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基于排列的贪婪因果推断算法的一致性保证AI 解读

Summary Directed acyclic graphical models are widely used to represent complex causal systems. Since the basic task of learning such a model from data is NP-hard, a standard approach is greedy search over the space of directed acyclic graphs or Markov equivalence classes of directed acyclic graphs. As the space of directed acyclic graphs on $p$ nodes and the associated space of Markov equivalence classes are both much larger than the space of permutations, it is desirable to…

协变量数量增长时完全随机实验中的回归调整AI 解读

Summary Randomized experiments have become important tools in empirical research. In a completely randomized treatment-control experiment, the simple difference in means of the outcome is un- biased for the average treatment effect, and covariate adjustment can further improve the efficiency without assuming a correctly specified outcome model. In modern applications, experimenters often have access to many covariates, motivating the need for a theory of covariate adjustment…

连续处理下工具变量有效性的不可检验性AI 解读

Summary This note presents a proof of the conjecture in Pearl (1995) about testing the validity of an instrumental variable in hidden variable models. It implies that instrument validity cannot be tested in the case where the endogenous treatment is continuously distributed. This stands in contrast to the classical testability results for instrument validity when the treatment is discrete. However, imposing weak structural assumptions on the model, such as continuity between…

疫苗试验主替代指标评价中缺失协变量下的变点推断AI 解读

Summary We consider the use of threshold-based regression models to evaluate immune response biomarkers as principal surrogate markers of a vaccine’s protective effect. Threshold-based regression models, which allow the relationship between a clinical outcome and a covariate to change dramatically across a threshold value in the covariate, have been studied by various authors under fully observed data. Limited research, however, has examined these models in the presence of…

学习基于 U 统计量的矩阵块结构AI 解读

Summary We introduce a conceptually simple, efficient and easily implemented approach for learning the block structure in a large matrix. Using the properties of U-statistics and large-dimensional random matrix theory, the group structure of many variables can be directly identified based on the eigenvalues and eigenvectors of the scaled sample matrix. We also establish the asymptotic properties of the proposed approach under mild conditions. The finite-sample performance of…

全基因组关联研究中协变量自适应的族错误率控制AI 解读

Summary The familywise error rate has been widely used in genome-wide association studies. With the increasing availability of functional genomics data, it is possible to increase detection power by leveraging these genomic functional annotations. Previous efforts to accommodate covariates in multiple testing focused on false discovery rate control, while covariate-adaptive procedures controlling the familywise error rate remain underdeveloped. Here, we propose a novel…

事件史方法与拓扑数据分析AI 解读

Summary Persistent homology is used to track the appearance and disappearance of features as we move through a nested sequence of topological spaces. Equating the nested sequence to a filtration and the appearance and disappearance of features to events, we show that simple event history methods can be used for the analysis of topological data. We propose a version of the well-known Nelson–Aalen cumulative hazard estimator for the comparison of topological features of random…

基于最大偏差的函数型数据生物等效性检验AI 解读

Summary We study the problem of testing equivalence of functional parameters, such as the mean or the variance function, in the two-sample functional data setting. In contrast to previous work where the functional problem is reduced to a multiple testing problem for the equivalence of scalar data by comparing the functions at each point, our approach is based on an estimate of a distance measuring the maximum deviation between the two functional parameters. Equivalence is…

面板计数数据半参数回归模型的最大似然估计AI 解读

Summary Panel count data, in which the observation for each study subject consists of the number of recurrent events between successive examinations, are commonly encountered in industrial reliability testing, medical research and other scientific investigations. We formulate the effects of potentially time-dependent covariates on one or more types of recurrent events through nonhomogeneous Poisson processes with random effects. We employ nonparametric maximum likelihood…

依赖删失生存数据的半参数建模、估计与推断AI 解读

Summary When modelling survival data, it is common to assume that the survival time $T$ is conditionally independent of the censoring time $C$ given a set of covariates. However, there are numerous situations in which this assumption is not realistic. The goal of this paper is therefore to develop a semiparametric normal transformation model which assumes that, after a proper nonparametric monotone transformation, the vector $(T, C)$ follows a linear model, and the vector of…

交叉验证带宽的袋装聚合及其大数据应用AI 解读

Summary Hall & Robinson (2009) proposed and analysed the use of bagged cross-validation to choose the bandwidth of a kernel density estimator. They established that bagging greatly reduces the noise inherent in ordinary cross-validation, and hence leads to a more efficient bandwidth selector. The asymptotic theory of Hall & Robinson (2009) assumes that $N$, the number of bagged subsamples, is $\infty$. We expand upon their theoretical results by allowing $N$ to be finite, as…

利用时变波动率识别结构冲击AI 解读

Abstract I propose to identify an SVAR, up to shock ordering, using the autocovariance structure of the squared innovations implied by an arbitrary stochastic process for the shock variances. These higher moments are available without parametric assumptions on the variance process. In contrast, previous approaches exploiting heteroskedasticity rely on the path of innovation covariances, which can only be recovered from the data under specific parametric assumptions on the…

