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

J
Java Code Geeks
T
Tailwind CSS Blog
酷 壳 – CoolShell
酷 壳 – CoolShell
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
L
LangChain Blog
博客园 - 【当耐特】
I
InfoQ
腾讯CDC
人人都是产品经理
人人都是产品经理
H
Help Net Security
Y
Y Combinator Blog
B
Blog
博客园 - Franky
Microsoft Security Blog
Microsoft Security Blog
Stack Overflow Blog
Stack Overflow Blog
The Cloudflare Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
WordPress大学
WordPress大学
H
Hackread – Cybersecurity News, Data Breaches, AI and More
博客园 - 叶小钗
D
Docker
博客园 - 聂微东
B
Blog RSS Feed
G
Google Developers Blog

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
On bivariate lower semilinear copulas and the star product
Lea Maislinger, Wolfgang Trutschnig · 2024-08-12 · via math.ST updates on arXiv.org

We revisit the family $\mathcal{C}^{LSL}$ of all bivariate lower semilinear (LSL) copulas first introduced by Durante et al. in 2008 and, using the characterization of LSL copulas in terms of diagonals with specific properties, derive several novel and partially unexpected results. In particular we prove that the star product (also known as Markov product) $S_{δ_1}*S_{δ_2}$ of two LSL copulas $S_{δ_1},S_{δ_2}$ is again a LSL copula, i.e., that the family $\mathcal{C}^{LSL}$ is closed with respect to the star product. Moreover, we show that translating the star product to the class of corresponding diagonals $\mathcal{D}^{LSL}$ allows to determine the limit of the sequence $S_δ, S_δ*S_δ, S_δ*S_δ*S_δ,\ldots$ for every diagonal $δ\in \mathcal{D}^{LSL}$. In fact, for every LSL copula $S_δ$ the sequence $(S_δ^{*n})_{n \in \mathbb{N}}$ converges to some LSL copula $S_{\overlineδ}$, the limit $S_{\overlineδ}$ is idempotent, and the class of all idempotent LSL copulas allows for a simple characterization. Complementing these results we then focus on concordance of LSL copulas. After deriving simple formulas for Kendall's $τ$ and Spearman's $ρ$ we study the exact region $Ω^{LSL}$ determined by these two concordance measures of all elements in $\mathcal{C}^{LSL}$, derive a sharp lower bound and finally show that $Ω^{LSL}$ is convex and compact.