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随机考虑集多项响应模型的可扩展估计AI 解读

A common assumption in the fitting of unordered multinomial response models for $J$ mutually exclusive categories is that the responses arise from the same set of $J$ categories across subjects. However, when responses measure a choice made by the subject, it is more appropriate to condition the distribution of multinomial responses on a subject-specific consideration set, drawn from the power set of $\{1,2,\ldots,J\}$. This leads to a mixture of multinomial response models…

估计警员武力升级倾向的条件有序刻板模型AI 解读

This article introduces a conditional ordinal stereotype model for estimating a police officer’s latent propensity to escalate to higher-severity force options during an encounter. Propensity to escalate is the likelihood of selecting a more serious force category than peer officers confronting the same circumstances, not necessarily implying that such force is excessive or contrary to policy. The associated conditional likelihood depends solely on data from the times and…

最大稳定分布的主成分分析AI 解读

Principal component analysis (PCA) is one of the most popular dimension reduction techniques in statistics and is especially powerful when a multivariate distribution is concentrated near a lower-dimensional subspace. Multivariate extreme value distributions have turned out to provide challenges for the application of PCA since their constraint support impedes the detection of lower-dimensional structures and heavy-tails can imply that second moments do not exist, thereby…

贝叶斯非参数准似然AI 解读

A recent trend in Bayesian research has been revisiting generalizations of the likelihood that enable Bayesian inference without requiring the specification of a data generating mechanism. This article focuses on a Bayesian nonparametric extension of Wedderburn’s quasi-likelihood, using Bayesian additive regression trees to model the mean function. Here, the analyst posits only a structural relationship between the mean and variance of the outcome. We show that this approach…

通过因果表征学习构建可解释的深度生成模型AI 解读

Recent developments in generative artificial intelligence (AI) rely on machine learning techniques such as deep learning and generative modeling to achieve state-of-the-art performance across wide-ranging domains. These methods’ surprising performance is due in part to their ability to learn implicit “representations” of complex, multi-modal data. Unfortunately, deep neural networks are notoriously black boxes that obscure these representations, making them difficult to…

可能性推断模型：综述AI 解读

An inferential model (IM) is a model describing the construction of provably reliable, data-driven uncertainty quantification and inference about relevant unknowns. IMs and Fisher’s fiducial argument have similar objectives, but a fundamental distinction between the two is that the former doesn’t require that uncertainty quantification be probabilistic, offering greater flexibility and allowing for a proof of its reliability. Important recent developments have been made…

从模型到含义：用 R 与 Python 解读统计模型AI 解读

Vincent Arel-Bundock. Boca Raton, FL: Chapman & Hall/CRC Press, 2025, ix + 252 pp., $84.99(P), ISBN: 978-1-032-90872-4.Interpreting statistical models and communicating their implications clearly r...

深度 Cox 模型的变量显著性检验AI 解读

Deep learning has become enormously popular in the analysis of complex data, including event time measurements with censoring. To date, deep survival methods have mainly focused on prediction. Such methods are scarcely used in matters of statistical inference such as hypothesis testing. Due to their black-box nature, deep-learned outcomes lack interpretability which limits their use for decision-making in biomedical applications. This article provides estimation and…

生存分析中的似然方法：附 R 示例AI 解读

Jun Ma, Annabel Webb, and Harold Malcolm Hudson. Boca Raton, FL: Chapman & Hall/CRC Press, 2024, xv + 384 pp., $130.00(H), ISBN: 978-0-815-36284-5.Likelihood Methods in Survival Analysis—With R Exa...

批量最优 Q 函数学习中的数据驱动知识迁移AI 解读

In data-driven decision-making across marketing, healthcare, and education, leveraging large datasets from existing ventures is crucial for navigating high-dimensional feature spaces and addressing data scarcity in new ventures. We investigate knowledge transfer in dynamic decision-making by focusing on batch stationary environments and formally defining task discrepancies through the framework of Markov decision processes (MDPs). We propose the Transfer Fitted Q-Iteration…

深度聚类评估：如何验证内部聚类评价指标AI 解读

Deep clustering partitions complex high-dimensional data using deep neural networks for clustering. It involves projecting data into lower-dimensional embeddings before partitioning, which embarks unique evaluation challenges. Traditional clustering validation measures, designed for low-dimensional spaces, are problematic for deep clustering for two reasons: (a) the curse of dimensionality when applied to the high-dimensional input data, and (b) unreliable comparison of…

通过测度分解实现稀疏线性回归的高效后验采样AI 解读

We consider the problem of sampling from the posterior distribution of a d-dimensional coefficient vector θ, given linear observations y=Xθ+ε. For sparse Bayesian models, such posteriors are in general multimodal, and therefore challenging to sample from. This observation has prompted the exploration of various heuristics that aim at approximating the posterior distribution. In this article, we study a different approach based on decomposing the posterior distribution into a…

