惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More
S
SegmentFault 最新的问题
N
Netflix TechBlog - Medium
Vercel News
Vercel News
F
Fortinet All Blogs
量子位
博客园 - Franky
酷 壳 – CoolShell
酷 壳 – CoolShell
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC
MongoDB | Blog
MongoDB | Blog
Y
Y Combinator Blog
GbyAI
GbyAI
博客园 - 三生石上(FineUI控件)
Apple Machine Learning Research
Apple Machine Learning Research
爱范儿
爱范儿
月光博客
月光博客
Recent Announcements
Recent Announcements
人人都是产品经理
人人都是产品经理
Hugging Face - Blog
Hugging Face - Blog
D
DataBreaches.Net
H
Help Net Security
阮一峰的网络日志
阮一峰的网络日志
D
Docker
WordPress大学
WordPress大学

math.ST updates on arXiv.org

What is Learnable in Valiant's Theory of the Learnable? Learning Perturbations to Extrapolate Your LLM Byzantine-Robust Distributed Sparse Learning Revisited The Sample Complexity of Multiple Change Point Identification under Bandit Feedback A proximal gradient algorithm for composite log-concave sampling Model-based Bootstrap of Controlled Markov Chains Approximation of Maximally Monotone Operators : A Graph Convergence Perspective Posterior Contraction Rates for Sparse Kolmogorov-Arnold Networks in Anisotropic Besov Spaces MIST: Reliable Streaming Decision Trees for Online Class-Incremental Learning via McDiarmid Bound A Spectral Framework for Closed-Form Relative Density Estimation Fast Rates for Offline Contextual Bandits with Forward-KL Regularization under Single-Policy Concentrability Higher-Order Equilibrium Tracking for EM-Compressible Online Estimation Scaling Limits of Long-Context Transformers A Note on Non-Negative $L_1$-Approximating Polynomials Susceptibilities and Patterning: A Primer on Linear Response in Bayesian Learning Linear Response Estimators for Singular Statistical Models Statistical inference with belief functions: A survey Robust stochastic first order methods in heavy-tailed noise via medoid mini-batch gradient sampling Every Feedforward Neural Network Definable in an o-Minimal Structure Has Finite Sample Complexity Adaptive auditing of AI systems with anytime-valid guarantees Locally Near Optimal Piecewise Linear Regression in High Dimensions via Difference of Max-Affine Functions Risk-Controlled Post-Processing of Decision Policies Covariate Balancing and Riesz Regression Should Be Guided by the Neyman Orthogonal Score in Debiased Machine Learning A Unified Pair-GRPO Family: From Implicit to Explicit Preference Constraints for Stable and General RL Alignment Time-Inhomogeneous Preconditioned Langevin Dynamics A Fine-Grained Understanding of Uniform Convergence for Halfspaces CITE: Anytime-Valid Statistical Inference in LLM Self-Consistency Ratio-based Loss Functions Optimal Confidence Band for Kernel Gradient Flow Estimator A renormalization-group inspired lattice-based framework for piecewise generalized linear models
Level sets and drift estimation for reflected Brownian mo...
Alejandro Cholaquidis, Ricardo Fraiman, Ernesto Mordecki, Cecili · 2016-11-29 · via math.ST updates on arXiv.org

We consider the estimation of the drift and the level sets of the stationary distri- bution of a Brownian motion with drift, reflected in the boundary of a compact set $S\subset R^d$ , departing from the observation of a trajectory of this process. We obtain the uniform consistency and rates of convergence for the proposed kernel based estimators. This problem has relevant applications in ecology, in estimating the home-range and the core-area of an animal based on tracking data. Recently, the problem of estimat- ing the domain of a reflected Brownian motion was considered in Cholaquidis, et al. (2016), when the stationary distribution is uniform and the estimation of the core-area, defined as a level set of the stationary distribution, is meaningless. As a by-product of our results, we obtain an estimation of the drift function. In order to prove our re- sults, some new theoretical properties of the reflected Brownian motion with drift are obtained, under fairly general assumptions. These properties allow us to perform the estimation for flexible regions close to reality. The theoretical findings are illustrated on simulated and real data examples.