反事实与个体处理效应的共形推断AI 解读

Abstract Evaluating treatment effect heterogeneity widely informs treatment decision making. At the moment, much emphasis is placed on the estimation of the conditional average treatment effect via flexible machine learning algorithms. While these methods enjoy some theoretical appeal in terms of consistency and convergence rates, they generally perform poorly in terms of uncertainty quantification. This is troubling since assessing risk is crucial for reliable…

高维协变量观测数据中的异质性处理效应推断AI 解读

Abstract This study proposes novel estimation and inference approaches for heterogeneous local treatment effects using high-dimensional covariates and observational data without a strong ignorability assumption. To achieve this, with a binary instrumental variable, the parameters of interest are identified on an unobservable subgroup of the population (compliers). Lasso estimation under a non-convex objective function is developed for a two-stage generalized linear model,…

高维 Markov 网络的双样本推断AI 解读

Abstract Markov networks are frequently used in sciences to represent conditional independence relationships underlying observed variables arising from a complex system. It is often of interest to understand how an underlying network differs between two conditions. In this paper, we develop methods for comparing a pair of high-dimensional Markov networks where we allow the number of observed variables to increase with the sample sizes. By taking the density ratio approach,…

利用稀疏 β 模型分析网络AI 解读

Abstract Data in the form of networks are increasingly available in a variety of areas, yet statistical models allowing for parameter estimates with desirable statistical properties for sparse networks remain scarce. To address this, we propose the Sparse β-Model (SβM), a new network model that interpolates the celebrated Erdős–Rényi model and the β-model that assigns one different parameter to each node. By a novel reparameterization of the β-model to distinguish global and…

整群随机实验的模型辅助分析AI 解读

Abstract Cluster-randomized experiments are widely used due to their logistical convenience and policy relevance. To analyse them properly, we must address the fact that the treatment is assigned at the cluster level instead of the individual level. Standard analytic strategies are regressions based on individual data, cluster averages and cluster totals, which differ when the cluster sizes vary. These methods are often motivated by models with strong and unverifiable…

贝叶斯代理工具变量 SVAR 的推断AI 解读

Motivated by the increasing use of external instruments to identify structural vector autoregressions (SVARs), we develop an algorithm for exact finite sample inference in this class of time series models, commonly known as Proxy-SVARs. Our algorithm makes independent draws from any posterior distribution over the structural parameterization of a Proxy-SVAR. Our approach allows researchers to simultaneously use proxies and traditional zero and sign restrictions to identify…

利用高阶无条件矩识别结构向量自回归AI 解读

This paper pursues two objectives. First, we determine the sufficient condition for local, statistical identification of SVAR processes through the third and fourth unconditional moments of the reduced-form innovations. Our findings provide novel insights when the entire system is not identified, as they highlight which subset of structural parameters is identified and which is not. Second, we elaborate a tractable testing procedure to verify whether the identification…

外部工具变量识别的结构VAR推断AI 解读

This paper studies Structural Vector Autoregressions in which a structural shock of interest (e.g., an oil supply shock) is identified using an external instrument. The external instrument is taken to be correlated with the target shock (the instrument is relevant) and to be uncorrelated with other shocks of the model (the instrument is exogenous). The potential weak correlation between the external instrument and the target structural shock compromises the large-sample…

时间使用调查能与不能揭示的劳动供给信息AI 解读

Summary The American Time Use Survey (ATUS) accurately measures hours worked on a single day. We propose several estimators of elasticities of weekly labor supply in a linear regression model, despite certain impossibility results due to the time specific feature of the ATUS. We recommend the impute estimator , a simple modification of the standard two stage least squares estimator, that imputes the dependent variable using daily subsamples, based on our careful…

重新审视家庭内的性别认同与相对收入：密度估计的潜在陷阱AI 解读

Summary We show that Bertrand et al.'s (QJE, 2015, ) finding of a sharp drop in the relative income distribution within married couples at the point where wives start to earn more than their husbands is unstable across different estimation procedures and varies across contexts. We apply the estimators by McCrary (JoE, 2008, McC) and Cattaneo et al. (JASA, 2020, CJM) to administrative data from the United States and Germany and compare their performance in a simulation. Large…

函数型数据分布相等性的置换检验AI 解读

Summary Economic data are often generated by stochastic processes that take place in continuous time, though observations may occur only at discrete times. Such data are called functional data. This paper is concerned with comparing two or more stochastic processes that generate functional data. The data may be produced by a randomized experiment in which there are multiple treatments. The paper presents a method for testing the hypothesis that the same stochastic process…

利用期权数据推断金融泡沫AI 解读

Summary Financial bubbles arise when the underlying asset's market price deviates from its fundamental value. Unlike other bubble tests that use time series data and assume a reduced‐form price process, we infer the existence of bubbles nonparametrically using option price data. Under no‐arbitrage and acknowledging data constraints, we can partially identify asset price bubbles using a cross section of European option prices. In the empirical analysis, we obtain interval…