利用对称性的自适应选择与错误发现率控制AI 解读

Controlling the false discovery rate (FDR) in high-dimensional multiple testing has recently been advanced through mirror statistics via knockoff and data splitting. However, these approaches primarily emphasize the symmetry structure of the one-dimensional mirror statistics while inadvertently overlooking the distribution information from non-null features when determining the rejection region, potentially causing a power loss. To tackle this challenge, we present a novel…

PALAR：用相对丰度预测变量估计绝对丰度回归效应AI 解读

High-dimensional compositional data are ubiquitous in omics research. Microbiome sequencing experiments measure the relative abundances (proportions) of microbial features, while the absolute abundances within the ecosystem remain unobserved. Most regression methods with microbial relative abundance predictors rely on log-ratio transformations and typically impose sparsity on the regression coefficients to manage high dimensionality. However, we show that the sparsity…

未测量混杂下的连续行动强化学习AI 解读

This article addresses the challenge of offline policy learning in continuous action spaces when unmeasured confounders are present. While most existing research focuses on policy evaluation within partially observable Markov decision processes (POMDPs) and assumes discrete action spaces, we advance this field by establishing a novel identification result to enable the nonparametric estimation of policy value for a given target policy under an infinite-horizon framework.…

深度离散编码器：具有离散潜在层的可识别深度生成模型AI 解读

In the era of generative AI, deep generative models (DGMs) with latent representations have gained tremendous popularity. Despite their impressive empirical performance, the statistical properties of these models remain underexplored. DGMs are often overparametrized, non-identifiable, and uninterpretable black boxes, raising serious concerns when deploying them in high-stakes applications. Motivated by this, we propose interpretable deep generative models for rich data types…

极端洪水保险损失的因子 Copula 潜在藤时间序列模型AI 解读

Statistical inference on the dependence of multivariate extremes poses notable challenges, particularly in contexts characterized by large dimensions and sparse extreme observations. While copula models provide flexible parametric methods for dependence modeling, caution is warranted when using them for extremal dependence inference or tail extrapolation. In this article, a novel class of factor-vine copula models is introduced. It is designed for modeling the dependence of…

《制药统计中的因果推断》书评AI 解读

Yixin Fang. Boca Raton, FL: Chapman & Hall/CRC Press, 2024, xiv + 231 pp., $130.00(H), ISBN: 978-1-032-56014-4.Causal Inference in Pharmaceutical Statistics is an excellent resource for the practic...

面向因果的稳健性：利用一般噪声干预AI 解读

Since distribution shifts are common in real-world applications, there is a pressing need to develop prediction models that are robust against such shifts. Existing frameworks, such as empirical risk minimization or distributionally robust optimization, either lack generalizability for unseen distributions or rely on postulated distance measures. Alternatively, causality offers a data-driven and structural perspective to robust predictions. However, the assumptions necessary…

线样带数据的多尺度空间变异：南极长须鲸案例AI 解读

Line transect sampling is a widely used survey method for estimating animal density or abundance. We present a novel model for such data that allows for spatial variation in animal density at two scales: a long scale, representing trends caused by for instance climatic or terrain gradients, and a short scale, representing abrupt shifts due to local effects such as prey patchiness. The long-range variation is modeled as a latent Gaussian random field, while the abrupt changes…

ERP 脑机接口迁移学习中的贝叶斯信号匹配AI 解读

An Event-Related Potential (ERP)-based Brain-Computer Interface (BCI) Speller System assists people with disabilities to communicate by decoding electroencephalogram (EEG) signals. A P300-ERP embedded in EEG signals arises in response to a rare, but relevant event (target) among a series of irrelevant events (non-target). Different machine learning methods have constructed binary classifiers to detect target events, known as calibration. The existing calibration strategy…

SMART-MC：基于协变量的多发性硬化治疗转换模型AI 解读

Treatment switching is a common occurrence in the management of Multiple Sclerosis (MS), where patients transition across various disease-modifying therapies (DMTs) due to heterogeneous treatment responses, differences in disease progression, patient characteristics, and therapy-associated adverse effects. To investigate how patient-level covariates influence the likelihood of treatment transitions among DMTs, we adopt a Markovian framework, Sparse Matrix Estimation with…

群体影像数据的检验与差异位置恢复AI 解读

In this article, we propose a multiscale adaptive test to detect differences between two samples of intrinsically smoothed image data in high-dimensional context. The test aggregates data from nearby locations using adaptive weights, significantly enhancing statistical power. We demonstrate that the test statistic converges to a Gumbel extreme value distribution under the null hypothesis. Moreover, we investigate its multiscale nature, showing that the chosen scales can grow…

随机对象的离散程度与曲率推断AI 解读

There are many open questions pertaining to the statistical analysis of random objects, which are increasingly encountered. A major challenge is the absence of linear operations in such spaces. A basic statistical task is to quantify statistical dispersion or spread. For two measures of dispersion for data objects in geodesic metric spaces, Fréchet variance and metric variance, we derive a central limit theorem (CLT) for their joint distribution. This analysis reveals that…