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资源丰富经济体的财政政策状态AI 解读

ABSTRACT We analyse fiscal policy in resource‐rich economies using a novel Bayesian regime‐switching panel model. The identified regimes capture pro‐ or countercyclical fiscal behaviour by allowing regime‐specific shifts in the average fiscal stance, while the switches between the regimes have the interpretation of changes in fiscal policy. Applying the model to a panel of sixteen oil‐producing economies, we show that fiscal policy has alternated between a procyclical and…

高维多视图数据协方差结构的推断AI 解读

Summary This article focuses on covariance estimation for multi-view data. Popular approaches rely on factor-analytic decompositions with shared and view-specific latent factors. Posterior computation is conducted via expensive and brittle Markov chain Monte Carlo sampling or variational approximations that underestimate uncertainty and lack theoretical guarantees. Our proposed methodology employs spectral decompositions to estimate and align latent factors that are active…

准贝叶斯经验贝叶斯：泊松复合决策问题的序贯方法AI 解读

SUMMARY The Poisson compound decision problem is a long-standing problem is statistics, for which empirical Bayes methods are commonly used to estimate Poisson means in static or batch settings. We consider this problem in a streaming, or online, framework. Building on a quasi-Bayesian approach based on Newton’s algorithm, we develop a sequential estimate that is easy to evaluate, computationally efficient and has constant per-observation cost as the data accrue. We…

高维极值图结构学习AI 解读

Extremal graphical models encode the conditional independence structure of multivariate extremes. Key statistics for learning extremal graphical structures are empirical extremal variograms, for which we prove non-asymptotic concentration bounds that hold under general domain of attraction conditions. For the popular class of Hüsler--Reiss models, we propose a majority voting algorithm for learning the underlying graph from data through $L^1$ regularized optimization. Our…

通过类别分布近似实现个体疫情模型的可扩展校准AI 解读

Traditional compartmental models capture population-level dynamics but fail to characterize individual-level risk. The computational cost of exact likelihood evaluation for partially observed individual-based models, however, grows exponentially with the population size, necessitating approximate inference. Existing sampling-based methods usually require multiple simulations of the individuals in the population and rely on bespoke proposal distributions or summary…

López de Prado 与 Porcu 对《组合回归》的讨论意见AI 解读

Marcos López de Prado, Emilio Porcu; López de Prado and Porcu’s contribution to the Discussion of ‘Regression by compositio’ by Farewell et al, Journal of

Garib Nath Singh 对《通过组合进行回归》的讨论意见AI 解读

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AI 解读

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AI 解读

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AI 解读

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AI 解读

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AI 解读

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AI 解读

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AI 解读

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AI 解读

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AI 解读

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AI 解读

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

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高维均值向量的分布无关收缩估计AI 解读

We introduce shrinkage estimators of the sample mean vector in high dimension. Our estimators share desirable properties relative to existing methods: they are distribution-free, consider bona-fide target estimators, and use simple estimators of the shrinkage intensities independent of the precision matrix which are L2 consistent in high dimension. Unlike existing estimators that impose that the whitened data be an i.i.d. matrix, we only impose i.i.d. across sample…

局部稳健且半参数有效的贝叶斯推断AI 解读

We propose a framework for making Bayesian parametric models robust to local misspecification. Suppose in a baseline parametric model, a parameter of interest has an interpretation in an encompassing semiparametric model. Bayesian and maximum likelihood estimators are generally biased under local misspecification. We propose to augment the baseline likelihood by a multiplicative factor that involves scores for the baseline model, the efficient scores for the encompassing…