日期精度：到日 2575 篇 · 到月 3 篇 · 到年 0 篇
Garib Nath Singh 对《通过组合进行回归》的讨论意见
Garib Nath Singh; Professor Garib Nath Singh’s contribution to the Discussion of ‘Regression by composition’ by Farewell et al, Journal of the Royal Statis
Safaa K Kadhem 对《通过组合进行回归》的讨论意见
Safaa K Kadhem; Safaa K Kadhem's contribution to the Discussion of ‘Regression by composition’ by Farewell et al, Journal of the Royal Statistical Society
对Farewell等《构成回归》的致谢附议与讨论
Peter McCullagh; Seconder of the vote of thanks to Farewell et al. and contribution to the Discussion of Regression by composition', Journal of the Royal S
赵睿轩等对《组合回归》的讨论意见
Ruixuan Zhao, Oliver Dukes, Linbo Wang, Lin Liu; Ruixuan Zhao, Oliver Dukes, Linbo Wang, and Lin Liu’s contribution to the Discussion of ‘Regression by com
Gaffi、Legramanti 与 Rimella 对《通过组合进行回归》的讨论
Francesco Gaffi, Sirio Legramanti, Lorenzo Rimella; Gaffi, Legramanti, and Rimella’s contribution to the Discussion of ‘Regression by Composition’ by Farew
严景新、刘林等对《组合回归》的讨论
Jingxin Yan, Lin Liu, Oliver Dukes, Qizhai Li, Linbo Wang; Jingxin Yan, Lin Liu, Oliver Dukes, Qizhai Li, and Linbo Wang’s contribution to the Discussion o
Cheng等对Farewell等《组合回归》的讨论
Kaiming Cheng, Rong Jiang, Jiangfeng Wang, Keming Yu; Cheng et al's contribution to the Discussion of ‘Regression by composition’ by Farewell et al, Journa
田茂再、熊伟、马少培与梁金文对《组合回归》的讨论
Maozai Tian, Wei Xiong, Shaopei Ma, Jinwen Liang; Maozai Tian, Wei Xiong, Shaopei Ma, and Jinwen Liang's Contribution to the Discussion of ‘Regression by C
Archer Gong Zhang 对《组合回归》的讨论
Archer Gong Zhang; Archer Gong Zhang's contribution to the Discussion of ‘Regression by composition’ by Farewell et al, Journal of the Royal Statistical So
Andi Wang 对 Farewell 等人《通过组合进行回归》的讨论
Andi Q Wang; Andi Wang's contribution to the Discussion of ‘Regression by composition’ by Farewell et al, Journal of the Royal Statistical Society Series B
董梅、王林波、刘林与Oliver Dukes对“复合回归”的讨论意见
Mei Dong, Linbo Wang, Lin Liu, Oliver Dukes; Mei Dong, Linbo Wang, Lin Liu, and Oliver Dukes's contribution to the Discussion of ‘Regression by composition
Li、van Dyk与Autenreith对《组合回归》的讨论
Si-Yang Li, David A van Dyk, Maximilian Autenrieth; Si-Yang Li, David van Dyk and Maximilian Autenreith's contribution to the Discussion of ‘Regression by
自动调参的回火哈密顿蒙特卡洛：高维多峰分布采样
Abstract Hamiltonian Monte Carlo (HMC) is widely used for sampling from high-dimensional target distributions with densities known up to proportionality. While HMC exhibits favourable scaling properties in high dimensions, it struggles with strongly multimodal distributions. Tempering methods are commonly used to address multimodality, but they can be difficult to tune, especially in high-dimensional settings. In this study, we propose a method that combines tempering with…
Abstract Adaptive experiments use preliminary analyses of the data to inform further course of action and are commonly used in many disciplines including medical and social sciences. Because the null hypothesis and experimental design are data-dependent, it has long been recognized that statistical inference for adaptive experiments is not straightforward. Most existing methods only apply to specific adaptive designs and rely on strong assumptions. In this work, we propose…
一致有界邻域干扰下的因果推断
Abstract Randomized experiments remain the gold standard for estimating treatment effects; however, network interference compromises the validity of traditional estimators by violating the stable unit treatment value assumption and introducing bias. Although cluster-randomized designs help mitigate some bias, they struggle to accommodate complex network structures and cannot disentangle direct from indirect effects. To address these challenges, we develop a design-based…
KL-BSS：重新审视结构方程模型邻域选择的最优性
Abstract We introduce a new method for neighbourhood selection in linear structural equation models that improves over classical methods such as best subset selection (BSS) and the Lasso. Our method, called KL-BSS, takes advantage of the existence of underlying structure in SEM—even when this structure is unknown—and is easily implemented using existing solvers. Under weaker eigenvalue conditions compared to BSS and the Lasso, KL-BSS can provably recover the support of…
极值图模型中的参数与非参数对称性
Abstract Coloured graphical models provide a parsimonious approach to modeling high-dimensional data by exploiting symmetries in the model parameters. In this work, we introduce the notion of colouring for extremal graphical models on generalized multivariate Pareto distributions, a natural class of limiting distributions for threshold exceedances. Thanks to a stability property of the generalized multivariate Pareto distributions, coloured extremal tree models can be…
利用最优传输构建多属性数据的Copula图模型
Abstract Motivated by modern data types such as images and multi-view data, the multi-attribute graphical model aims to uncover conditional independence structures among vector-valued nodes. Under the Gaussian assumption, such independence is encoded in blockwise zeros of the precision matrix. To relax the restrictive Gaussian assumption, we propose a semiparametric multi-attribute graphical model leveraging a newly introduced cyclically monotone copula. This copula treats…
含局部测量误差的非平稳函数型时间序列结构变化推断
Abstract We study the problem of detecting and localizing change points for a general class of locally stationary functional time series. To accommodate the nonstationarity and other possible complex features, such as discontinuous trajectories, and heterogeneous partial measurement error of contemporary functional data, we propose methods that do not rest on the preprocessing techniques of presmoothing and dimension-reduction, which would be less accurate without the…
二元结局下的个体风险量化
Abstract Understanding treatment effect heterogeneity is crucial for reliable decision-making in treatment evaluation and selection. The conditional average treatment effect (CATE) is widely used to capture treatment effect heterogeneity induced by observed covariates and to design individualized treatment policies. However, it is an average metric within subpopulations, which prevents it from revealing individual risk, potentially leading to misleading results. This article…
基于对偶高斯随机化的交叉验证
Abstract We introduce a new cross-validation (CV) method based on an equicorrelated Gaussian randomization scheme. Our method is well-suited for problems where sample splitting is infeasible, either because the data violate the assumption of independent and identically distributed samples, or because there are insufficient samples to form representative train–test data pairs. In such problems, our method provides a simple, principled, and computationally efficient approach…
序贯模型置信集
Abstract 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…