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半圆极限下的最优特征值收缩AI 解读

In response to the increasing dimensionality of modern datasets, recent theoretical studies of covariance estimation frequently adopt the proportional-growth asymptotic framework in which the sample size n and dimension p are comparable, with n,p→∞ and γn:=p/n→γ>0. However, in many datasets—perhaps most—the sample size and dimension are highly imbalanced. To address this, we consider the disproportional-growth asymptotic framework, where n,p→∞ and γn→0 or γn→∞. These regimes…

用高阶批判检验比较大型频数表中的稀疏微弱差异AI 解读

We adapt Higher Criticism (HC) to the comparison of two frequency tables which may—or may not—exhibit moderate differences between the tables in some unknown, relatively small subset out of a large number of categories. Our analysis of the power of the proposed HC test quantifies the rarity and size of assumed differences and applies moderate deviations-analysis to determine the asymptotic powerfulness/powerlessness of our proposed HC procedure. Our analysis considers the…

中值滤波是否真的比线性滤波更能保留边缘？AI 解读

Image processing researchers commonly assert that “median filtering is better than linear filtering for removing noise in the presence of edges.” Using a straightforward large-n decision-theory framework, this folk-theorem is seen to be false in general. We show that median filtering and linear filtering have similar asymptotic worst-case mean-squared error (MSE) when the signal-to-noise ratio (SNR) is of order 1, which corresponds to the case of constant per-pixel noise…

通过控制错误发现率适应未知稀疏性AI 解读

We attempt to recover an n-dimensional vector observed in white noise, where n is large and the vector is known to be sparse, but the degree of sparsity is unknown. We consider three different ways of defining sparsity of a vector: using the fraction of nonzero terms; imposing power-law decay bounds on the ordered entries; and controlling the ℓp norm for p small. We obtain a procedure which is asymptotically minimax for ℓr loss, simultaneously throughout a range of such…

均匀随机点背景中丝状结构的自适应多尺度检测AI 解读

We are given a set of n points that might be uniformly distributed in the unit square [0,1]2. We wish to test whether the set, although mostly consisting of uniformly scattered points, also contains a small fraction of points sampled from some (a priori unknown) curve with Cα-norm bounded by β. An asymptotic detection threshold exists in this problem; for a constant T−(α,β)>0, if the number of points sampled from the curve is smaller than T−(α,β)n1/(1+α), reliable detection…

病态逆问题中的边缘恢复：曲波框架的最优性AI 解读

We consider a model problem of recovering a function $f(x_1,x_2)$ from noisy Radon data. The function $f$ to be recovered is assumed smooth apart from a discontinuity along a $C^2$ curve, that is, an edge. We use the continuum white-noise model, with noise level $\varepsilon$. Traditional linear methods for solving such inverse problems behave poorly in the presence of edges. Qualitatively, the reconstructions are blurred near the edges; quantitatively, they give in our…

Wedgelet：边缘的近极小极大估计AI 解读

We study a simple “horizon model” for the problem of recovering an image from noisy data; in this model the image has an edge with $\alpha$-Hölder regularity. Adopting the viewpoint of computational harmonic analysis, we develop an overcomplete collection of atoms called wedgelets, dyadically organized indicator functions with a variety of locations, scales and orientations. The wedgelet representation provides nearly optimal representations of objects in the horizon model,…

通过小波收缩实现极小极大估计AI 解读

We attempt to recover an unknown function from noisy, sampled data. Using orthonormal bases of compactly supported wavelets, we develop a nonlinear method which works in the wavelet domain by simple nonlinear shrinkage of the empirical wavelet coefficients. The shrinkage can be tuned to be nearly minimax over any member of a wide range of Triebel- and Besov-type smoothness constraints and asymptotically mini-max over Besov bodies with $p \leq q$. Linear estimates cannot…

CART 与最佳正交基：一种联系AI 解读

We study what we call "dyadic CART" --a method of nonparametric regression which constructs a recursive partition by optimizing a complexity penalized sum of squares, where the optimization is over all recursive partitions arising from midpoint splits. We show that the method is adaptive to unknown degrees of anisotropic smoothness. Specifically, consider the anisotropic smoothness classes of Nikoľskii, consisting of bivariate functions $f(x_1, x_2)$ whose finite difference…

基于小波阈值化的密度估计AI 解读

Density estimation is a commonly used test case for nonparametric estimation methods. We explore the asymptotic properties of estimators based on thresholding of empirical wavelet coefficients. Minimax rates of convergence are studied over a large range of Besov function classes $B_{\sigma pq}$ and for a range of global $L'_p$ error measures, $1 \leq p' < \infty$. A single wavelet threshold estimator is asymptotically minimax within logarithmic terms simultaneously over a…

