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一个精确 t 检验AI 解读

Multivariate linear regression and randomization-based inference are two essential methods in statistics and econometrics. Nevertheless, the problem of producing a randomized test for the value of a single regression coefficient that is exactly valid when errors are exchangeable, and which is asymptotically valid for the best linear predictor, has remained elusive. In this paper, we produce a test that is exactly valid with exchangeable errors and which allows for general…

集合值预测与模型误设下的信息推断AI 解读

This paper proposes an information-based inference method for partially identified parameters in incomplete models that is valid both when the model is correctly specified and when it is misspecified. Key features of the method are: (i) it is based on minimizing a suitably defined Kullback-Leibler information criterion that accounts for incompleteness of the model and delivers a non-empty pseudotrue set; (ii) it is computationally tractable; (iii) its implementation is the…

最优公式工具变量AI 解读

When estimating the effects of treatments defined by complex formulas, researchers often use simple functions of exogenous shocks as instruments. A leading example is “simulated instruments” for public policy eligibility, which capture variation in state-level policy generosity. We show how more powerful instruments can be constructed by incorporating heterogeneous shock exposure while using a recentering procedure to avoid bias. We characterize the asymptotically efficient…

有序随机效用模型中参数假设的可检验含义AI 解读

We establish the testable implications of ordered random utility models under nonparametric, semi-nonparametric, and parametric assumptions. After characterizing these models in a continuous setting and in the absence of fundamental parametric restrictions, we show that assumptions about the type distribution alone are immaterial, while assumptions about the map linking types to utilities are relevant only insofar as they restrict the class of utilities at stake.…

基于条件异方差识别日度油价冲击及其不确定性AI 解读

This paper develops a daily structural VAR for identifying oil price shocks from conditional heteroskedasticity and quantifies the transmission of their timevarying uncertainty. Identification exploits volatility clustering and spillovers in a structural GARCH specification and the complete identified set is derived. We derive covariance impulse response functions and introduce a time-varying forecast error variance decomposition (TV-FEVD) that allows for a decomposition of…

紧识别SVAR中基于“软”符号限制的快速后验抽样AI 解读

We propose algorithms for conducting Bayesian inference in structural vector autoregressions identified using sign restrictions. The key feature of our approach is a sampling step based on ‘soft’ sign restrictions. This step draws from a target density that smoothly penalises parameter values that violate the restrictions, facilitating the use of computationally efficient Markov chain Monte Carlo sampling algorithms. An importance-sampling step yields draws conditional on…

利用成交价格进行有限样本拍卖推断AI 解读

We first provide inference on the quantile function of the bid distribution in firstprice or descending auctions, and the quantile function of the value distribution in second-price or ascending auctions, with a known number of bidders with symmetric independent private values. Specifically, we provide finite-sample, nonparametric, tuning parameter-free, uniform confidence bands for these quantile functions when observing only the transaction price. We also show how these…

即期相关过程日内变动的非参数检验AI 解读

The association between log-price increments of exchange-traded equities, as measured by their spot correlation estimated from high-frequency data, exhibits a pronounced upwardsloping and almost piecewise linear relationship at the intraday horizon. There is notably lower—on average less positive—correlation in the morning than in the afternoon. We develop a nonparametric testing procedure to detect such variation in a correlation process. The test statistic has a known…

当回归变量被随机分配时的标准误AI 解读

We show that the OLS variance formula simplifies when values of the regressor of interest are assigned randomly and independently of other regressors. When the regressor of interest is independent not only of other regressors but also of the error term, the homoskedastic variance formula is valid even if regression errors are correlated. When the error term is heteroscedastic with respect to the regressor of interest, the variance formula has to be adjusted not only for…

条件影响函数AI 解读

There are many nonparametric objects of interest that are functionals of a conditional distribution, such as average treatment effects conditional on a low-dimensional set of covariates. We define a conditional influence function (CIF) as the Gateaux derivative of a conditional parameter with respect to perturbations of the conditional law of a single observation given the conditioning variable. CIFs generalize classical influence functions and can be used to (i) construct…

样本选择下观测数据中总体平均与分位处理效应的部分识别AI 解读

ABSTRACT This article partially identifies population treatment effects in observational data under both non‐random treatment assignment and sample selection. Bounds are provided for both average and quantile population treatment effects, combining assumptions for the selected and the non‐selected subsamples. We show how different assumptions help narrow identification regions, and we illustrate our methods by partially identifying the effect of maternal education on the…

深度变换模型AI 解读

The increasing data complexity leads to the demand for more general and flexible modeling approaches. We consider a nonparametric transformation model with weak assumptions, which encompasses many popular models as special cases. A rank-based deep neural network (DNN) estimation approach is developed, with a loss function significantly different from the existing ones. It can accommodate outliers in response, which is a robustness property not broadly shared. For…

