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相关随机系数变换模型的识别与估计AI 解读

This study examines identification and estimation in a correlated random coefficients (CRC) model with an unknown transformation of the dependent variable, namely $\lambda \left (Y^{*}\right)=B_{0}+X^{\prime }B$ , where the latent outcome $Y^{*}$ may be subject to a certain kind of censoring mechanism, $\lambda (\cdot)$ is an unknown, one-to-one monotone function, and the random coefficients $\left (B_{0},B\right)$ are allowed to be correlated with one or several components…

高维中介分析：筛选连接童年创伤与皮质醇应激反应的 DNA 甲基化位点AI 解读

Childhood trauma tends to influence cortisol stress reactivity through the mediating effects of DNA methylation. Houtepen et al. conducted a study to investigate the role of DNA methylation in cortisol stress reactivity and its relationship with childhood trauma. The study collected a dataset consisting of 385,882 DNA methylation loci, cortisol stress reactivity, one-dimensional score on a childhood trauma questionnaire and several covariates for 85 healthy individuals. Of…

含内生协变量的复合分布 Copula 回归及保险免赔额定价应用AI 解读

This article concerns deductible pricing in nonlife insurance contracts. The primary interest of insurers is the effect of the contract deductible on a policyholder’s aggregate loss that is determined by a compound distribution where the sum of individual claim amount is stopped by the number of claims. Policyholders choose the deductible level based on their hidden risks, which makes deductible endogenous in the regressions for both claim frequency and claim severity. To…

网络数据的动态随机区块回归模型：国际军事冲突应用AI 解读

The decision to engage in military conflict is shaped by many factors, including state- and dyad-level characteristics as well as the state’s membership in geopolitical coalitions. Supporters of the democratic peace theory, for example, hypothesize that the community of democratic states is less likely to wage war with each other. Such theories explain the ways in which nodal and dyadic characteristics affect the evolution of conflict patterns over time via their effects on…

极值均值回归（Extremile Regression）AI 解读

Regression extremiles define a least squares analogue of regression quantiles. They are determined by weighted expectations rather than tail probabilities. Of special interest is their intuitive meaning in terms of expected minima and maxima. Their use appears naturally in risk management where, in contrast to quantiles, they fulfill the coherency axiom and take the severity of tail losses into account. In addition, they are comonotonically additive and belong to both the…

高维空间分位数函数对标量回归AI 解读

This article develops a novel spatial quantile function-on-scalar regression model, which studies the conditional spatial distribution of a high-dimensional functional response given scalar predictors. With the strength of both quantile regression and copula modeling, we are able to explicitly characterize the conditional distribution of the functional or image response on the whole spatial domain. Our method provides a comprehensive understanding of the effect of scalar…

非离散暴露下因果零假设的非参数检验AI 解读

In many scientific studies, it is of interest to determine whether an exposure has a causal effect on an outcome. In observational studies, this is a challenging task due to the presence of confounding variables that affect both the exposure and the outcome. Many methods have been developed to test for the presence of a causal effect when all such confounding variables are observed and when the exposure of interest is discrete. In this article, we propose a class of…

极小极大高效随机实验设计及其模型稳健预测应用AI 解读

In game theory and statistical decision theory, a random (i.e., mixed) decision strategy often outperforms a deterministic strategy in minimax expected loss. As experimental design can be viewed as a game pitting the Statistician against Nature, the use of a random strategy to choose a design will often be beneficial. However, the topic of minimax-efficient random strategies for design selection is mostly unexplored, with consideration limited to Fisherian randomization of…

基于未测量混杂比例的敏感性分析AI 解读

In observational studies, identification of ATEs is generally achieved by assuming that the correct set of confounders has been measured and properly included in the relevant models. Because this assumption is both strong and untestable, a sensitivity analysis should be performed. Common approaches include modeling the bias directly or varying the propensity scores to probe the effects of a potential unmeasured confounder. In this article, we take a novel approach whereby…

无金标准病例定义下的疟疾疫苗效力估计：孟德尔析因设计AI 解读

Accurate estimates of malaria vaccine efficacy require a reliable definition of a malaria case. However, the symptoms of clinical malaria are unspecific, overlapping with other childhood illnesses. Additionally, children in endemic areas tolerate varying levels of parasitemia without symptoms. Together, this makes finding a gold-standard case definition challenging. We present a method to identify and estimate malaria vaccine efficacy that does not require an observable…

利用多类别角度学习估计删失数据下的最优动态治疗方案AI 解读

An optimal dynamic treatment regime (DTR) consists of a sequence of decision rules in maximizing long-term benefits, which is applicable for chronic diseases such as HIV infection or cancer. In this article, we develop a novel angle-based approach to search the optimal DTR under a multicategory treatment framework for survival data. The proposed method targets to maximize the conditional survival function of patients following a DTR. In contrast to most existing approaches…

