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Fisher 完备化与缠绕高斯族的统计正则化AI 解读

We determine the intrinsic Fisher completion of the full-covariance wrapped Gaussian family on a fixed torus and relate its geometry to uniform estimation. The completion is homeomorphic to the total-variation closure. Every added law is, in suitable integer torus coordinates, a product of a uniform law and a lower-dimensional wrapped Gaussian. All these densities are positive and real analytic, but in dimension at least two no added point has a compact neighborhood. Small…

Two-sample Mendelian randomization (MR) is a widely used methodology in epidemiology. In two-sample MR, summary data (typically regression coefficients and standard errors) quantifying the association between multiple genetic variants and the exposure and the outcome are used in an instrumental variable framework to estimate the causal effect of the exposure on the outcome. Most two-sample MR methods assume that the association between each genetic variant and the exposure,…

基于高频数据的常数波动率扩散过程模型聚类AI 解读

This paper considers model-based clustering of diffusion processes using high-frequency data. We formulate a finite mixture model for diffusion processes and develop a clustering method based on their constant diffusion coefficients. Using a quasi-likelihood, we propose estimators of the number of clusters, the diffusion coefficients and the cluster labels, and establish their consistency and the asymptotic normality of the diffusion coefficient estimators under…

异质处理效应估计的全局极小极大速率AI 解读

Recent work established minimax rates for pointwise estimation of the conditional average treatment effect (CATE), but the fundamental limits under global losses remained unresolved. We derive matching minimax lower and upper bounds under the $L_2$ and $L_\infty$ risks on certain covariate-density and support classes. For the lower bounds, we develop a many-fuzzy-hypotheses method that replaces each fixed alternative in a multiple-hypothesis construction with a mixture over…

Design-based simulations hold outcomes fixed and generate variation by resampling treatment assignment or shocks. They can be used in both methodological and applied work to assess inference procedures. However, their informativeness for these uses is not straightforward. We show that, when treatment effects are nonzero, these simulations can be misleading about spatial or serial correlation in clustered experiments, difference-in-differences, and shift-share designs. We…

基于成本的预测模型变量选择AI 解读

Objective: Variable selection for clinical prediction models is commonly done on statistical grounds related to model fit. Statistical approaches ignore the cost of misclassifications and the harm of measuring a variable. We aim to develop a decision-analytic variable selection algorithm based on net benefit. Materials and Methods: The proposed algorithm selects the set of predictors by exhaustively comparing the net benefit of all combinations using cross validation, with…

含未观测缺货替代的需求估计：精确似然最大化AI 解读

When a product is sold out, customers who prefer it may buy a substitute or leave without purchasing. Efficient demand estimation in this setting is challenging when inventory is observed only at replenishments, because the stockout times, and hence the products available to each customer, are unobserved. Estimating demand efficiently under stockout substitution has been a challenging problem since Anupindi et al. (1998), and it has become more pressing as the scale of such…

竞争风险下的惩罚性因特定原因风险回归：基于SANAD试验数据的弹性网方法AI 解读

Competing risks analysis is an important framework for studying time-to-event data in the presence of multiple, mutually exclusive events. Traditional methods, such as non-parametric estimators and proportional hazards models, may not adequately capture complex hazard patterns or provide stable estimates in practical settings. In this study, we propose a parametric approach based on the generalized exponential regression model for competing risks data. The model allows…

利用多个处理前期识别并分离直接效应与溢出效应AI 解读

Under interference, the treatment of some units affects the outcomes of others, so that the effect on a treated unit consists of a direct and a spillover component. A non-treated comparison group then generally identifies neither component, but the difference between the total effect on the treated and the spillover effect on the comparison units. We show that both components are identified separately when multiple pre-treatment periods are available, without an…

Population causal effects and their decomposition into direct and indirect effects cannot be estimated from case-control data without the outcome prevalence in the source population, which is rarely known. Because case-control sampling alters the population only through the outcome, the population is a mixture of the case and control distributions whose mixing weight is the prevalence. We show that this weight is identified by the population mean of any function of a single…

Large-scale longitudinal molecular profiling is now firmly established in biomedical research, prompted by the need to uncover coordinated biomarker trajectories reflecting the dynamics of underlying biological mechanisms and characterise patient heterogeneity in disease progression. While a range of statistical tools exist for either longitudinal modelling or high-dimensional analysis, there is no unified framework tailored to address these questions jointly. Motivated by a…

利用大语言模型知识改进分布外表格预测：一种交叉验证正则化方法AI 解读

Prediction models degrade when the deployment population differs from the one used to train them, and training data cannot say which feature-response relationships will transfer. Expert elicitation is slow and unscalable. We ask whether coefficient priors elicited from large language models (LLMs) improve out-of-distribution (OOD) prediction as the shrinkage target of a regularised regression instead of zero, across five tabular datasets split on real covariates, selecting…

