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删失事件时间数据中因果净获益的去偏学习AI 解读

Summary Cox regression is the default approach to evaluating the (relative) effect of two treatments on a survival endpoint. This standard framework has nonetheless been criticized for its canonical effect measure, the hazard ratio, having a subtle interpretation, thereby hindering policy-making. This in turn has prompted interest in other effects measures, such as the difference in restricted mean survival time, the net benefit and the win ratio, which have become…

有序结果中因果必要性概率的识别与界限AI 解读

Summary Although the existing causal inference literature focuses on the forward-looking perspective by estimating effects of causes, the backward-looking perspective can provide insights into causes of effects. In backward-looking causal inference, the probability of necessity measures the probability that a certain event is caused by the treatment, given the observed treatment and outcome. Most existing results focus on binary outcomes. Motivated by applications with…

图模型伪似然估计量的存在性与唯一性AI 解读

Summary Graphical and sparse (inverse) covariance models have found widespread use in modern sample-starved high-dimensional applications. A part of their wide appeal stems from the significantly low sample sizes required for existence of the estimators, especially in comparison with the classical full covariance model. For undirected Gaussian graphical models, the minimum sample size required for the existence of maximum likelihood estimators had been an open question for…

交叉随机效应 probit 模型的一致且可扩展复合似然估计AI 解读

Summary Estimation of crossed random effects models commonly incurs computational costs that grow faster than linearly in the sample size $ N $, often as fast as $ \Omega(N^{3/2}) $, making them unsuitable for large datasets. For non-Gaussian responses, integrating out the random effects to obtain a marginal likelihood poses significant challenges, especially for high-dimensional integrals for which the Laplace approximation may not be accurate. In this article we develop a…

二次谱估计量的偏误修正AI 解读

Summary The three cardinal, statistically consistent families of nonparametric estimators for the power spectral density of a time series are the lag-window, multitaper and Welch estimators. However, when estimating power spectral densities from a finite sample, each can be subject to nonignorable bias. Astfalck et al. (2024) developed a method that offers significant bias reduction for finite samples for Welch’s estimator, which this article extends to the larger family of…

期望的光滑函数之无偏蒙特卡洛估计：迈向自动化方法AI 解读

Abstract Given a smooth function $ f $, we develop a general approach to turn Monte Carlo samples with expectation $ m $ into an unbiased estimate of $ f(m) $. Specifically, we develop estimators that are based on randomly truncating the Taylor series expansion of $ f $ and estimating the coefficients of the truncated series. We derive their properties and propose a strategy to set their tuning parameters (which depend on $ m $) automatically, with a view to making the whole…

协变量自适应随机化临床试验中的一般协变量调整AI 解读

Abstract In randomized clinical trials, adjusting for baseline covariates can improve credibility and efficiency for demonstrating and quantifying treatment effects. This article studies the augmented inverse propensity weighted estimator, which is a general form of covariate adjustment that uses linear, generalized linear and nonparametric or machine learning models for the conditional mean of the response given covariates. Under covariate-adaptive randomization, we…

高维重复事件的动态因子分析AI 解读

Summary Recurrent event time data arise in many studies, including in biomedicine, public health, marketing and social media analysis. High-dimensional recurrent event data involving many event types and observations have become prevalent with advances in information technology. This article proposes a semiparametric dynamic factor model for the dimension reduction of high-dimensional recurrent event data. The proposed model imposes a low-dimensional structure on the mean…

提高平均处理效应跨总体迁移的估计效率AI 解读

Abstract We develop flexible, semiparametric estimators of the average treatment effect transported to a new target population, which offer potential efficiency gains. Transport may be of value when the average treatment effect may differ across populations. We consider the setting where differences in the average treatment effect are due to differences in the distribution of effect modifiers, baseline covariates that modify the treatment effect. First, we propose a…

广义线性网络特征模型中用于维度选择的尖峰与厚尾先验AI 解读

Summary Latent space models are often used to model network data by embedding a network’s nodes into a low-dimensional latent space; however, choosing the dimension of this space remains a challenge. To this end, we begin by formalizing a class of latent space models we call generalized linear network eigenmodels that can model various edge types (binary, ordinal, nonnegative continuous) found in scientific applications. This model class subsumes the traditional eigenmodel…

分段常数均值估计的迁移学习：最优性与ℓ₁、ℓ₀惩罚AI 解读

Summary We study transfer learning for estimating piecewise-constant signals when source data, which may be relevant but disparate, are available in addition to target data. We first investigate transfer learning estimators that respectively employ $ \ell_{1} $ and $ \ell_{0} $ penalties for unisource data scenarios and then generalize these estimators to accommodate multisources. To further reduce estimation errors, especially when some sources significantly differ from the…

