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图结构分布值预测变量的惩罚Cox回归AI 解读

We propose the Graph-Fused Distributional Cox Model (GFDCM), a penalized survival analysis framework for distribution-valued predictors organized on a known graph. Modern neuroimaging studies collect high-dimensional voxel-level measurements across multiple regions of interest (ROIs) with rich spatial dependency structures. Rather than reducing these to scalar summaries, GFDCM models the full distribution of voxel intensities within each ROI, retaining region-specific…

自我对照病例系列的因果推断AI 解读

The self-controlled case series (SCCS) design is widely used in studies of rare events, for example of vaccine side effects. Conceptually, the design has two main features. First, it requires only data from individuals who have experienced the event of interest. Therefore, the design is especially useful when the outcome is rare and the available data come from surveillance or clinical records, say, of hospitalized patients. Second, because comparisons are made within single…

基于变分稳定 Doob 匹配的扩散模型推理时对齐AI 解读

Inference-time alignment for diffusion models aims to adapt a pre-trained reference diffusion model toward a target distribution without retraining the reference score network, thereby preserving the generative capacity of the reference model while enforcing desired properties at inference time. A central mechanism for achieving such alignment is guidance, which modifies the sampling dynamics through an additional drift term. In this work, we introduce variationally stable…

Specification LASSO 与灵活的特征类资产定价模型AI 解读

This paper studies a partially linear semiparametric additive model that allows for interactions among variables in high-dimensional settings. We propose the Specification LASSO (S-LASSO), a two-step method that integrates LASSO and Adaptive Group LASSO to simultaneously perform variable selection and model specification. Theoretically, we establish that S-LASSO possesses oracle properties in terms of selection consistency and asymptotic normality. Monte Carlo simulations…

无穷维球面核岭回归AI 解读

We introduce an intrinsic nonlinear regression framework for responses on a sphere of finite or infinite dimension, viewed as a Riemannian manifold. Unlike traditional tangent-space regressions, which lift responses to a common tangent space and thereby violate intrinsic spherical distances, our proposed method employs an intrinsic approach. We model the intrinsic conditional mean, fitting the responses with respect to the geodesic distance. We use a linear function space as…

有限超越依赖原理：和—最大值渐近独立性AI 解读

Asymptotic independence between sum-type and maximum-type statistics arises repeatedly across many areas of probability and statistics, particularly in high-dimensional testing and random matrix theory. Motivated by the shared structure underlying existing proofs, which is obscured by their model-specific formulations, this note isolates a common intermediate principle. We introduce a finite-exceedance dependence principle: if the finite-exceedance dependence error vanishes…

区间观测与缺失协变量下回归的锐利识别AI 解读

Researchers frequently estimate regressions when key variables are missing or observed only through intervals. Imputation or assumptions about the observation process can yield seemingly precise but misleading conclusions. We develop methods for identification and inference that remain robust to such incomplete data. For a broad class of conditional moment models, we characterize the sharp identified set, the set of all parameter values consistent with the observed data and…

静息态fMRI时间长记忆空间建模中的嵌套MCMC不确定性传播AI 解读

Resting-state functional magnetic resonance imaging (rs-fMRI) signals exhibit long-range temporal dependence, characterized by a slow power-law decay of autocorrelation. This scale-free temporal structure, quantified by the long-memory (LM) parameter, has been associated with brain function and age-related changes in neural dynamics. Investigating spatial variation in cortical LM and its association with demographic and clinical covariates requires methods that account for…

高维代理变量下的因果中介分析：潜因子模型下的识别、估计与推断AI 解读

Causal mediation analysis is an important tool for understanding how a treatment affects an outcome through intermediate variables referred to as mediators. However, in many applications, these mediators and pre-treatment covariates are not directly observed and are instead measured through noisy, high-dimensional surrogates. Mediation analysis when such latent variables are observed only through these surrogates remains less explored. In this paper, we propose…

因果发现需要明确的认知标准AI 解读

There are many causal discovery algorithms, but few guiding principles for choosing between them. Methods are usually compared through three lenses: identifiability, consistency, and benchmarks. Each introduces assumptions about the data-generating process that are often untestable. We argue that there are no shared standards for comparing, prioritizing, or even discussing those assumptions. We support this claim by showing that (i) identifiability assumptions are often…

当协变量掩盖预趋势AI 解读

Honest difference-in-differences methods judge how far parallel trends may fail after treatment by how far it failed before treatment, as shown by placebo estimates. When the design adjusts for covariates, the placebo estimates are adjusted too. We show that this benchmark is valid only if the covariates are measured before the earliest placebo window. A covariate measured later already reflects shocks to the outcome during the placebo period, so adjusting for it hides part…

选择性不完整观测下的锐利 Fréchet–Hoeffding 界AI 解读

Incomplete records can sharpen bounds on joint outcome probabilities even when no pair of outcomes is jointly observed. I derive sharp pointwise Fréchet--Hoeffding bounds combining known population marginals with selectively observed records, without restricting the observation mechanism. I establish an exact reduction of the identification problem to finite-dimensional optimization, yielding computable characterizations of both bounds. Illustrations involving income…

