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公立学校经费、学校质量与成年后的犯罪AI 解读

Abstract This paper asks whether increasing public school funding can be an effective long-run crime-prevention strategy in the United States. Specifically, we examine the effect of increases in funding early in children’s lives on the likelihood that they are arrested as adults. We exploit quasi-experimental variation in public school funding, leveraging two natural experiments in Michigan and a novel administrative data set linking the universe of Michigan public school…

不完全依从的协变量自适应随机化中，利用回归调整提高估计效率AI 解读

Abstract We investigate how to improve efficiency using regression adjustments with covariates in covariate-adaptive randomizations (CARs) with imperfect subject compliance. Our regression-adjusted estimators, which are based on the doubly robust moment for local average treatment effects, are consistent and asymptotically normal even with heterogeneous probabilities of assignment and misspecified regression adjustments. We propose an optimal but potentially misspecified…

从诊断性检验看论文发表过程中的选择压力AI 解读

Abstract Economics papers increasingly report balance, pretrend, placebo, and other “sniff tests,” rejection of which is bad news for authors, undermining the credibility of their main results. We derive nonparametric bounds on the latent proportion of significant sniff tests removed by the publication process (whether by p-hacking or relegation to the file drawer) and the proportion whose significance was due to true misspecification, not bad luck. Using a hand-collected…

基于潜在因子模型的个体化动态中介分析AI 解读

Mediation analysis plays a crucial role in causal inference as it can investigate the pathways through which treatment influences outcome. Most existing mediation analysis assumes that mediation effects are static and homogeneous within populations. However, mediation effects usually change over time and exhibit significant heterogeneity among individuals in many real-world applications. Additionally, the mediation mechanism can be complicated and involves non-sparse, making…

广义非参数模型中深度神经网络估计量的统计推断AI 解读

While deep neural networks (DNNs) are used for prediction, inference on DNN-estimated subject-specific means for categorical or exponential family outcomes remains underexplored. We address this by proposing a DNN estimator under generalized nonparametric regression models (GNRMs) and developing a rigorous inference framework. Unlike existing approaches that assume independence between estimation errors and inputs to establish the error bound, a condition often violated in…

尾部中的尾部风险：相关变量极端时的高分位数估计AI 解读

In this article we address the problem of high quantile estimation conditional on a related variable being extreme. The problem set-up is of interest in a number applications to evaluate tail risk of a focal variable in the tail of a conditioning variable. A primary example we consider is the assessment of systemic risk in financial markets using a risk measure known as the conditional value-at-risk (CoVaR). The proposed estimator is based on a novel approach to handle the…

单调连续处理效应曲线的双重稳健逐点置信区间AI 解读

We study nonparametric inference for the causal dose–response curve when the treatment variable is continuous rather than discrete. We develop doubly robust confidence intervals for the continuous treatment effect curve (at a fixed point) under the assumption that it is monotonic, based on inverting a likelihood ratio-type test. Monotonicity of the treatment effect curve is often a very natural assumption, and this assumption removes the need to choose a smoothing or tuning…

时间依赖球面随机场的多尺度 CUSUM 检验AI 解读

This paper investigates the asymptotic behavior of structural break tests in the harmonic domain for time dependent spherical random fields. In particular, we prove a functional central limit theorem result for the fluctuations over time of the sample spherical harmonic coefficients, under the null of isotropy and stationarity; furthermore, we prove consistency of the corresponding CUSUM test, under a broad range of alternatives, including deterministic trend, abrupt change,…

通过统计高效的类别调节变量促进异质效应估计AI 解读

Categorical covariates such as race, sex, or group are ubiquitous in regression analysis. While main-only (or ANCOVA) linear models are predominant, linear models that include categorical-continuous or categorical-categorical interactions are increasingly important and allow heterogeneous, group-specific effects. However, with standard approaches, the addition of categorical interactions fundamentally alters the estimates and interpretations of the main effects, often…

高频价格停滞因子与波动率估计AI 解读

In this article, we propose a price staleness factor model that accounts for pervasive market friction across assets and incorporates relevant covariates. Using large-panel high-frequency data, we derive the maximum likelihood estimators of the regression coefficients, the nonstationary factors, and their loading parameters. These estimators recover the time-varying price staleness probabilities. We develop asymptotic theory in which both the dimension d and the sampling…

结果延迟下的协变量调整响应自适应设计AI 解读

Covariate-adjusted response-adaptive (CARA) designs have gained widespread adoption for their clear benefits in enhancing experimental efficiency and participant welfare. These designs dynamically adjust treatment allocations during interim analyses based on participant responses and covariates collected during the experiment. However, delayed responses can significantly compromise the effectiveness of CARA designs, as they hinder timely adjustments to treatment assignments…

不同实验中个体处理效应分布与分位数的增强推断AI 解读

Understanding treatment effect heterogeneity has become increasingly important in many fields. In this paper we study distributions and quantiles of individual treatment effects to provide a more comprehensive and robust understanding of treatment effects beyond usual averages, despite they are more challenging to infer due to nonidentifiability from the observed data. Recent randomization-based approaches offer finite-sample valid inference for treatment effect…