面向大规模时空协变量的空间效应检测回归AI 解读

Abstract We develop a Spatial Effect Detection Regression (SEDR) model to capture the nonlinear and irregular effects of high-dimensional spatio-temporal predictors on a scalar outcome. Specifically, we assume that both the component and the coefficient functions in the SEDR are unknown smooth functions of location and time. This allows us to leverage spatially and temporally correlated information, transforming the curse of dimensionality into a blessing, as confirmed by…

函数型数据的 α 可分离性与幅度、相位的可调组合模型AI 解读

Abstract We consider separating and joint modelling amplitude and phase variations for functional data in an identifiable manner. To rigorously address this separability issue, we introduce the notion of α-separability upon constructing a family of α-indexed metrics. We bridge α-separability with the uniqueness of Fréchet mean, leading to the proposed adjustable combination of amplitude and phase model. The parameter α allows user-defined modelling emphasis between vertical…

删失数据下纵向替代指标的稳健评估AI 解读

Abstract 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…

利用随机微分方程动态建模稀疏纵向数据与函数片段AI 解读

Abstract Sparse functional/longitudinal data have attracted widespread interest due to the prevalence of such data in social and life sciences. A prominent scenario where such data are routinely encountered are accelerated longitudinal studies, where subjects are enrolled in the study at a random time and are only tracked for a short amount of time relative to the domain of interest. The statistical analysis of such functional snippets is challenging since information for…

超越总体变量重要性：个体变量重要性的概念、理论与应用AI 解读

Abstract In a non-parametric regression setting, we introduce a novel concept of ‘individual variable importance’, which assesses the relevance of certain covariates to an outcome variable among individuals with specific characteristics. This concept holds practical importance for both risk assessment and association identification. For example, it can represent (i) the usefulness of expensive biomarkers in risk prediction for individuals at a specified baseline risk, or…

随机游走 Metropolis 算法的可扩展耦合AI 解读

Abstract There has been a recent surge of interest in coupling methods for Markov chain Monte Carlo algorithms: they facilitate convergence quantification and unbiased estimation, while exploiting embarrassingly parallel computing capabilities. Motivated by these, we consider the design and analysis of couplings of the random walk Metropolis algorithm which scale well with the dimension of the target measure. Methodologically, we introduce a low-rank modification of the…

高维稳健角度迁移学习AI 解读

Abstract Transfer learning improves target model performance by leveraging data from related source populations, especially when target data are scarce. This study addresses the challenge of training high-dimensional regression models with limited target data in the presence of heterogeneous source populations. We focus on a practical setting where only parameter estimates of pretrained source models are available, rather than individual-level source data. For a single…

自适应函数型主成分分析AI 解读

Abstract Functional data analysis almost always involves smoothing discrete observations into curves, because they are never observed in continuous time and rarely without error. Although smoothing parameters affect the subsequent inference, data-driven methods for selecting these parameters are not well-developed, frustrated by the difficulty of using all the information shared by curves while being computationally efficient. On the one hand, smoothing individual curves in…

因果中介分析：选择与渐近有效推断AI 解读

Abstract Researchers are often interested in learning not only the effect of treatments on outcomes, but also the mechanisms that transmit these effects. A mediator is a variable that is affected by treatment and subsequently affects outcome. Existing methods for penalized mediation analyses may lead to ignoring important mediators and either assume that finite-dimensional linear models are sufficient to remove confounding bias, or perform no confounding control at all. In…

Engression：从分布回归视角研究外推AI 解读

Abstract 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…

含干预的半机制流行病模型：频率学派推断AI 解读

Abstract The effect of public health interventions on an epidemic are often estimated by adding the intervention to epidemic models. During the Covid-19 epidemic, numerous papers used such methods for making scenario predictions. The majority of these papers use Bayesian methods to estimate the parameters of the model. In this article, we show how to use frequentist methods for estimating these effects which avoids having to specify prior distributions. We also use…

多元极端事件的 X-vine 模型AI 解读

Abstract Regular vine sequences permit the organization of variables in a random vector along a sequence of trees. Vine-based dependence models have become greatly popular as a way to combine arbitrary bivariate copulas into higher-dimensional ones, offering flexibility, parsimony, and tractability. In this project, we use regular vine sequences to decompose and construct the exponent measure density of a multivariate extreme value distribution, or, equivalently, the tail…

没有免费午餐？经由专家中介销售企业的福利分析AI 解读

Abstract We study how firms target and influence expert intermediaries. In our context, pharmaceutical manufacturers provide payments to physicians during promotional interactions. We develop an identification strategy based on plausibly exogenous variation in payments driven by differential exposure to spillovers from AMC CoI policies. Using a case study of an important class of cardiovascular drugs, we estimate heterogeneous effects of payments on prescribing, with firms…