论文检索

后分层重要性抽样AI 解读

Importance sampling is a building block of modern statistical computing, but the weighted estimate is prone to high or infinite variance. Moreover, its empirical standard error is often unreliable, leaving importance sampling without practical uncertainty quantification. Motivated by multilevel regression and poststratification (MRP) in survey sampling, we propose post-stratified importance sampling (PIS) for estimating target expectations from proposal draws. PIS…

推断后模型链式连接：问题陈述与面向端到端数字孪生的基于模型自助法AI 解读

End-to-end digital twins can be constructed by fitting separate stage models and connecting them after inference. We investigate the Frequentist prediction properties of a commonly used Monte Carlo simulation scheme that forwards complete predictive draws through such a chain when measured stage outputs contain independent normal errors, but the latent stage means are deterministic. Analytical two-stage examples show persistent asymptotic interval and coverage inflation and,…

Longitudinal modified treatment policies (LMTPs) are a class of interventions that allow the definition, identification, and estimation of causal effects in general settings, including continuous or multivariate exposures and treatment regimens that require grace periods. Targeted machine learning estimators (i.e., double/debiased) have been formulated for LMTPs that assign exposure at time $t$ as a function of the natural treatment value at time $t$. However, applications…

Missing data is pervasive in many scientific domains such as public health, environmental science, and the social sciences. Recoverability from missing data is typically studied using fully specified variable-level missingness models despite that, in many applications, only coarse structural information is available, for instance when variables are grouped into clusters due to limited knowledge or interpretability reasons. In this paper, we investigate recoverability from…

单臂临床试验结合外部对照数据摘要的治疗效应估计AI 解读

Randomized controlled trials are crucial for assessing new treatments, but they are often infeasible due to ethical or practical constraints. In these cases, single-arm trials combined with external control data constitute an alternative approach for estimating treatment effects. However, individual patient-level data on external controls are frequently inaccessible due to privacy and economic concerns. Therefore, investigators rely on data summaries published by previous…

向量增量处理效应AI 解读

Causal questions involving multiple binary treatments are poorly served by standard causal approaches. Potential outcomes grow exponentially with the number of treatments, many treatment combinations are rarely or never observed, and fixing everyone to one combination can require implausible positivity assumptions. We extend incremental propensity score interventions to vector-valued binary treatments, defining causal effects through counterfactual distributions that scale…

Missing outcomes in randomized controlled trials are often handled by multiple imputation (MI). Rubin's rules are routinely used to estimate standard errors but can fail to provide valid standard error estimates for some commonly used procedures, such as reference-based imputation. We propose a one-step alternative by explicitly targeting the treatment effect implied by a given imputation model and constructing an efficient one-step estimator for that treatment effect via…

We develop Gaussian approximations for high-dimensional vectors formed by second-order $U$-and $V$-statistics whose kernels depend on sample size. To accommodate weighted and two-sample $U$-and $V$-statistics within a unified framework, we establish our results under independent but not identically distributed (i.n.i.d.) sampling. The resulting theorems apply to a broad range of problems, including estimation in linear regression models with many covariates or many-weak…

贝叶斯聚类调整标准化均值差的模拟研究AI 解读

Reporting standardised mean differences of continuous psychometric measures is omnipresent in psychology. In a randomised controlled trial with a multilevel structure, one can define a cluster-adjusted version of these quantities naturally taking into account heterogeneous residual variances and accounting for the multilevel structure. In this article, we carefully define the underlying location-scale distributional mixed-effects models and derive cluster-adjusted…

This paper studies model selection in high dimensional regression settings. We propose a Boosting with Multiple Testing (BMT) approach designed to address proxy signal contamination in environments with strongly correlated regressors, including settings driven by latent common factors. At each stage, at most one regressor is selected conditional on those already included, using a multiple testing threshold for the remaining candidates. Under stated dependence, detectability…

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…

去中心化稀疏分位数回归AI 解读

Distributed statistical learning has recently attracted significant attention owing to its computational efficiency and scalability in large-scale systems. However, the non-smooth quantile loss poses substantial challenges to the development of efficient decentralized algorithms for quantile regression. Existing methods may either suffer from slow convergence or rely on smoothing-based approximations that require additional stringent assumptions, particularly in…