Simulated maximum likelihood is widely used in latent-variable models where likelihood evaluation requires intractable integrals or probabilities. With a fixed number of simulation draws, its estimating equation is generally biased. We develop Stochastic Approximation with Unbiased Simulated Scores (SAUSS), combining score simulation with stochastic gradient descent. The Hajivassiliou--McFadden identity, an instance of Fisher's identity, expresses the likelihood score as the…

单样本孟德尔随机化与工具变量分析：R 与 Stata 的 OneSampleMR 和 ivonesamplemr 包AI 解读

Mendelian randomization (MR) uses genetic variants as instrumental variables (IVs) to estimate the causal effect of an exposure on an outcome. When individual-level data on the instruments, exposure, and outcome are available in a single sample (one-sample MR), a range of IV estimators can be used beyond the standard two-stage least squares estimator, including structural mean models (SMMs) and two-stage predictor substitu- tion (TSPS) and two-stage residual inclusion (TSRI)…

In target trial emulation, the treatment strategies that patients follow are inferred from the treatments they actually receive in routine care. However, in most settings, the outcome may preclude the observation of planned treatment, giving rise to immortal time bias through misclassification of treatment strategy, while markers of treatment response may influence subsequent treatment decisions, giving rise to time-varying confounding. A key step toward unbiased treatment…

回归中的草图化：实践中真的可行吗？AI 解读

The Johnson-Lindenstrauss (JL) lemma and its extensions provide tools for solving the $L_2$ regression problem in high-dimensional settings with $n>d$, by transforming a subspace of $\mathbb{R}^{n}$ to a subspace of $\mathbb{R}^{m}$, (where $m<n$) while approximately preserving the norms of elements of the subspace. In this paper, we study the theoretical guarantees made by various JL-based subspace embeddings critically and concretely to gauge their practical applicability.…

目标试验模拟中的时间划分AI 解读

In target trial emulation, the treatment strategies that patients follow are inferred from the treatments they actually receive in routine care. However, in most settings, the outcome may preclude the observation of planned treatment, giving rise to immortal time bias through misclassification of treatment strategy, while markers of treatment response may influence subsequent treatment decisions, giving rise to time-varying confounding. A key step toward unbiased treatment…

评估时间序列已知转换点连续性的时间接缝分数AI 解读

Long environmental records increasingly combine observations from successive observing or processing systems. Transitions between these systems create temporal seams that may reflect imperfect harmonization or changes in the observed process. We introduce the Temporal Seam Score (S-score, or S), a signed, robust measure for assessing continuity at a known transition time. The complete time series is fitted with a seam-blind model accounting for trend, seasonality, and…

条件期望分位VaR：半参数估计与推断AI 解读

Conditional expectile-based value-at-risk (EVaR) has attracted increasing attention in financial risk management owing to its desirable theoretical properties. However, existing estimation methods for conditional EVaR either rely on potentially misspecified parametric models or suffer from the curse of dimensionality in nonparametric approaches. To this end, we study semiparametric estimation and inference for conditional EVaR in nonlinear time series models with unspecified…

金融中的数量方法AI 解读

These lecture notes provide a comprehensive introduction to Quantitative Methods in Finance (QMF), designed for graduate students in finance and economics with heterogeneous programming backgrounds. The material develops a unified toolkit combining probability theory, statistics, numerical methods, and empirical modeling, with a strong emphasis on implementation in Python. Core topics include random variables and distributions, moments and dependence, simulation and Monte…

预测回归中的稳健柯西方法AI 解读

This paper develops robust inference methods for predictive regressions that address key challenges posed by endogenously persistent or heavy-tailed regressors, as well as persistent volatility in errors. Building on the Cauchy estimation framework, we propose two novel tests: one based on $t$-statistic group inference and the other employing a hybrid approach that combines Cauchy and OLS estimation. These methods effectively mitigate size distortions that commonly arise in…

有界纵向结局的参照基准敏感性分析AI 解读

Reference-based sensitivity analysis is widely used to assess departures from the assumption that data are missing at random in longitudinal randomized trials, but existing methods are typically formulated for approximately Gaussian continuous outcomes and do not directly accommodate bounded outcomes, particularly when exact lower or upper bounds may occur with appreciable probability. We propose a Bayesian reference-based sensitivity analysis framework for bounded…

干扰下的选择性随机化推断AI 解读

Empirical researchers often use tests for interference as pretests or robustness checks. However, when the results guide the choice of models, parameters, or hypotheses, the resulting data-dependent selection can compromise the validity of subsequent inference. This paper develops a two-stage selective randomization inference framework for interference that explicitly conditions on the selection event to account for data-dependent selection. We first test the null hypothesis…

Softplus INGARCH 模型中的平稳单位根与推断AI 解读

A unit root makes linear count time series models such as the Poisson INGARCH(1,1) nonstationary. We show that a softplus link changes this. Weiß, Zhu and Hoshiyar (2022) introduced softplus INGARCH models and gave a sufficient condition for stationarity. For Poisson, negative binomial, gamma and related versions of these models, we obtain the necessary and sufficient condition, which allows a unit root. At a stationary unit root, the conditional mean has a power-law tail,…