论文检索

用于政策学习的半参数工具变量双重差分方法AI 解读

Summary Recently, there has been a surge in methodological development for the difference-in-differences approach to evaluate causal effects. Standard methods in the literature rely on the parallel-trend assumption to identify the average treatment effect on the treated. However, the parallel-trend assumption may be violated in the presence of unmeasured confounding, and the average treatment effect on the treated may not be useful in learning a treatment assignment policy…

删失生存数据的多重校准与普适适应性AI 解读

Summary Traditional statistical and machine learning methods typically assume that the training and test data follow the same distribution. However, this assumption is frequently violated in real-world applications, where the training data in the source domain may underrepresent specific subpopulations in the test data of the target domain. This article addresses target-independent learning under covariate shift, focusing on multicalibration for survival probability and…

成对依赖马尔可夫随机场中的虚假关联AI 解读

Summary identified the issue of ′nonsense correlations’ in time series data, where dependence within each of two random vectors causes overdispersion, i.e., variance inflation, for measures of dependence between the two. Since then much has been written about nonsense correlations, but nearly all of it confined to the time series literature. In this paper we provide the first, to our knowledge, rigorous study of this phenomenon for other forms of (positive) dependence,…

非欧随机对象的稳健函数主成分分析AI 解读

Summary Functional data analysis offers a diverse toolkit of statistical methods tailored to analysing samples of real-valued random functions. Recently, samples of time-varying random objects, such as time-varying networks, have been increasingly encountered in data analysis. These data structures represent elements within general metric spaces that lack local or global linear structures, rendering traditional functional data analysis methods inapplicable. Moreover, the…

利用重尾组合检验聚合相依信号AI 解读

Summary Combining dependent $ p $-values poses a long-standing challenge in statistical inference, particularly when aggregating findings from multiple methods to enhance signal detection. Recently, $ p $-value combination tests based on regularly-varying-tailed distributions, such as the Cauchy combination test and harmonic mean $ p $-value, have attracted attention for their robustness to unknown dependence. This paper provides a theoretical and empirical evaluation of…

通过条件化提高部分合取假设检验的功效AI 解读

Summary A partial conjunction hypothesis test combines information across a set of base hypotheses to determine whether some subset is nonnull. Partial conjunction hypothesis tests arise in a diverse array of fields, but standard partial conjunction hypothesis testing methods can be highly conservative, leading to low power especially in low-signal settings commonly encountered in applications. In this paper, we introduce the conditional partial conjunction hypothesis test,…

状态切换整数值自回归模型AI 解读

Time series of counts often display complex dynamic and distributional characteristics. For this reason, we develop a flexible framework combining the integer-valued autoregressive (INAR) model with a latent Markov structure, leading to the hidden Markov model-INAR (HMM-INAR). First, we illustrate conditions for the existence of an ergodic and stationary solution and derive closed-form expressions for the autocorrelation function and its components. Second, we show…

稳健的高维时变系数估计AI 解读

In this article, we develop a novel high-dimensional coefficient estimation procedure based on high-frequency data. Unlike usual high-dimensional regression procedures such as LASSO, we additionally handle the heavy-tailedness of high-frequency observations as well as time variations of coefficient processes. Specifically, we employ the Huber loss and a truncation scheme to handle heavy-tailed observations, while $\ell _{1}$ -regularization is adopted to overcome the curse…

基于核现货波动率估计量的积分波动率泛函估计AI 解读

For a multidimensional Itô semimartingale, we consider the problem of estimating integrated volatility functionals. Jacod and Rosenbaum (2013, The Annals of Statistics 41(3), 1462–1484) studied a plug-in type of estimator based on a Riemann sum approximation of the integrated functional and a spot volatility estimator with a forward uniform kernel. Motivated by recent results that show that spot volatility estimators with general two-sided kernels of unbounded support are…

RLHF 对齐的算法偏差：偏好坍缩与匹配正则化AI 解读

Accurately aligning large language models (LLMs) with human preferences is crucial for informing fair, economically sound, and statistically efficient decision-making processes. However, we argue that the predominant approach for aligning LLMs with human preferences through a reward model—reinforcement learning from human feedback (RLHF)—suffers from an inherent algorithmic bias due to its Kullback–Leibler-based regularization in optimization. In extreme cases, this bias…

混合模型：参数、半参数与新方向AI 解读

Weixin Yao and Sijia Xiang. Boca Raton, FL: Chapman & Hall/CRC Press, 2024, xvii + 379 pp., $130.00(H), ISBN: 978-0-367-48182-7.The mixture modeling framework has become a mainstay in the toolkit o...

