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MOSAiC：多站点压缩风险函数的一次性聚合AI 解读

Multi-center research has strengthened the generalizability and reproducibility of scientific findings, yet multi-site inference in modern distributed research networks remains constrained by privacy regulations, heterogeneous site characteristics, and the logistical burden of human-in-the-loop, multi-round communication. In this work, we distill these operational constraints into four mathematical requirements for practical distributed inference and introduce MOSAiC, a…

异质站点下单轮通信的分布式半参数推断AI 解读

Distributed inference has proven useful in contexts where data privacy is the central concern. However, privacy constraints often necessitate cumbersome coordination to exchange summary data during distributed learning. Most existing methods require two rounds of data communication, yet excessive communication rounds might discourage participation from local sites. This study develops distributed algorithms that require only a single communication round while maintaining…

控制错误发现率的联邦特征选择AI 解读

Abstract Selecting a set of universally relevant features associated with a given response variable across multiple distributed data sites is an important problem in numerous scientific fields. However, performing this federated feature selection task becomes challenging when individual-level data cannot be shared due to privacy concerns. The problem is further complicated by potential heterogeneity in both feature distributions and model parameters across sites. In this…

先学习再决策：数据市场设计的学习方法AI 解读

As data marketplaces become increasingly central to the digital economy, it is crucial to design efficient pricing mechanisms that optimize revenue while ensuring fair and adaptive pricing. We introduce the Maximum Auction-to-Posted Price (MAPP) mechanism, a novel two-stage approach that first estimates the bidders’ value distribution through auctions and then determines the optimal posted price based on the learned distribution. We establish that MAPP is individually…

双层删失生存数据的半参数筛估计AI 解读

Summary Disease registry data provide important information on the progression of disease conditions. However, reports of death or drop-out of patients enrolled in the registry are always subject to a noticeable delay. Reporting delays, together with the administrative censoring that arises from a freeze date in data collection, lead to two layers of right censoring in the data. The first layer results from random drop-out and acts on the survival time. The second layer is…

考虑异质性且通信高效的分布式统计推断AI 解读

Summary In multicentre research, individual-level data are often protected against sharing across sites. To overcome the barrier of data sharing, many distributed algorithms, which only require sharing aggregated information, have been developed. The existing distributed algorithms usually assume the data are homogeneously distributed across sites. This assumption ignores the important fact that the data collected at different sites may come from various subpopulations and…

分组回归与稀疏广义加性模型的尖峰—厚尾组 LassoAI 解读

–We introduce the spike-and-slab group lasso (SSGL) for Bayesian estimation and variable selection in linear regression with grouped variables. We further extend the SSGL to sparse generalized additive models (GAMs), thereby introducing the first nonparametric variant of the spike-and-slab lasso methodology. Our model simultaneously performs group selection and estimation, while our fully Bayes treatment of the mixture proportion allows for model complexity control and…

复合似然推断的模型设定检验AI 解读

Summary Composite likelihood functions are often used for inference in applications where the data have a complex structure. While inference based on the composite likelihood can be more robust than inference based on the full likelihood, the inference is not valid if the associated conditional or marginal models are misspecified. In this paper, we propose a general class of specification tests for composite likelihood inference. The test statistics are motivated by the fact…

微观结构先于宏观？总量非流动性对股票收益与经济活动的预测力AI 解读

This paper constructs and analyzes various measures of trading costs in US equity markets covering the period 1926–2015. These measures contain statistically and economically significant predictive signals for stock market returns and real economic activity. We decompose illiquidity proxies into a component capturing aggregate volatility and a residual. The predictive content of these components differs in important ways. Specifically, we find strong evidence that the…

边界约束下的条件复合似然比检验AI 解读

Composite likelihood has been widely used in applications. The asymptotic distribution of the composite likelihood ratio statistic at the boundary of the parameter space is a complicated mixture of weighted |$\chi^2$| distributions. In this paper we propose a conditional test with data-dependent degrees of freedom. We consider a modification of the composite likelihood which satisfies the second-order Bartlett identity. We show that the modified composite likelihood ratio…

PLEMT：广义指数倾斜混合模型的拟似然 EM 同质性检验AI 解读

Motivated by analyses of DNA methylation data, we propose a semiparametric mixture model, namely the generalized exponential tilt mixture model, to account for heterogeneity between differentially methylated and non-differentially methylated subjects in the cancer group, and capture the differences in higher order moments (e.g. mean and variance) between subjects in cancer and normal groups. A pairwise pseudolikelihood is constructed to eliminate the unknown nuisance…

二元复发事件过程的依赖结构：推断与估计AI 解读

Bivariate or multivariate recurrent event processes are often encountered in longitudinal studies in which more than one type of event is of interest. There has been much research on regression analysis for such data, but little has been done to measure the dependence between recurrent event processes. We propose a time-dependent measure, termed the rate ratio, to assess the local dependence between two types of recurrent event processes. We model the rate ratio as a…