通过小波收缩适应未知光滑度AI 解读

We attempt to recover a function of unknown smoothness from noisy sampled data. We introduce a procedure, SureShrink, that suppresses noise by thresholding the empirical wavelet coefficients. The thresholding is adaptive: A threshold level is assigned to each dyadic resolution level by the principle of minimizing the Stein unbiased estimate of risk (Sure) for threshold estimates. The computational effort of the overall procedure is order N · log(N) as a function of the…

小波收缩：渐近理想之境？AI 解读

SUMMARY Much recent effort has sought asymptotically minimax methods for recovering infinite dimensional objects—curves, densities, spectral densities, images—from noisy data. A now rich and complex body of work develops nearly or exactly minimax estimators for an array of interesting problems. Unfortunately, the results have rarely moved into practice, for a variety of reasons—among them being similarity to known methods, computational intractability and lack of spatial…

统计估计与最优恢复AI 解读

New formulas are given for the minimax linear risk in estimating a linear functional of an unknown object from indirect data contaminated with random Gaussian noise. The formulas cover a variety of loss functions and do not require the symmetry of the convex a priori class. It is shown that affine minimax rules are within a few percent of minimax even among nonlinear rules, for a variety of loss functions. It is also shown that difficulty of estimation is measured by the…

SUMMARY Maximum entropy (ME) inversion is a non-linear inversion technique for inverse problems where the object to be recovered is known to be positive. It has been applied in areas ranging from radio astronomy to various forms of spectroscopy, sometimes with dramatic success. In some cases, ME has attained an order of magnitude finer resolution and/or an order of magnitude smaller noise level than that obtainable by standard linear methods. The dramatic successes all seem…

重整化指数与最优逐点收敛速率AI 解读

Simple renormalization arguments can often be used to calculate optimal rates of convergence for estimating linear functionals from indirect measurements contaminated with white noise. This allows one to quickly identify optimal rates for certain problems of density estimation, nonparametric regression, signal recovery and tomography. Optimal kernels may also be derived from renormalization; we give examples for deconvolution and tomography.

收敛速度的几何化，第三篇AI 解读

We establish upper and lower bounds on the asymptotic minimax risk in estimating (1) a density at a point when the density is known to be decreasing with a Lipschitz condition; (2) a density at a point when the density satisfies a local second-order smoothness (Sacks-Ylvisaker) condition; and (3) the $k$th derivative of the density at a point, when the density satisfies a local $L_p$ constraint on the $m$th derivative. In (1), (2) and (3) the upper and lower bounds differ…

超矩形上的极小极大风险及其含义AI 解读

Consider estimating the mean of a standard Gaussian shift when that mean is known to lie in an orthosymmetric quadratically convex set in $l_2$. Such sets include ellipsoids, hyperrectangles and $l_p$-bodies with $p > 2$. The minimax risk among linear estimates is within 25% of the minimax risk among all estimates. The minimax risk among truncated series estimates is within a factor 4.44 of the minimax risk. This implies that the difficulty of estimation--a statistical…

投影近似及其与核方法的对偶关系AI 解读

Projection pursuit regression and kernel regression are methods for estimating a smooth function of several variables from noisy data obtained at scattered sites. Methods based on local averaging can perform poorly in high dimensions (curse of dimensionality). Intuition and examples have suggested that projection based approaches can provide better fits. For what sorts of regression functions is this true? When and by how much do projection methods reduce the curse of…

密度泛函的单侧推断AI 解读

This paper discusses the possibility of truly nonparametric inference about functionals of an unknown density. Examples considered include: discrete functionals, such as the number of modes of a density and the number of terms in the true model; and continuous functionals, such as the optimal bandwidth for kernel density estimates or the widths of confidence intervals for adaptive location estimators. For such functionals it is not generally possible to make two-sided…

某些最小距离估计量的病态表现AI 解读

Minimum distance estimates are studied at the $N(\theta, 1)$ model. Estimates based on a non-Hilbertian distance $\mu (\mu = \text{Kolmogorov-Smirnov, Levy, Kuiper, variation and Prohorov})$ can exhibit very large variances, or even outright inconsistency, at distributions arbitrarily close to the model in terms of $\mu$-distance. For Hilbertian distances $(\mu = \text{Cramer-von Mises, Hellinger})$ this problem does not seem to occur. Geometric motivation for these results…

最小距离泛函的“自动”稳健性AI 解读

The minimum distance (MD) functional defined by a distance $\mu$ is automatically robust over contamination neighborhoods defined by $\mu$. In fact, when compared to other Fisher-consistent functionals, the MD functional was no worse than twice the minimum sensitivity to $\mu$-contamination, and at least half the best possible breakdown point. In invariant settings, the MD functional has the best attainable breakdown point against $\mu$-contamination among equivariant…