整合个体数据与汇总数据的贝叶斯随机效应元分析AI 解读

Meta-analysis using individual participant data (IPD) offers many benefits, including greater analytical flexibility, compared to conventional analyses based on aggregate data (AD). However, it is often hindered by restricted access to IPD. Relying solely on available IPD may introduce “data availability bias,” compromising external validity. Integrating IPD with relevant AD addresses this concern, but existing methods are restrictive, requiring precise knowledge of the…

概率预测的尾部校准AI 解读

Probabilistic forecasts comprehensively describe the uncertainty in the unknown future outcome, making them essential for decision making and risk management. While several methods have been introduced to evaluate probabilistic forecasts, existing evaluation techniques are ill-suited to the evaluation of tail properties of such forecasts. However, these tail properties are often of particular interest to forecast users due to the severe impacts caused by extreme outcomes. In…

受多臂老虎机启发的模型不确定性下最优设计AI 解读

An optimal design is usually model-dependent and is sub-optimal if the postulated model is not correctly specified. Furthermore, it is far from ideal even if it is efficient for model selection but has a poor performance for estimating parameters in the selected model. In practice, it is common that a researcher has a list of candidate models at hand and a design has to be found that is efficient for both model discrimination and parameter estimation in the (unknown) “true”…

独立簇数据的随机效应充分降维AI 解读

Sufficient dimension reduction (SDR) is a popular class of regression methods which aim to find a small number of linear combinations of covariates that capture all the information of the responses i.e., a central subspace. The majority of current methods for SDR focus on the setting of independent observations, while the few techniques that have been developed for clustered data assume the linear transformation is identical across clusters. In this article, we introduce…

利用统计推断增强 AI 生成生物医学图像的可信度AI 解读

Generative artificial intelligence (AI) has transformed the biomedical imaging field through image synthesis, addressing challenges of data availability, privacy, and diversity in biomedical research. This paper proposes a novel nonparametric method within the functional data framework to discern significant differences between the mean and covariance functions of original and synthetic biomedical imaging data, thereby enhancing the fidelity and utility of synthetic data.…

种族信息不可观测时的种族差异估计AI 解读

Estimating racial disparities without access to individual-level racial information is a common challenge in economic and policy settings. We develop a statistical method that relaxes the strong independence assumption of common race imputation approaches like Bayesian-Improved Surname Geocoding (BISG). Our identification assumption is that surname is conditionally independent of the outcome given (unobserved) race, residence location, and other observed characteristics. The…

整合微生物 16S 与宏基因组测序数据以提高差异丰度检验效率AI 解读

The most widely used technologies for profiling microbial communities are 16S marker-gene sequencing and shotgun metagenomic sequencing. Surprisingly, many microbiome studies have performed both experiments on the same cohort of samples. The two sequencing datasets often reveal consistent patterns of microbial signatures, suggesting that an integrative analysis of both datasets could enhance the testing power for these signatures. However, differential experimental biases,…

再随机化的贝叶斯准则AI 解读

Rerandomization is a powerful tool for experiment-based causal inference because it can better balance covariates than classic randomized designs, thereby leading to more accurate causal effect estimation. However, basic rerandomization and some of its extensions do not prioritize covariates that believed to be strongly associated with potential outcomes. To address this limitation, and thereby create more efficient rerandomization procedures, the quantification of covariate…

通过特征值分析识别高维时间序列结构AI 解读

Cross-sectional structures and temporal tendency are important features of high-dimensional time series. Based on eigen-analysis on sample covariance matrices, we propose a novel approach to identifying four popular structures of high-dimensional time series, which are grouped in terms of factor structures and stationarity. The proposed three-step method includes a ratio statistic of empirical eigenvalues;a projected Augmented Dickey-Fuller Test;a new unit-root test based on…

受访者驱动抽样中个体网络度数的潜变量模型AI 解读

Respondent-driven sampling (RDS) is widely used to collect data from hidden populations in social and biomedical science. Although RDS may provide comprehensive coverage of the target hidden population through social network recruitment, its non-random sampling process poses challenges for generalizing findings beyond the sample. Current analytical methods rely on the network size (degree) reported by respondents to adjust for unequal sampling probabilities. However, the…

早产儿观察研究中的工具变量强化：设计、界限与推断AI 解读

Regionalization of intensive care for premature infants refers to a triage system that directs mothers to hospitals with varying capabilities based on the risks their babies face. Given the limited capacity of highly specialized hospitals, understanding the impact of delivering premature infants at these facilities on infant mortality could facilitate the design of a more efficient perinatal regionalization system. To address this, Baiocchi et al. proposed strengthening a…

面向人类活动数据的邻接矩阵分解聚类AI 解读

Mobile apps and wearable devices accurately and continuously measure human activity; patterns within this data can provide a wealth of information applicable to fields such as transportation and health. Despite the potential utility of this data, there has been limited development of analysis methods for sequences of daily activities. In this article, we propose a novel clustering method and cluster evaluation metric for human activity data that leverages an adjacency matrix…

利用空间分布式高斯过程实现自适应AI 解读

We consider the accuracy of an approximate posterior distribution in nonparametric regression problems by combining posterior distributions computed on subsets of the data defined by the locations of the independent variables. We show that this approximate posterior retains the rate of recovery of the full data posterior distribution, where the rate of recovery adapts to the smoothness of the true regression function. As particular examples we consider Gaussian process…