含非平稳方差的半参数实时 GARCH 模型AI 解读

We propose a semiparametric real-time GARCH (SRT-GARCH) model that combines a smooth deterministic long-run volatility component with a stationary short-run process driven by contemporaneous return innovations. The model accommodates gradual changes in unconditional variance while allowing short-run volatility to respond directly to current information rather than relying solely on lagged observations. We derive the variance normalization, conditional moments, and…

高维成分微生物组数据含离群值的稳健对数对比回归AI 解读

It is well known that microbiome sequencing data are naturally compositional and often have a complex subcomposition structure, as well as a lot of noise and some outliers. These characteristics make classical log-contrast regression difficult to apply, because it is sensitive to extreme values and cannot fully meet the linear constraints of compositional predictors. To tackle these issues, in this paper, we propose a robust log-contrast regression framework that…

通过样本分割评估 A/B 测试方法：理论与实践AI 解读

We develop a theoretical framework for sample splitting in A/B testing environments, where data for each test are partitioned into two splits to measure methodological performance when the true impacts of tests are unobserved. We show that sample-split estimators are generally biased for full-sample performance but consistently estimate sample-split analogues of it. We derive their asymptotic distributions, construct valid confidence intervals, and characterize the…

时变分位数VAR模型：估计与推断AI 解读

We study time-varying quantile VAR models in which the conditional quantile does not propagate linearly through the recursion, and develop a toolkit for impulse response analysis, which to the best of our knowledge has not been established elsewhere. For example, we establish Bahadur representation for quantile VAR regression, infer model parameters via smoothed check function, and define and investigate the best linear projection of the time-varying tau-quantile impulse…

协变量特定胜率函数的灵活估计：优先时事件结局AI 解读

Prioritized time-to-event outcomes arise frequently in clinical studies, where treatment comparisons may be based on a prespecified hierarchy of clinically important outcomes. The win ratio provides a natural summary of treatment benefit for such prioritized outcomes. However, methods for characterizing heterogeneous treatment effects by estimating how the win ratio varies with baseline covariates remain limited in this setting. We develop a flexible local inverse…

PERMANOVA 的效应量估计与混杂感知置换方法AI 解读

Motivation: PERMANOVA is widely used to test associations between covariates and high-dimensional data with arbitrary distributions, such as ecology, metabolomics, or microbial community profiling. In these contexts, its effect size measure (R2) is generally used to indicate how much the high-dimensional measurement covaries with respect to a separate, univariate measure. However, as we show, this conventional effect size is inflated under the null, resulting in values that…

镜像似然：流形数据建模及其在相关时间序列中的应用AI 解读

Manifold-valued data arise in a wide range of scientific applications. Existing statistical methods typically follow either an intrinsic approach, which characterizes the manifold itself via quantities such as geodesic distance or the Fr\'echet mean, or an extrinsic approach, which embeds the manifold into an ambient Euclidean space, performs unconstrained statistical estimation, and projects the result back to the manifold. While the latter is conceptually and…

面板分位数回归的偏差校正与推断AI 解读

We develop a large-$n$, large-$T$ theory for the unsmoothed fixed-effects quantile regression estimator, paralleling Hahn and Newey (2004) for smooth nonlinear panel models. An exact rank-score identity for the profiled criterion, with a convexity argument, delivers the leading incidental parameter bias of the slope estimator, of order $T^{-1}$, in closed form. When $n$ and $T$ grow at the same rate, the estimator is asymptotically normal but in general not centred, and…

众人拾柴火焰高：实验储备库的长期效应推断AI 解读

The surrogate index method is the modern standard for estimation of long-term effects in a designed experiment using only short-term outcomes. This method relies on the surrogacy and comparability assumptions which may be violated in practice leading to estimation bias. We propose a new method called Experiment Reservoir Surrogate (ERS) which leverages a reservoir of historical long-term experiments that is common in large-scale experimentation platforms. ERS avoids the…

Distribution-matching posterior inference (DMPI) extends the minimal econometric interpretation to generalized posterior inference and model comparison for incomplete structural models. It combines a structural-parameter prior with exact Dirichlet-multinomial scores comparing empirical and model-implied distributions of selected population moments. Ensemble size controls structural discipline, and annealed sequential Monte Carlo computes posterior and generalized evidence.…

不完全与部分识别计量经济模型中的间接推断AI 解读

We extend the indirect inference principle to structural econometric models that are incomplete, in the sense that a single value of the structural parameter may be compatible with multiple distributions of the observed endogenous variables, so that the structural parameter is typically only partially identified. We define the binding function as the smallest norm of the auxiliary estimating equations over the set of data generating processes compatible with the structural…

中断时间序列中具有自回归误差的贝塔回归AI 解读

Interrupted time series analyses (ITSA) of proportion and rate outcomes are frequently estimated using ordinary least squares regression despite the bounded nature of these outcomes. When methods appropriate for bounded outcomes are used, the standard approach is a quasi-likelihood generalized linear model (GLM) with heteroskedasticity- and autocorrelation-consistent (HAC) standard errors. However, no existing estimator jointly models the beta-distributed conditional density…