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

GbyAI
GbyAI
人人都是产品经理
人人都是产品经理
Hugging Face - Blog
Hugging Face - Blog
罗磊的独立博客
博客园 - 【当耐特】
D
Docker
Y
Y Combinator Blog
L
LangChain Blog
博客园 - 三生石上(FineUI控件)
I
InfoQ
阮一峰的网络日志
阮一峰的网络日志
F
Fortinet All Blogs
J
Java Code Geeks
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
V
V2EX
B
Blog
The GitHub Blog
The GitHub Blog
腾讯CDC
MongoDB | Blog
MongoDB | Blog
博客园 - Franky
爱范儿
爱范儿
A
About on SuperTechFans
量子位
Cyber Security Advisories - MS-ISAC
Cyber Security Advisories - MS-ISAC

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
Asymptotic results on the moments of the ratio of the ran...
S. A. Ladoucette · 2005-05-12 · via math.ST updates on arXiv.org

Let \{X_1, X_2, ...\} be a sequence of positive independent and identically distributed random variables of Pareto-type with index α>0 and let \{N(t); t\geq 0\} be a mixed Poisson process independent of the X_i's. For t\geq 0, define T_{N(t)}:=\frac{X_1^2 + X_2^2 + ... + X_{N(t)}^2} {(X_1 + X_2 + ... + X_{N(t)})^2} if N(t)\geq 1 and T_{N(t)}:=0 otherwise. We derive the limiting behavior of the k-th moment of T_{N(t)}, k\in\mathbb{N}, by using the theory of functions of regular variation and an integral representation for \mathbb{E}\{T_{N(t)}^k\}. We also point out the connection between T_{N(t)} and the sample coefficient of variation which is a popular risk measure in practical applications.