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48 results for Wild bootstrap

A wild bootstrap method for nonparametric hypothesis tests based on kernel distribution embeddings is proposed. This bootstrap method is used to construct provably consistent tests that apply to random processes, for which the naive permutation-based bootstrap fails. It applies to a large group of kernel tests based on…

2014-08-23abs ↗pdf ↗

We apply a wild bootstrap method to the Lancaster three-variable interaction measure in order to detect factorisation of the joint distribution on three variables forming a stationary random process, for which the existing permutation bootstrap method fails. As in the i.i.d. case, the Lancaster test is found to outperf…

2016-03-02abs ↗pdf ↗

The paper introduces Shapley curves for measuring variable importance in nonparametric settings.

problem Limited statistical understanding of Shapley values as variable importance measures.
method Introduces Shapley curves based on conditional expectation and covariate distribution; derives convergence rates and normality; proposes a novel bootstrap procedure.
result Validates theoretical findings with numerical studies and analyzes vehicle prices determinants.

New method for inference on covariates in NMF with random effects.

problem Formal inference for covariate effects in NMF with non-negativity constraints.
method NMF-RE model with random effects, ridge updates, df-based cap, asymptotic linearization, wild bootstrap.
result Valid inference on covariates with non-negativity constraint, avoiding degeneracy.

Ridge regression is revisited with debiasing and thresholding, offering advantages over Lasso.

problem High-dimensional data challenges classical ridge regression's sparsity detection and bias issues.
method Debiasing and thresholding ridge regression, introducing a wild bootstrap for confidence regions and hypothesis testing, and a hybrid bootstrap for prediction intervals.
result Debiased and thresholded ridge regression can offer similar performance to thresholded Lasso and may be preferable in some settings.

Proposes a new data augmentation method for imbalanced datasets in both classification and regression.

problem Imbalanced datasets in supervised learning, especially in regression.
method GOLIATH algorithm based on kernel density estimates for classification and regression.
result Significant improvement over existing state-of-the-art techniques in imbalanced regression.

tsbootstrap handles time series uncertainty without assuming independence.

problem Time series data violate IID assumptions, leading to undercoverage in traditional methods.
method Provides various resampling and bootstrap methods, including classical and adaptive conformal calibration.
result Dependence-aware methods reduce coverage deficits, with sieve resampling performing best.

China's stock market is the largest emerging market all over the world. It is widely accepted that the Chinese stock market is far from efficiency and it possesses possible linear and nonlinear dependence. We study the predictability of returns in the Chinese stock market by employing the wild bootstrap automatic varia…

2016-11-13abs ↗pdf ↗

Efficient tests for various statistical problems using incomplete U-statistics.

problem Nonparametric tests for two-sample, independence, and goodness-of-fit problems.
method Proposes MMDAggInc, HSICAggInc, and KSDAggInc tests aggregating over multiple kernel bandwidths.
result Aggregated tests provide a solution to the kernel selection problem and achieve optimal rates.

We study (i) asymptotic behaviour of wild harmonic bundles, (ii) the relation between semisimple meromorphic flat connections and wild harmonic bundles, (iii) the relation between wild harmonic bundles and polarized wild pure twistor DD-modules. As an application, we show the hard Lefschetz theorem for algebraic semis…

2008-03-10abs ↗pdf ↗

Two-sample tests using MMD control type I error and achieve optimal power.

problem Developing reliable nonparametric two-sample tests for small sample sizes.
method Maximum Mean Discrepancy (MMD) for constructing novel nonparametric tests, proving non-asymptotic error control and optimality.
result MMDAgg test controls type I error and achieves minimax rate over Sobolev balls, outperforming other tests.

This paper constructs wild knots from beaded necklaces using a Schottky group.

problem Creating wild knots from beaded necklaces and studying their properties.
method Using a Schottky group generated by inversions on spheres to construct wild knots.
result The constructed wild knots are fibered if the original knot is fibered.

Study of wild mapping class groups on complex reflection groups.

problem Understanding deformations of wild Riemann surfaces.
method Construction of configuration spaces and combinatorial fission forests.
result Sharp parameterisation of admissible deformation classes of wild Riemann surfaces.

