带超级光滑噪声密度函数的小波自适应点态估计

利用小波方法在局部Holder空间中研究一类反卷积密度函数的点态估计问题.首先,针对超级光滑噪声给出该模型任一估计器的点态风险下界;其次,构造有限求和小波估计器,并证明其在超级光滑噪声条件下达到了最优收敛阶,即该估计器在点态风险下的收敛速度与下界一致.最后,还讨论了这类小波估计器的强收敛性.值得指出的是上述估计都是自适应的.     

非线性半参数统计模型估计函数的几何方法

将非线性半参数统计模型的概率密度函数族视为统计流形,利用微分几何方法,建立非线性半参数统计模型相对应的Hilbert空间,进而研究非线性半参数统计模型的估计函数问题.利用两类得分函数张成的子空间对Hilbert空间进行正交分解,进而讨论估计函数所在的集合,以及如何选取最优估计函数的问题.最后,通过实例分析来验证此方法的有效性.     

切波框架的存在充分条件及其稳定性

2011年,Kittipoom等人引入了一类新的切波生成函数空间,并指出此空间拥有许多优秀的性质,例如,该空间在平方可积函数空间中稠密,由该空间中元素生成的切波框架拥有强齐次逼近性质等.本文的主要目的是研究由Kittipoom等人引入的切波生成函数空间中的元素生成切波框架的充分条件及由该空间中的元素生成的切波框架的稳定性.具体而言,首先参考由Dahlke等人引入的切波群的定义将Kittipoom等人引入的切波群的定义进行适当调整,使得由Kittipoom等人引入的切波生成函数空间中每个元素都是可允许的;其次得到由该切波生成函数空间中任意一个元素和任意一个相对分离的稠密点列可形成一个切波框架;最后证明这些框架在时间、尺度和剪切参数或生成函数发生小扰动时仍然形成切波框架.这些结论使得切波框架在工程应用方面有着极大的灵活性和实用性.     

基于缺失数据的B样条单指标模型估计

本文主要研究基于响应变量随机缺失的单指标模型的逆概率加权估计问题.首先通过B样条逼近未知单指标函数,然后构建逆概率加权最小二乘损失函数,接着通过两阶段牛顿迭代算法获得指标函数和指标系数的估计,最后通过大量模拟例子和实例分析说明了我们所提估计方法的有效性和合理性.     

指数族刻度参数EB估计的渐近最优性

依据经验Bayes(EB)估计的思想方法,研究在LINEX损失函数下指数族刻度参数的EB估计问题.在这种损失函数下,求得参数的Bayes估计,利用密度函数的核估计方法,构造了总体X的密度函数估计,从而得到参数的EB估计,证明了这种EB估计是渐近最优的,并获得了它的收敛速度,最后将这种方法推广到多参数情形,并举例、模拟说明了它的应用.     

一类非线性波动方程整体解的衰减估计

主要研究一类带有色散项和耗散项的高维非线性波动方程的柯西问题及衰减行为,利用双值分解和Bessel函数的一些性质给出其相应线性方程解的衰减估计,通过压缩映射原理证明了小初值条件下整体解的存在性和衰减估计.     

失真风险保费的最优经验厘定

研究了贝叶斯模型中失真风险保费的经验厘定问题.通过引入分布函数的加权积分损失函数,利用信度理论的方法最小化期望损失得到分布函数的最优线性估计,进而得到失真风险保费的两个信度估计,并对信度估计的统计性质进行了比较.文章还讨论了失真函数和权重函数的选取问题,给出了结构参数的估计方法,证明了估计的无偏性和相合性.最后,利用数值模拟的方法验证了估计的收敛情况,并对不同失真函数下的收敛情况进行了比较.     

基于三维Copula函数的金融指数相关性分析

给出了三维Copula函数模型中未知参数的估计方法及最优三维Copula函数的选择方法,此构造方法对研究多变量之间的相依性提供了新途径.通过对上证指数、深圳成指及创业板指的历史数据进行实证分析,选出了最优三维Copula函数以描述三者之间的相关性,并分析三者之间的尾部相关性.     