多尺度分位数分段AI 解读

We introduce a new methodology for analyzing serial data by quantile regression assuming that the underlying quantile function consists of constant segments. The procedure does not rely on any distributional assumption besides serial independence. It is based on a multiscale statistic, which allows to control the (finite sample) probability for selecting the correct number of segments S at a given error level, which serves as a tuning parameter. For a proper choice of this…

以分布泛函为目标的序贯处理分配AI 解读

Consider a setting in which a policy maker assigns subjects to treatments, observing each outcome before the next subject arrives. Initially, it is unknown which treatment is best, but the sequential nature of the problem permits learning about the effectiveness of the treatments. While the multi-armed-bandit literature has shed much light on the situation when the policy maker compares the effectiveness of the treatments through their mean, much less is known about other…

用于高维推断的马尔可夫邻域回归AI 解读

This article proposes an innovative method for constructing confidence intervals and assessing p-values in statistical inference for high-dimensional linear models. The proposed method has successfully broken the high-dimensional inference problem into a series of low-dimensional inference problems: For each regression coefficient βi, the confidence interval and p-value are computed by regressing on a subset of variables selected according to the conditional independence…

标准合成控制：同时使用全部干预前结果与协变量的情形AI 解读

It is becoming increasingly popular in applications of standard synthetic control methods to include the entire pretreatment path of the outcome variable as economic predictors. We demonstrate both theoretically and empirically that using all outcome lags as separate predictors renders all other covariates irrelevant in such settings. This finding holds irrespective of how important these covariates are for accurately predicting posttreatment values of the outcome,…

社交网络中同质性与聚类的结构模型AI 解读

I develop and estimate a structural model of network formation with heterogeneous players and latent community structure, whose equilibrium homophily and clustering levels match those usually observed in real-world social networks. Players belong to communities unobserved by the econometrician and have community-specific payoffs, allowing preferences to have a bias for similar people. Players meet sequentially and decide whether to form bilateral links, after receiving a…

生产前沿的准贝叶斯推断AI 解读

This article proposes to estimate and infer the production frontier by combining multiple first-stage extreme quantile estimates via the quasi-Bayesian method. We show the asymptotic properties of the proposed estimator and the validity of the inference procedure. The finite sample performance of our method is illustrated through simulations and an empirical application.

超越比较案例研究的合成控制估计：最低工资是否减少就业？AI 解读

Panel data are often used in empirical work to account for fixed additive time and unit effects. The synthetic control estimator relaxes the assumption of additive fixed effects for comparative case studies in which a treated unit adopts a single policy. This article generalizes the synthetic control estimator to estimate parameters associated with multiple discrete or continuous explanatory variables, jointly estimating the parameters and synthetic controls for each unit. I…

断点回归设计中的局部多项式阶数选择AI 解读

Treatment effect estimates in regression discontinuity (RD) designs are often sensitive to the choice of bandwidth and polynomial order, the two important ingredients of widely used local regression methods. While Imbens and Kalyanaraman and Calonico, Cattaneo, and Titiunik provided guidance on bandwidth, the sensitivity to polynomial order still poses a conundrum to RD practitioners. It is understood in the econometric literature that applying the argument of bias reduction…

PCDID：趋势可能非平行且随机时的主成分双重差分AI 解读

We develop a class of regression-based estimators, called Principal Components Difference-in-Differences (PCDID) estimators, for treatment effect estimation. Analogous to a control function approach, PCDID uses factor proxies constructed from control units to control for unobserved trends, assuming that the unobservables follow an interactive effects structure. We clarify the conditions under which the estimands in this regression-based approach represent useful causal…

剩余截面相关检验中的偶然参数问题AI 解读

In this article, we consider the properties of the Pesaran CD test for cross-section correlation when applied to residuals obtained from panel data models with many estimated parameters. We show that the presence of period-specific parameters leads the CD test statistic to diverge as the time dimension of the sample grows. This result holds even if cross-section dependence is correctly accounted for and hence constitutes an example of the incidental parameters problem. The…

多折点分位数回归的估计与推断AI 解读

This article proposes a new Multi-Kink Quantile Regression (MKQR) model which assumes different linear quantile regression forms in different regions of the domain of the threshold covariate but are still continuous at kink points. First, we investigate parameter estimation, kink points detection and statistical inference in MKQR models. We propose an iterative segmented quantile regression algorithm for estimating both the regression coefficients and the locations of kink…

基于分位数相关的变量选择AI 解读

This article is concerned with identifying important features in high-dimensional data analysis, especially when there are complex relationships among predictors. Without any specification of an actual model, we first introduce a multiple testing procedure based on the quantile correlation to select important predictors in high dimensionality. The quantile-correlation statistic is able to capture a wide range of dependence. A stepwise procedure is studied for further…

右删失持续时间结果的非参数工具变量回归AI 解读

This article analyzes the effect of a discrete treatment Z on a duration T. The treatment is not randomly assigned. The confounding issue is treated using a discrete instrumental variable explaining the treatment and independent of the error term of the model. Our framework is nonparametric and allows for random right censoring. This specification generates a nonlinear inverse problem and the average treatment effect is derived from its solution. We provide local and global…