We propose a nonparametric statistical test for goodness-of-fit: given a set of samples, the test determines how likely it is that these were generated from a target density function. The measure of goodness-of-fit is a divergence constructed via Stein's method using functions from a Reproducing Kernel Hilbert Space. O…

2016-02-09abs ↗pdf ↗

Generative models predict page quality without training, useful for low-resource settings.

problem Detecting low-quality content in web articles.
method Human evaluation and analysis of 500 million web articles.
result Generative models can predict page quality without training, useful for low-resource settings.

Study of wild mapping class groups and their cabled braids.

problem Understanding the structure of wild mapping class groups and their cabled versions.
method Define and study generalizations of pure g\mathfrak{g}-braid groups, establish product decompositions, and introduce fission trees.
result Obtain cabled versions of braid groups, related to braid operads.

The Gauss-Bonnet formula for classical translation surfaces relates the cone angle of the singularities (geometry) to the genus of the surface (topology). When considering more general translation surfaces, we observe so-called wild singularities for which the notion of cone angle is not applicable any more. We study w…

2014-10-06abs ↗pdf ↗

LoD improves model safety by integrating unlabeled wild data, reducing OOD misclassification.

problem Improving model safety and reliability using unlabeled wild data containing both in-distribution and out-of-distribution samples.
method Intentionally label-noisifying unlabeled wild data to enable joint learning of labeled ID and OOD data, distinguishing losses between ID and OOD samples.
result LoD framework achieves superior OOD detection without requiring thresholds, improving model safety.

In this paper we study kleinian groups of Schottky type whose limit set is a wild knot in the sense of Artin and Fox. We show that, if the ``original knot'' fibers over the circle then the wild knot ΛΛ also fibers over the circle. As a consequence, the universal covering of S3Λ\mathbb{S}^{3}-Λ is R3\mathbb{R}^{3}. We p…

2005-09-06abs ↗pdf ↗

We prove the Kobayashi-Hitchin correspondence between good wild harmonic bundles and polystable good filtered λλ-flat bundles satisfying a vanishing condition. We also study the correspondence for good wild harmonic bundles with the homogeneity with respect to a group action, which is expected to provide another way t…

2019-02-21abs ↗pdf ↗

We introduce a general non-parametric independence test between right-censored survival times and covariates, which may be multivariate. Our test statistic has a dual interpretation, first in terms of the supremum of a potentially infinite collection of weight-indexed log-rank tests, with weight functions belonging to …

2019-12-08abs ↗pdf ↗

In this paper we prove that a wild knot KK which is the limit set of a Kleinian group acting conformally on the unit 3-sphere, with its standard metric, is homogeneous: given two points p,qKp, q\in{K} there exists a homeomorphism ff of the sphere such that f(K)=Kf(K)=K and f(p)=qf(p)=q. We also show that if the wild knot is a …

2005-08-26abs ↗pdf ↗

New method refines model-free evaluation of complex machine learning models.

problem Evaluating the excess risk of opaque machine learning predictors.
method Perturbing derivatives to create pseudo-outcomes and refitting the model twice.
result Upper bound on excess risk derived efficiently without prior function class knowledge.

Let CC and DD be a pair of crumpled nn-cubes and hh a homeomorphism of Bd C\text{Bd }C to Bd D\text{Bd }D for which there exists a map fh:CDf_h: C\to D such that fhBd C=hf_h|\text{Bd }C =h and fh1(Bd D)=Bd Cf_{h}^{-1}(\text{Bd }D)=\text{Bd }C. In our view the presence of such a triple (C,D,h)(C,D,h) suggests that CC is "at least as wild as" $D…

2014-11-10abs ↗pdf ↗

Optimizes a small set of centroid points to approximate bootstrap distribution.

problem Computational inefficiency of standard bootstrap methods in large-scale machine learning.
method Explicitly optimizes a small set of high quality centroid points to approximate the ideal bootstrap distribution.
result Accurately estimates uncertainty with a small number of bootstrap centroids, outperforming i.i.d. sampling.

Human affect recognition is an essential part of natural human-computer interaction. However, current methods are still in their infancy, especially for in-the-wild data. In this work, we introduce our submission to the Affective Behavior Analysis in-the-wild (ABAW) 2020 competition. We propose a two-stream aural-visua…

2020-02-09abs ↗pdf ↗

4-manifolds have special topological properties which can be used to get a different view on quantum mechanics. One important property (connected with exotic smoothness) is the natural appearance of 3-manifold wild embeddings (Alexanders horned sphere) which can be interpreted as quantum states. This relation can be co…

2018-11-11abs ↗pdf ↗