Parameter Estimation for Multiscale Diffusions

We study the problem of parameter estimation for time-series possessing two, widely separated, characteristic time scales. The aim is to understand situations where it is desirable to fit a homogenized single-scale model to such multiscale data. We demonstrate, numerically and analytically, that if the data is sampled too finely then the parameter fit will fail, in that the correct parameters in the homogenized model are not identified. We also show, numerically and analytically, that if the data is subsampled at an appropriate rate then it is possible to estimate the coefficients of the homogenized model correctly. The ideas are studied in the context of thermally activated motion in a two-scale potential. However the ideas may be expected to transfer to other situations where it is desirable to fit an averaged or homogenized equation to multiscale data.     

Compressed sensing with local structure: Uniform recovery guarantees for the sparsity in levels class

In compressed sensing, it is often desirable to consider signals possessing additional structure beyond sparsity. One such structured signal model – which forms the focus of this paper – is the local sparsity in levels class. This class has recently found applications in problems such as compressive imaging, multi-sensor acquisition systems and sparse regularization in inverse problems. In this paper we present uniform recovery guarantees for this class when the measurement matrix corresponds to a subsampled isometry. We do this by establishing a variant of the standard restricted isometry property for sparse in levels vectors, known as the restricted isometry property in levels. Interestingly, besides the usual log factors, our uniform recovery guarantees are simpler and less stringent than existing nonuniform recovery guarantees. For the particular case of discrete Fourier sampling with Haar wavelet sparsity, a corollary of our main theorem yields a new recovery guarantee which improves over the current state-of-the-art.     

Blind image watermarking based on discrete fractional random transform and subsampling

This paper proposes a blind watermarking scheme based on discrete fractional random transform. The watermark information can be a binary sequence, a gray level image or a set of decimal fractions sampled from a given source signal. The host image is subsampled into four subimages, and the high correlations among their discrete fractional random transform coefficients are exploited for watermark embedding. Based on this self-reference strategy, the watermark can be extracted without the aid of the host image. As a fragile watermarking technique, our scheme can be used in tamper detection. Besides, it can be used in self-embedding for a large payload is provided. Meanwhile, security of the watermark is preserved due to the randomness of the discrete fractional random transform. Experimental results demonstrate the effectiveness of our scheme.     

非参数局部多项式回归估计的最优子抽样算法

随着科学技术的发展,虽然人们提高了收集和处理数据的能力,但仍存在一些大数据集超出了现有计算机的计算能力.目前,抽取一部分样本来替代全样本进行建模计算是减轻计算负担的一种方法.大数据背景下线性模型的子抽样方法已经得到了相对成熟的研究,在减轻计算量方面获得了很大的优势.文章将线性模型下的子抽样方法推广到非参数回归模型,并推...     

Subsampling Unit Root Tests for Heavy-Tailed Observations

It is shown that the subsampling methodology can be used to develop unit root tests when the noise sequence is heavy-tailed with infinite variance. Using least-squares residuals, we construct processes which approximately satisfy the null hypothesis and then, using subsampling, we approximate the null distribution of test statistics. We establish the asymptotic validity of this method and demonstrate its applicability in finite samples by means of a simulation study and a data example.     

Maximum likelihood estimation for multiscale Ornstein–Uhlenbeck processes

We study the problem of estimating the parameters of an Ornstein–Uhlenbeck (OU) process that is the coarse-grained limit of a multiscale system of OU processes, given data from the multiscale system. We consider both the averaging and homogenization cases and both drift and diffusion coefficients. By restricting ourselves to the OU system, we are able to substantially improve the results with strong modes of convergence, and provide some intuition of what to expect in the general case. In particular, in the homogenisation case we derive optimal rates of sub-sampling to minimize the estimation errors.     

基于岭回归模型大数据最优子抽样算法研究

随着大数据时代的来临,为了提高计算效率,Wang等(2018)提出基于logistic回归的最优子抽样算法,在保证参数估计精度的前提下,节省了大量的运算时间.为解决变量间的多重共线性,文章提出基于岭回归模型的最优子抽样算法,并证明岭回归模型中参数估计的一致性与渐近正态性.利用数值模拟与实证分析对最优子抽样算法进行评估,...     

改进的自回归过程结构变点检验

In this paper, we consider the problem of detecting for structural changes in the autoregressive processes including AR（p） process. In performing a test, we employ the conventional residual CUSUM of squares test （RCUSQ） statistic. The RCUSQ test is based on the subsampling method introduced by Jach and Kokoszka [J. Methodology and Computing in Applied Probability 25（2004）]. It is shown that under regularity conditions, the asymptotic distribution of the test statistic is the function of a standard Brownian bridge. Simulation results as to AR（1） process and an example of real data analysis are provided for illustration.     

微扫描减少光电成像系统频谱混淆的数学原理及实现

光电成像系统由于欠抽样引起谱混淆,推导分析了包括均匀微扫描在内的非均匀微扫描在减少混淆贩数学原理以及与亚像素位移之间的关系,同时用计算机模拟实现了微扫描的一维和二维过程。