用卷积神经网络估计“已实现”偏度AI 解读

We propose a new estimator of low-frequency skewness (monthly) that exploits high-frequency data (5 min) through a direct functional mapping consisting of layers of Convolutional Neural Networks followed by Multi Layer Perceptrons. Monte Carlo experiments involving stochastic volatility models, with and without jumps, show that the estimator reduces finite-sample bias relative to existing realized-skewness estimators and remains accurate under model misspecification.…

Mendelian randomization (MR) is a widely used tool for causal inference in the presence of unmeasured confounders, which uses single nucleotide polymorphisms (SNPs) as instrumental variables to estimate causal effects. However, SNPs often have weak effects on complex traits, leading to bias in existing MR analyses when weak instruments are included. In addition, existing MR methods often restrict analysis to independent SNPs via linkage disequilibrium clumping and result in…

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…

列表删除何时有效：因果图与完全案例回归AI 解读

Listwise deletion (LWD) appears more common than imputation: our review of leading journals finds that about 50% of articles state or imply LWD, versus 7% describing an imputation model. Assuming oracle regression with all required values observed consistently estimates the population average treatment effect, we ask when complete-case regression remains consistent. With constant effects, we offer two checks: whether covariates and the missing indicator satisfy the…

总量校准可能掩盖压力状态覆盖不足：递归金融压力下的有限记忆预测区间AI 解读

Sequential forecast intervals are often evaluated by aggregate coverage even when calibration memory is bounded. This can be misleading when economically important states recur after long absences: a recent-only buffer may satisfy its overall coverage target while no longer containing errors from earlier episodes of the returning state. We study this problem in next-day financial range forecasting, holding the forecasting model, conformity score, adaptive update, and memory…

Augmented inverse probability weighting (AIPW), targeted maximum likelihood estimation (TMLE), and double/debiased machine learning (DML) are three asymptotically equivalent routes to the same efficient influence function for the average treatment effect. We contribute a worked, shared-nuisance reconciliation on real data: what a practitioner must hold fixed for the routes and their software to agree, and when they do not. Using open data on smoking cessation and weight…

非参数GARCH：迭代神经估计器AI 解读

We study nonparametric estimation and forecasting for GARCH-type processes and propose an iterated neural estimator that alternates between estimating the recursion map and updating the unobserved conditional variances. Under mild conditions, we establish finite-sample bounds for the time-averaged in-sample variance-path error and show that the estimator is minimax rate-optimal up to logarithmic factors. Under stationarity and mixing conditions, we establish the same rate…

This article provides an introduction to the Regression Discontinuity (RD) design, and its application to empirical research in the medical sciences. While the main focus of this article is on causal interpretation, key concepts of estimation and inference are also briefly mentioned. A running medical empirical example is provided.

PanelGP：资产收益面板的概率因子模型AI 解读

We develop PanelGP, a conditional factor model for large panels of stock returns in which firm characteristics determine the factor loadings and economic conditions determine the expected factors. Both relations are estimated nonparametrically with Gaussian-process priors. The kernel choice nests instrumented principal component analysis (IPCA) as well as smooth nonlinear maps. Asset-specific loading innovations and common factor innovations separate idiosyncratic from…

部分识别的异方差 SVARAI 解读

This paper studies the identification of Structural Vector Autoregressions (SVARs) exploiting a break in the variances of the structural shocks. Point-identification for this class of models relies on an eigen-decomposition involving the covariance matrices of reduced-form errors and requires that all the eigenvalues are distinct. This point-identification, however, fails in the presence of multiplicity of eigenvalues. This occurs in an empirically relevant scenario where,…

This paper proposes an identification inspired by the SVAR-IV literature that uses external instruments to identify panel vector autoregressions (PVARs), and discusses associated issues of identification, estimation, and inference. I introduce a form of local average treatment effect, the $μ$-LATE, which arises when a continuous instrument targets a policy variable with linear effects, and which reduces to the local average treatment effect when the policy and the instrument…

图上加权与正则化近等渗回归的广义 lasso 表述AI 解读

In this paper we study a regularized version of nearly-isotonic regression with respect to a general partial order. In this estimator the sum of squares is weighted, with some of the weights allowed to be zero, and an additional quadratic penalty is imposed. The only assumption we make is that the quadratic part of the objective function is strictly convex, and we show that this assumption is mild. First, we show that for all sufficiently large values of the penalty…