Zobrazeno 1 - 10
of 44 792
pro vyhledávání: '"Mathematics/Statistics"'
Autor:
Améndola, Carlos, Ferry, Kamillo
A polytrope is a tropical polyhedron that is also classically convex. We study the tropical combinatorial types of polytropes associated to weighted directed acyclic graphs (DAGs). This family of polytropes arises in algebraic statistics when describ
Externí odkaz:
http://arxiv.org/abs/2411.10394
Comparing the mean vectors across different groups is a cornerstone in the realm of multivariate statistics, with quadratic forms commonly serving as test statistics. However, when the overall hypothesis is rejected, identifying specific vector compo
Externí odkaz:
http://arxiv.org/abs/2411.10121
Autor:
Ballinari, Daniele, Wehrli, Alexander
We introduce a double/debiased machine learning (DML) estimator for the impulse response function (IRF) in settings where a time series of interest is subjected to multiple discrete treatments, assigned over time, which can have a causal effect on fu
Externí odkaz:
http://arxiv.org/abs/2411.10009
Autor:
Zhang, Zhi, Padilla, Carlos Misael Madrid, Luo, Xiaokai, Padilla, Oscar Hernan Madrid, Wang, Daren
In this paper, we focus on fully connected deep neural networks utilizing the Rectified Linear Unit (ReLU) activation function for nonparametric estimation. We derive non-asymptotic bounds that lead to convergence rates, addressing both temporal and
Externí odkaz:
http://arxiv.org/abs/2411.09961
Autor:
Mou, Wenlong, Qian, Jian
Bootstrapping and rollout are two fundamental principles for value function estimation in reinforcement learning (RL). We introduce a novel class of Bellman operators, called subgraph Bellman operators, that interpolate between bootstrapping and roll
Externí odkaz:
http://arxiv.org/abs/2411.09731
Importance sampling and independent Metropolis-Hastings (IMH) are among the fundamental building blocks of Monte Carlo methods. Both require a proposal distribution that globally approximates the target distribution. The Radon-Nikodym derivative of t
Externí odkaz:
http://arxiv.org/abs/2411.09514
Autor:
Wang, Hongjian, Ramdas, Aaditya
We present two sharp empirical Bernstein inequalities for symmetric random matrices with bounded eigenvalues. By sharp, we mean that both inequalities adapt to the unknown variance in a tight manner: the deviation captured by the first-order $1/\sqrt
Externí odkaz:
http://arxiv.org/abs/2411.09516
We develop novel tools for computing the likelihood correspondence of an arrangement of hypersurfaces in a projective space. This uses the module of logarithmic derivations. This object is well-studied in the linear case, when the hypersurfaces are h
Externí odkaz:
http://arxiv.org/abs/2411.09508
Autor:
Xu, Wenchao, Zhang, Xinyu
Asymptotic optimality is a key theoretical property in model averaging. Due to technical difficulties, existing studies rely on restricted weight sets or the assumption that there is no true model with fixed dimensions in the candidate set. The focus
Externí odkaz:
http://arxiv.org/abs/2411.09258
Higher-Order Influence Functions (HOIF), developed in a series of papers over the past twenty years, is a fundamental theoretical device for constructing rate-optimal causal-effect estimators from observational studies. However, the value of HOIF for
Externí odkaz:
http://arxiv.org/abs/2411.08491