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作者：Borgonovo, Emanuele; Figalli, Alessio; Ghosal, Promit; Plischke, Elmar; Savare, Giuseppe
作者单位：Bocconi University; Bocconi University; Swiss Federal Institutes of Technology Domain; ETH Zurich; University of Chicago; Helmholtz Association; Helmholtz-Zentrum Dresden-Rossendorf (HZDR)
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作者：Kallus, Nathan; Mao, Xiaojie
作者单位：Cornell University; Tsinghua University
摘要：In many experimental and observational studies, the outcome of interest is often difficult or expensive to observe, reducing effective sample sizes for estimating average treatment effects (ATEs) even when identifiable. We study how incorporating data on units for which only surrogate outcomes not of primary interest are observed can increase the precision of ATE estimation. We refrain from imposing stringent surrogacy conditions, which permit surrogates as perfect replacements for the target ...
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作者：Song, Shanshan; Wang, Tong; Shen, Guohao; Lin, Yuanyuan; Huang, Jian
作者单位：Tongji University; Tongji University; Chinese University of Hong Kong; Hong Kong Polytechnic University; Hong Kong Polytechnic University; Hong Kong Polytechnic University
摘要：In this paper, we propose a new and unified approach for nonparametric regression and conditional distribution learning. Our approach simultaneously estimates a regression function and a conditional generator using a generative learning framework, where a conditional generator is a function that can generate samples from a conditional distribution. The main idea is to estimate a conditional generator satisfying the constraint that it produces a good regression function estimator. We use deep n...
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作者：Shen, Xinwei; Meinshausen, Nicolai
作者单位：Swiss Federal Institutes of Technology Domain; ETH Zurich
摘要：Distributional regression aims to estimate the full conditional distribution of a target variable, given covariates. Popular methods include linear and tree ensemble based quantile regression. We propose a neural network- based distributional regression methodology called 'engression'. An engression model is generative in the sense that we can sample from the fitted conditional distribution and is also suitable for high-dimensional outcomes. Furthermore, we find that modelling the conditional ...
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作者：Dong, Pinjun; Han, Ruijian; Jiang, Binyan; Xu, Yiming
作者单位：Zhejiang University; Hong Kong Polytechnic University; University of Kentucky
摘要：We introduce a general covariate-assisted statistical ranking model within the Plackett-Luce framework. Unlike previous studies that focus on individual effects with fixed covariates, our model allows covariates to vary across comparisons. This added flexibility enhances model fitting but also brings significant challenges in analysis. This article addresses these challenges in the context of maximum likelihood estimation (MLE). We first provide necessary and sufficient conditions for both mod...
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作者：Kiriliouk, Anna; Lee, Jeongjin; Segers, Johan
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作者：Agniel, Denis; Parast, Layla
作者单位：RAND Corporation; Rand Health; University of Texas System; University of Texas Austin
摘要：The development of statistical methods to evaluate surrogate markers is an active area of research. In many clinical settings, the surrogate marker is not simply a single measurement but is instead a longitudinal trajectory of measurements over time, e.g. fasting plasma glucose measured every 6 months for 3 years. In general, available methods developed for the single-surrogate setting cannot accommodate a longitudinal surrogate marker. Furthermore, many of the methods have not been developed ...
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作者：Lee, Jongmin; Jung, Sungkyu
作者单位：Pusan National University; Seoul National University (SNU); Seoul National University (SNU)
摘要：This article introduces Huber means on Riemannian manifolds, providing a robust alternative to the Fr & eacute;chet mean by integrating elements of both L2 and L1 loss functions. The Huber means are designed to be highly resistant to outliers while maintaining efficiency, making it a valuable generalization of Huber's M-estimator for manifold-valued data. We comprehensively investigate the statistical and computational aspects of Huber means, demonstrating their utility in manifold-valued data...
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作者：Beraha, Mario; Argiento, Raffaele; Camerlenghi, Federico; Guglielmi, Alessandra
作者单位：University of Milano-Bicocca; University of Bergamo; Polytechnic University of Milan
摘要：The study of almost surely discrete random probability measures is an active line of research in Bayesian non-parametrics. The idea of assuming interaction across the atoms of the random probability measure has recently spurred significant interest in the context of Bayesian mixture models. This allows the definition of priors that encourage well-separated and interpretable clusters. In this work, we provide a unified framework for the construction and the Bayesian analysis of random probabili...
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作者：Cabral, Rafael; Bolin, David; Rue, Havard
作者单位：King Abdullah University of Science & Technology
摘要：Model checking is essential to evaluate the adequacy of statistical models and the validity of inferences drawn from them. Particularly, hierarchical models such as latent Gaussian models (LGMs) pose unique challenges as it is difficult to check assumptions on the latent parameters. Diagnostic statistics are often used to quantify the degree to which a model fit deviates from the observed data. We construct diagnostic statistics by (a) defining an alternative model with relaxed assumptions and...