Gegenbauer加法定理截断误差界的估计CSCD

1引言Bessel函数是在物理和工程中应用较为广泛的一类函数,德国天文学家F.W.Bessel早在18^(2)4年就第一次提出了该函数.Bessel函数是Bessel微分方程x^(2)d^(2)y/dx^(2)+xdy/dx+(x^(2)-m^(2))y=0,(1)的级数解,Bessel方程是在柱坐标或球坐标下使用分离变量法求解Laplace方程和Helmholtz方程时得到的.     

随机逆DEA模型的输出估计研究

逆DEA模型讨论了在保持决策单元的效率指数(即最优值)不变的情况下,当输入水平给定时估计输出值.在逆DEA模型的基础上研究了效率指数提高的输出估计,讨论了带有随机因素的情况,将该问题转化成机会约束的线性规划问题,并用数值算例加以说明.     

Inversion Formula for Shearlet Transform in Arbitrary Space Dimensions

In this article, we study the convergence of the inverse shearlet transform in arbitrary space dimensions. For every pair of admissible shearlets, we show that although the integral involved in the inversion formula from the continuous shearlet transform is convergent in the L² sense, it is not true in general whenever pointwise convergence is considered. We give some su?cient conditions for the pointwise convergence to hold. Moreover, for any pair of admissible shearlets we show that the Riemannian sums defined by the inverse shearlet transform are convergent to the original function as the sampling density tends to infinity.     

Optimal recovery of 3D X-ray tomographic data via shearlet decomposition

This paper introduces a new decomposition of the 3D X-ray transform based on the shearlet representation, a multiscale directional representation which is optimally efficient in handling 3D data containing edge singularities. Using this decomposition, we derive a highly effective reconstruction algorithm yielding a near-optimal rate of convergence in estimating piecewise smooth objects from 3D X-ray tomographic data which are corrupted by white Gaussian noise. This algorithm is achieved by applying a thresholding scheme on the 3D shearlet transform coefficients of the noisy data which, for a given noise level ε, can be tuned so that the estimator attains the essentially optimal mean square error rate O(log(ε ^???1)ε ^2/3), as ε→0. This is the first published result to achieve this type of error estimate, outperforming methods based on Wavelet-Vaguelettes decomposition and on SVD, which can only achieve MSE rates of O(ε ^1/2) and O(ε ^1/3), respectively.     

The root–unroot algorithm for density estimation as implemented via wavelet block thresholding

We propose and implement a density estimation procedure which begins by turning density estimation into a nonparametric regression problem. This regression problem is created by binning the original observations into many small size bins, and by then applying a suitable form of root transformation to the binned data counts. In principle many common nonparametric regression estimators could then be applied to the transformed data. We propose use of a wavelet block thresholding estimator in this paper. Finally, the estimated regression function is un-rooted by squaring and normalizing. The density estimation procedure achieves simultaneously three objectives: computational efficiency, adaptivity, and spatial adaptivity. A numerical example and a practical data example are discussed to illustrate and explain the use of this procedure. Theoretically it is shown that the estimator simultaneously attains the optimal rate of convergence over a wide range of the Besov classes. The estimator also automatically adapts to the local smoothness of the underlying function, and attains the local adaptive minimax rate for estimating functions at a point. There are three key steps in the technical argument: Poissonization, quantile coupling, and oracle risk bound for block thresholding in the non-Gaussian setting. Some of the technical results may be of independent interest.     

