (x). These results are applied to extend the Itô formula to the composition of a function (satisfying slight regularity conditions) with a hypoelliptic diffusion process in the spirit of the work of Föllmer et al. ⁽⁵⁾     

一阶时滞微分方程解的零点分布

Abstract. The paper gives two estimates of the distance between adjacent zeros of solutions     

Adaptive anisotropic noise filtering for magnitude MR data

Conventional noise filtering schemes applied to magnitude magnetic resonance (MR) images tacitly assume Gauss distributed noise. Magnitude MR data, however, are Rice distributed. Not incorporating this knowledge leads inevitably to biased results, in particular when applying such filters in regions with low signal-to-noise ratio. In this work, we show how the Rice data probability distribution can be incorporated so as to construct a noise filter that is far less biased.     

Maximum Likelihood Estimation of Isotonic Normal Means with Unknown Variances

To analyze the isotonic regression problem for normal means, it is usual to assume that all variances are known or unknown but equal. This paper then studies this problem in the case that there are no conditions imposed on the variances. Suppose that we have data drawn fromkindependent normal populations with unknown meansμ_i's and unknown variancesσ²_i's, in which the means are restricted by a given partial ordering. This paper discusses some properties of the maximum likelihood estimates ofμ_i's andσ²_i's under the restriction and proposes an algorithm for obtaining the estimates.     

Optimal filter design subject to output sidelobe constraints: Computational algorithm and numerical results

An algorithm is presented for the design of optimal detection filters in radar and communications systems, subject to inequality constraints on the maximum output sidelobe levels. This problem was reduced in an earlier paper (Ref. 1) to an unconstrained one in the dual space of regular Borel measures, with a nondifferentiable cost functional. Here, the dual problem is solved via steepest descent, using the directional Gateaux differential. The algorithm is shown to be convergent, and numerical results are presented.This research was supported by the Australian Research Grants Committee.     

A class of loss functions of catenary form

The catenary form of loss function is considered in the framework of Bayesian decision theory. The mathematical tractability of this form seems to be unrecognized; it contains quadratic loss as a limiting case. For various probability distributions expressions are given for posterior analysis, and limiting properties are investigated.     

Nonparametric estimation of location parameter after a preliminary test on regression in the multivariate case

For a simple multivariate regression model, nonparametric estimation of the (vector of) intercept following a preliminary test on the regression vector is considered. Along with the asymptotic distribution of these estimators, their asymptotic bias and dispersion matrices are studied and allied efficiency results are presented.     

GRCA(1)模型中误差方差自加权估计的渐近分布

考虑随机系数自回归模型Y_t =Φ_ty_t-1+ u_t,其中 Φ_t为随机系数,u_t为随机误差。在允许Φ_t与u_t相依以及Eu_t无穷的条件下,构造了误差方差的自加权估计,并证明了该估计的渐近正态性。最后通过数值模拟,说明 自加权估计的稳健和有效性。     

On Hsu's theorem in multivariate regression

The paper deals with optimal quadratic unbiased estimation of the unknown dispersion matrix in multivariate regression models without assuming normality of the errors. We show that Hsu's theorem for univariate regression models continues to multivariate models with no additional assumptions. Furthermore optimal quadratic plus linear estimating functions for regression coefficients are considered, and we investigate whether the ordinary linear estimates are the best. This leads to a new theorem which is similar to that of Hsu.