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作者：Cai, Yun; Gu, Hong; Kenney, Toby
作者单位：Dalhousie University
摘要：Deconvolution is the important problem of estimating the distribution of a quantity of interest from a sample with additive measurement error. Nearly all infinite-dimensional deconvolution methods in the literature use Fourier transformations. These methods are mathematically neat, but unstable, and produce bad estimates when signal-noise ratio or sample size are low. A popular alternative is to maximize penalized likelihood for a finite-dimensional basis expansion of the unknown density. We d...
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作者：Joseph, V. Roshan
作者单位：Georgia Institute of Technology; University System of Georgia; Georgia Institute of Technology
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作者：He, Yi; Einmahl, John H. J.
作者单位：Eastern Institute of Technology, Ningbo; Tilburg University
摘要：In the general setting of independent data with possibly very different distributions, extreme value estimators inevitably target the tail of the average distribution function. We consider all possible cases, that is, the extreme value index of the average distribution can be negative, zero, or positive, and we present novel asymptotic theory for the moment estimator. Our results require a different and much more challenging proof than those for the power-law case and are based on a uniform ce...
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作者：Yao, Yisha; Hu, Yue; Wang, Shiying; Dai, Wei; Liu, Zihuan; Zhang, Heping
作者单位：Columbia University; Yale University; Yale University
摘要：The organization of human brain subnetworks is fundamental to understanding cognition and neuropsychiatric health. Existing approaches predominantly construct subnetworks by clustering brain regions according to measured imaging phenotypes or functional correlation. Although successful, such phenotype-based parcellations reflect composite effects of genetics, environment, lifestyle, and measurement noise, thereby limiting biological interpretability and obscuring subnetworks attributable to sp...
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作者：Doss, Charles R.
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：We study nonparametric inference for the causal dose-response curve when the treatment variable is continuous rather than discrete. We develop doubly robust confidence intervals for the continuous treatment effect curve (at a fixed point) under the assumption that it is monotonic, based on inverting a likelihood ratio-type test. Monotonicity of the treatment effect curve is often a very natural assumption, and this assumption removes the need to choose a smoothing or tuning parameter for the n...
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作者：Koner, Salil
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作者：Ni, Yang
作者单位：University of Texas System; University of Texas Austin
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作者：Betancourt, Brenda
作者单位：George Mason University
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作者：Tian, Xinyu; Shen, Xiaotong
作者单位：University of Minnesota System; University of Minnesota Twin Cities
摘要：Reliable machine learning and statistical analysis rely on diverse, well-distributed training data. However, real-world datasets are often limited in size and exhibit underrepresentation across key subpopulations, leading to biased predictions and reduced performance, particularly in supervised tasks such as classification. To address these challenges, we propose Conditional Data Synthesis Augmentation (CoDSA), a novel framework that leverages generative models, such as diffusion models, to sy...
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作者：Shi, Jiaxin; Zhu, Xuening; Zhou, Jing; Yu, Baichen; Wang, Hansheng
作者单位：Peking University; Fudan University; Fudan University; Fudan University; Renmin University of China
摘要：We study one particular type of multivariate spatial autoregression (MSAR) model with diverging dimensions in both responses and covariates. This makes the usual MSAR models no longer applicable due to the high computational cost. To address this issue, we propose a factor-augmented spatial autoregression (FSAR) model. FSAR is a special case of MSAR but with a novel factor structure imposed on the high-dimensional random error vector. The latent factors of FSAR are assumed to be of a fixed dim...