基于成对比较的最优差分隐私排序AI 解读

Data privacy is a central concern in many applications involving ranking from incomplete and noisy pairwise comparisons, such as recommendation systems, educational assessments, and opinion surveys on sensitive topics. In this work, we propose differentially private algorithms for ranking based on pairwise comparisons. Specifically, we develop and analyze ranking methods under two privacy notions: edge differential privacy, which protects the confidentiality of individual…

Trans-Glasso：精度矩阵估计的迁移学习方法AI 解读

Precision matrix estimation is essential in various fields, yet it is challenging when samples for the target study are limited. Transfer learning can enhance estimation accuracy by leveraging data from related source studies. We propose Trans-Glasso, a two-step transfer learning method for precision matrix estimation. First, we obtain initial estimators using a multi-task learning objective that captures both shared and unique features across studies. Then, we refine these…

聚类回归的Jackknife标准误AI 解读

Abstract This article presents a theoretical case for replacement of conventional heteroskedasticity-consistent and cluster-robust variance estimators with jackknife variance estimators, in the context of linear regression with heteroskedastic and/or cluster-dependent observations. We examine the bias of variance estimation and the coverage probabilities of confidence intervals. Concerning bias, we show that conventional variance estimators have full downward worst-case…

含局部测量误差的非平稳函数型时间序列结构变化推断AI 解读

Abstract We study the problem of detecting and localizing change points for a general class of locally stationary functional time series. To accommodate the nonstationarity and other possible complex features, such as discontinuous trajectories, and heterogeneous partial measurement error of contemporary functional data, we propose methods that do not rest on the preprocessing techniques of presmoothing and dimension-reduction, which would be less accurate without the…

二元结局下的个体风险量化AI 解读

Abstract Understanding treatment effect heterogeneity is crucial for reliable decision-making in treatment evaluation and selection. The conditional average treatment effect (CATE) is widely used to capture treatment effect heterogeneity induced by observed covariates and to design individualized treatment policies. However, it is an average metric within subpopulations, which prevents it from revealing individual risk, potentially leading to misleading results. This article…

设计实验中失拟检验的功效：样本量与重复观测分配指南AI 解读

Although there exists substantial literature on lack-of-fit tests for normal linear models, there is scant literature for determining the number or distribution of points to replicate when starting with an un-replicated base design like a factorial, response surface, or related constructions. In this article, we provide an easy-to-implement approach to determining power and sample size based on the standard lack-of-fit test. The method employs simply stated bounds on the…

Hawkes 跳跃扩散过程的鞍点近似及风险管理应用AI 解读

We propose a statistical model based on Hawkes processes in which large financial losses can arise in close succession serially as well as cross-sectionally. We derive in closed form saddlepoint approximations to the tails of profit and loss distributions, both marginal and joint, and use them to construct explicit risk measure formulae that account for the fact that a given financial institution’s losses make it more likely that that institution will experience further…

面向人类反馈的情境在线不确定性感知偏好学习AI 解读

Reinforcement Learning from Human Feedback (RLHF) has become a pivotal paradigm in artificial intelligence to align large models with human preferences. In this paper, we propose a novel statistical framework to simultaneously conduct the online decision-making and statistical inference on the optimal model using human preference data based on dynamic contextual information. Our approach introduces an efficient decision strategy that achieves both the optimal regret bound…

连续比例数据的可扩展稳健回归模型AI 解读

Beta regression is used routinely for continuous proportional data, but it often encounters practical issues such as a lack of robustness to misspecification of the beta distribution and sensitivity to outliers. We develop an improved class of generalized linear models starting with the continuous binomial (cobin) distribution and further extending to dispersion mixtures of cobin distributions (micobin). The proposed cobin regression and micobin regression models have…

轻度每日饮酒与寿命观察性研究中证据因子的新构造AI 解读

Does light daily alcohol consumption lengthen life? In any observational study of this question, we expect drinking behavior—that is, treatment assignment—to be confounded by measured and unmeasured covariates. An observational study has two evidence factors if there are two essentially independent tests of the same null hypothesis about causal effects, where the two tests are susceptible to different unmeasured biases in treatment assignment. Because they are independent,…

利用混频数据改进密度预测：贝叶斯方法AI 解读

We construct a modeling framework that allows for the flexible contribution of intraday information to predict the conditional distribution of asset returns. A flexible spline specification is proposed to capture the contribution of intraday returns inside GARCH and stochastic volatility specifications for daily returns. The “focused Bayesian” prediction framework is adopted to allow the intraday contribution to the simpler GARCH specification to be trained according to the…

通胀、战争债券与1950年代的选民反弹AI 解读

Abstract We study the role of war bonds and inflation in post-WWII federal elections. After WWII, major bouts of inflation in 1946-48 and 1950-51 depressed the real returns of the bonds sold to households during the war. In a difference-in-differences framework, we find that counties with higher war bond purchases shifted their votes towards the Republican Party in postwar elections. To address the endogeneity of war bond purchases, we use an instrumental variables design,…