i.i.d情况下非参数回归模型的误差密度估计的收敛性质

本文研究了i．i．d情况下非参数回归的误差密度估计的一致收敛和均方收敛，给出了一定条件下误差密度的估计量f^n(x)的一致收敛速度和均方收敛速度。     

The nonexistence of shearlet scaling functions

Over the past five years, the directional representation system of shearlets has received much attention and has been shown to exhibit many advantageous properties. Over this time period, there have been a number of attempts to associate shearlet systems with a multiresolution analysis (MRA). However, one can argue that, in each of these attempts, the following statement regarding the resulting shearlet MRA notion is inaccurate: “There exist scaling functions satisfying various desirable properties, such as significant amounts of decay or regularity, nonnegativity, or advantageous refinement or representation conditions. Each such scaling function naturally induces an associated shearlet (either traditional or cone-adapted) that satisfies similar desirable properties. Each such scaling function/associated shearlet pair rationally induces a fast decomposition algorithm for discrete data.” In this article, we attempt to provide explanation for this situation by arguing the great difficulty of associating shearlet systems with such an MRA. We do so by considering two very natural and general notions of shearlet MRA—one which leads to traditional shearlets and one which leads to cone-adapted shearlets—each of which seems to be an excellent candidate to satisfy the above quoted statement. For each of these notions, we prove the nonexistence of associated scaling functions satisfying the above mentioned desirable properties.     

Dualizable Shearlet Frames and Sparse Approximation

Shearlet systems have been introduced as directional representation systems, which provide optimally sparse approximations of a certain model class of functions governed by anisotropic features while allowing faithful numerical realizations by a unified treatment of the continuum and digital realm. They are redundant systems, and their frame properties have been extensively studied. In contrast to certain band-limited shearlets, compactly supported shearlets provide high spatial localization but do not constitute Parseval frames. Thus reconstruction of a signal from shearlet coefficients requires knowledge of a dual frame. However, no closed and easily computable form of any dual frame is known. In this paper, we introduce the class of dualizable shearlet systems, which consist of compactly supported elements and can be proved to form frames for \(L^2({\mathbb {R}}^2)\). For each such dualizable shearlet system, we then provide an explicit construction of an associated dual frame, which can be stated in closed form and is efficiently computed. We also show that dualizable shearlet frames still provide near optimal sparse approximations of anisotropic features.     

Detection of Singularities by Discrete Multiscale Directional Representations

One of the most remarkable properties of the continuous curvelet and shearlet transforms is their sensitivity to the directional regularity of functions and distributions. As a consequence of this property, these transforms can be used to characterize the geometry of edge singularities of functions and distributions by their asymptotic decay at fine scales. This ability is a major extension of the conventional continuous wavelet transform which can only describe pointwise regularity properties. However, while in the case of wavelets it is relatively easy to relate the asymptotic properties of the continuous transform to properties of discrete wavelet coefficients, this problem is surprisingly challenging in the case of discrete curvelets and shearlets where one wants to handle also the geometry of the singularity. No result for the discrete case was known so far. In this paper, we derive non-asymptotic estimates showing that discrete shearlet coefficients can detect, in a precise sense, the location and orientation of curvilinear edges. We discuss connections and implications of this result to sparse approximations and other applications.     

Optimal variance estimation based on lagged second-order difference in nonparametric regression

Differenced estimators of variance bypass the estimation of regression function and thus are simple to calculate. However, there exist two problems: most differenced estimators do not achieve the asymptotic optimal rate for the mean square error; for finite samples the estimation bias is also important and not further considered. In this paper, we estimate the variance as the intercept in a linear regression with the lagged Gasser-type variance estimator as dependent variable. For the equidistant design, our estimator is not only \(n^{1/2}\)-consistent and asymptotically normal, but also achieves the optimal bound in terms of estimation variance with less asymptotic bias. Simulation studies show that our estimator has less mean square error than some existing differenced estimators, especially in the cases of immense oscillation of regression function and small-sized sample.     

A KERNEL ESTIMATOR OF A DENSITY FUNCTION IN MULTIVARIATE CASE FROM RANDOMLY CENSORED DATA

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Shearlet Coorbit Spaces: Compactly Supported Analyzing Shearlets, Traces and Embeddings

We show that compactly supported functions with sufficient smoothness and enough vanishing moments can serve as analyzing vectors for shearlet coorbit spaces. We use this approach to prove embedding theorems for subspaces of shearlet coorbit spaces resembling shearlets on the cone into Besov spaces. Furthermore, we show embedding relations of traces of these subspaces with respect to the real axes.