块状信号下的高维聚类与信号恢复AI 解读

This paper studies computationally efficient methods and their minimax optimality for high-dimensional clustering and signal recovery under block signal structures. We propose two sets of methods, cross-block feature aggregation PCA (CFA-PCA) and moving average PCA (MA-PCA), designed for sparse and dense block signals, respectively. Both methods adaptively utilize block signal structures, applicable to non-Gaussian data with heterogeneous variances and non-diagonal…

资产定价模型中 Alpha 的一般随机化检验AI 解读

We propose a methodology to test for the null hypothesis that the alphas of a panel of asset returns are jointly equal to zero in a linear factor pricing model with observable and tradable factors — that is, the null of “zero alpha”. The test is based on equation-by-equation estimation, using a randomized version of the estimated alphas, which only requires rates of convergence. The distinct features of the proposed methodology are that it does not require the estimation of…

ARMA-Design：部分可观测实验中 A/B 测试的最优处理分配AI 解读

Online experiments are frequently employed in technological companies to evaluate the performance of a newly developed policy, product, or treatment relative to a baseline control. In many applications, the experimental units receive a sequence of treatments over time. To handle these time-dependent settings, existing A/B testing solutions typically assume a fully observable experimental environment that satisfies the Markov condition. However, this assumption often does not…

任意噪声污染下线性模型的置信区间AI 解读

Summary We study confidence interval construction for linear regression under Huber’s contamination model, where an unknown fraction of noise variables is arbitrarily corrupted. While robust point estimation in this setting is well understood, statistical inference remains challenging, especially because the contamination proportion is not identifiable from the data. We develop a new algorithm that constructs confidence intervals for individual regression coefficients…

高维点过程的正则化谱估计AI 解读

Summary Advances in modern technology have enabled the simultaneous recording of neural spiking activity across large numbers of neurons, which statistically can be represented by a multivariate point process. We characterise the second order structure of this process via the spectral density matrix, a frequency domain equivalent of the covariance matrix. In the context of neuronal analysis, statistics based on the spectral density matrix can be used to infer connectivity in…

两阶段抽样研究的最优两步估计方法AI 解读

Summary Two-phase sampling is commonly adopted for reducing cost and improving estimation efficiency. In many two-phase studies, the outcome and some cheap covariates are observed for a large sample in Phase I, and expensive covariates are obtained for a selected subset of the sample in Phase II. As a result, the analysis of the association between the outcome and covariates faces a missing data problem. Complete-case analysis, which relies solely on the Phase II sample, is…

前门模型下因果效应的灵活非参数推断AI 解读

Abstract Evaluating causal treatment effects in observational studies requires addressing confounding. While the back-door criterion enables identification through adjustment for observed covariates, it fails in the presence of unmeasured confounding. The front-door criterion offers an alternative by leveraging variables that fully mediate the treatment effect and are unaffected by unmeasured confounders of the treatment-outcome pair. We develop novel one-step and targeted…

检测异质组织跨样本空间表达变异的灵活贝叶斯框架AI 解读

Spatial transcriptomics measures gene expression alongside the spatial coordinates of each capture spot or cell across tissue samples. The detection of spatially variable (SV) genes, whose expression exhibits systematic spatial variation, enables the delineation of functional tissue domains and the identification of region-specific transcriptional changes that underlie disease heterogeneity. However, empirical evidence from spatial transcriptomics data indicates that…

共形化鲁棒优化的最优模型选择AI 解读

In decision-making under uncertainty, Contextual Robust Optimization (CRO) provides reliability by minimizing the worst-case decision loss over a prediction set. While recent advances use conformal prediction to construct prediction sets for machine learning models, the downstream decisions critically depend on model selection. This paper introduces novel model selection frameworks for CRO that unify robustness control with decision risk minimization. We first propose…

结合协变量与稀疏内在得分的实体排序AI 解读

This paper addresses the item ranking problem with associated covariates, focusing on scenarios where the preference scores can not be fully explained by covariates, and the remaining intrinsic scores are sparse. Specifically, we extend the pioneering Bradley-Terry-Luce (BTL) model by incorporating covariate information and considering sparse individual intrinsic scores. Our work introduces novel model identification conditions and examines the statistical rates of the…

超越固定截断时点：临床试验中的自适应限制平均生存时间方法AI 解读

Summary Restricted mean survival time offers a compelling nonparametric alternative to hazard ratios for right-censored time-to-event data, particularly when the proportional hazards assumption is violated. By capturing the total event-free time over a specified horizon, it provides an intuitive and clinically meaningful measure of absolute treatment benefit. Nonetheless, selecting the restriction time poses challenges: choosing a small restriction time may overlook…

最优治疗规则下平均结局的最优推断AI 解读

Abstract When an optimal treatment regime (OTR) is considered, we need to evaluate the OTR in a valid and efficient way. The classical inference applied to the mean outcome under OTR, assuming the OTR is the same as the estimated OTR, might be biased when the regularity assumption that OTR is unique is violated. Although several methods have been proposed to allow nonregularity in such inference, its optimality is unclear due to challenges in deriving semiparametric…