2026

最新文章

• Inference with Mondrian random forests AI翻译
  作者：Cattaneo, Matias Damian; Klusowski, Jason Matthew; Underwood, William George
  作者单位：Princeton University; University of Cambridge
  摘要：Random forests are popular methods for regression and classification analysis, and many different variants have been proposed in recent years. One interesting example is the Mondrian random forest, in which the underlying constituent trees are constructed via a Mondrian process. We give precise bias and variance characterizations, along with a Berry-Esseen-type central limit theorem, for the Mondrian random forest regression estimator. By combining these results with a carefully crafted debias...
• Generalized universal inference on risk minimizers AI翻译
  作者：Dey, Neil; Martin, Ryan; Williams, Jonathan P.
  作者单位：North Carolina State University
  摘要：A common goal in statistics and machine learning is estimation of unknowns. Point estimates alone are of little value without an accompanying measure of uncertainty, but traditional uncertainty quantification methods, such as confidence sets and p-values, often require distributional or structural assumptions that may not be justified in modern applications. The present paper considers a very common case in machine learning, where the quantity of interest is the minimizer of a given risk (expe...
• Empirical likelihood for manifolds AI翻译
  作者：Kurisu, Daisuke; Otsu, Taisuke
  作者单位：University of Tokyo; University of London; London School Economics & Political Science
  摘要：There has been growing interest in statistical analysis of random objects taking values in a non-Euclidean metric space. One important class of such objects consists of data on manifolds. This article is concerned with inference on the Fr & eacute;chet mean and related population objects on manifolds. We develop the concept of nonparametric likelihood for data on manifolds and propose general inference methods by adapting the theory of empirical likelihood. In addition to the basic asymptotic ...
• The partial K function AI翻译
  作者：Grainger, Jake P.; Rajala, Tuomas A.; Murrell, David J.; Olhede, Sofia C.
  作者单位：Natural Resources Institute Finland (Luke); University of London; University College London
  摘要：The K function and its related statistics have been an enduring tool in the analysis of spatial point processes, providing an easy to compute and interpret summary statistic for characterising the interactions between points of one type, or between two different types of points. In this paper, we introduce a partial K function, enabling us to account for some of the effects of the other point types when analysing point-point interactions. The partial K function we introduce reduces to the usua...
• Exact Bayesian inference for Markov switching diffusions AI翻译
  作者：Stumpf-Fetizon, Timothee; Latuszynski, Krzysztof; Palczewski, Jan; Roberts, Gareth
  作者单位：Bocconi University; University of Warwick; Wroclaw University of Science & Technology
  摘要：We develop the first exact Bayesian methodology for the problem of inference in discretely observed regime switching diffusions. Switching diffusion models extend ordinary diffusions by allowing for jumps in instantaneous drift and volatility. The jumps are driven by a latent, continuous-time Markov switching process. We address the problem through an MCMC and an MCEM algorithm that target the exact posterior of diffusion parameters and the latent regime process. The algorithms are exact in th...
• Joshua Cape's contribution to the Discussion of 'Statistical exploration of the Manifold Hypothesis' by Whiteley et al. AI翻译
  作者：Cape, Joshua
  作者单位：University of Wisconsin Madison; University of Wisconsin System; University of Wisconsin Madison
• Robust detection of watermarks for large language models under human edits AI翻译
  作者：Li, Xiang; Ruan, Feng; Wang, Huiyuan; Long, Qi; Su, Weijie J.
  作者单位：University of Pennsylvania; Pennsylvania Medicine; Northwestern University; University of Pennsylvania
  摘要：Watermarking is an effective approach to distinguishing text generated by large language models (LLMs) from human-written text. However, the pervasive presence of human edits on LLM-generated text dilutes watermark signals, thereby significantly degrading detection performance of existing methods. In this paper, by modelling human edits through mixture model detection, we introduce a new method-a truncated goodness-of-fit test (Tr-GoF) for detecting watermarked text under human edits. We prove...
• Online multivariate changepoint detection: leveraging links with computational geometry AI翻译
  作者：Pishchagina, Liudmila; Romano, Gaetano; Fearnhead, Paul; Runge, Vincent; Rigaill, Guillem
  作者单位：Centre National de la Recherche Scientifique (CNRS); Universite Paris Saclay; Lancaster University; Universite Paris Saclay; Centre National de la Recherche Scientifique (CNRS); INRAE; Universite Paris Cite; INRAE; Universite Paris Saclay; AgroParisTech
  摘要：The increasing volume of data streams poses significant computational challenges for detecting changepoints online. Likelihood-based methods are effective, but a naive sequential implementation becomes impractical online due to high computational costs. We develop an online algorithm that exactly calculates the likelihood ratio test for a single changepoint in p-dimensional data streams by leveraging a fascinating connection with computational geometry. This connection straightforwardly allows...
• Sequential model confidence sets AI翻译
  作者：Arnold, Sebastian; Gavrilopoulos, Georgios; Schulz, Benedikt; Ziegel, Johanna
  作者单位：Centrum Wiskunde & Informatica (CWI); Swiss Federal Institutes of Technology Domain; ETH Zurich; Helmholtz Association; Karlsruhe Institute of Technology
  摘要：In most prediction and estimation situations, scientists consider various statistical models for the same problem, and naturally want to select amongst the best. Hansen et al. [(2011). The model confidence set. Econometrica: Journal of the Econometric Society, 79(2), 453-497] provide a powerful solution to this problem by the so-called model confidence set, a subset of the original set of available models that contains the best models with a given level of confidence. Importantly, model confid...
• Statistical inference for Gaussian Whittle-Matérn fields on metric graphs AI翻译
  作者：Bolin, David; Simas, Alexandre B.; Wallin, Jonas
  作者单位：King Abdullah University of Science & Technology; Lund University
  摘要：Whittle-Mat & eacute;rn fields are a recently introduced class of Gaussian processes on metric graphs, specified as solutions to a fractional-order stochastic differential equation. Unlike previous covariance-based methods, these fields are well-defined for any compact metric graph and can provide Gaussian processes with differentiable sample paths. We derive the main statistical properties, including the consistency and asymptotic normality of maximum likelihood estimators and the necessary a...