Transformation Models in High Dimensions

Transformation models are a very important tool for applied statisticians and econometricians. In many applications, the dependent variable is transformed so that homogeneity or normal distribution of the error holds. In this article, we analyze transformation models in a high-dimensional setting, where the set of potential covariates is large. We propose an estimator for the transformation parameter and we show that it is asymptotically normally distributed using an orthogonalized moment condition where the nuisance functions depend on the target parameter. In a simulation study, we show that the proposed estimator works well in small samples. A common practice in labor economics is to transform wage with the log-function. In this study, we test if this transformation holds in American Community Survey (ACS) data from the United States.

摘要来源：RePEc · 出版社摘要

笔记与分组

同方向的基础文献

相关领域论文

共同方法：高维与机器学习、矩估计与似然、检验与推断、一般估计、GMM 与矩条件

A unified spectral analysis of high-dimensional linear regression: debiasing and thresholding

Ying-Ao Wang · Yunyi Zhang · Ye Zhang首次提交 2025-02-24 · 修订 2026-10-12 · v3arXiv预印本

高维线性回归的统一谱分析：去偏与阈值化AI 解读

We use spectral analysis to establish a unified theory of debiasing, thresholding, and simultaneous inference for high-dimensional linear regression with ill-conditioned designs. The methods include Ridge regression, spectral cut-off, and iterative and dynamical regression procedures. A common debiasing procedure uses two applications of the original linear algorithm with fixed parameters. Spectral analysis shows that debiasing squares the residual, while implementations…

正则化与变量选择 GMM 与矩条件 Bootstrap 与重抽样

被引：暂无数据

AI 解读阅读原文 ↗BibTeX ↓

共同方法：高维与机器学习、矩估计与似然、检验与推断、一般估计、GMM 与矩条件

Partial Gini Covariance-based Robust Inference for High-dimensional Linear Models with Heavy-tailed Errors

Yilin Zhang · Songshan Yang · Yunan Wu · Lan Wang上线 2026-10-09Journal of the American Statistical Association在线待刊

基于偏 Gini 协方差的重尾误差高维线性模型稳健推断AI 解读

This paper introduces a novel robust high-dimensional inference framework based on partial Gini covariance. We specifically address the practically important problem of quantifying how changes in features affect the conditional mean in high-dimensional linear regression when the random noises are heavy-tailed and possibly asymmetric. Conventional high-dimensional regression inference methods suffer from inaccurate type I errors and reduced power in heavy-tailed contexts.…

正则化与变量选择 GMM 与矩条件

被引：暂无数据

AI 解读阅读原文 ↗BibTeX ↓

共同方法：高维与机器学习、矩估计与似然、检验与推断、一般估计、GMM 与矩条件

Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions

Gyungbae Park首次提交 2024-03-23 · 修订 2026-10-08 · v3arXiv预印本

This paper studies debiased machine learning when nuisance parameters appear in indicator functions. An important example is maximized average welfare gain under optimal treatment assignment rules. For asymptotically valid inference for a parameter of interest, the current literature on debiased machine learning relies on Gateaux differentiability of the functions inside moment conditions, which does not hold when nuisance parameters appear in indicator functions. In this…

机器学习与 DML GMM 与矩条件

关联记录被引 0 次 ↗OpenAlex · 核验 2026-09-30

阅读原文 ↗BibTeX ↓

共同方法：高维与机器学习、矩估计与似然、检验与推断、一般估计、GMM 与矩条件

Longitudinal causal inference under informative missingness using the self-censoring model

Iván Díaz首次提交 2026-10-06 · 修订 2026-10-06 · v1arXiv预印本

自删失模型下信息性缺失的纵向因果推断AI 解读

Longitudinal observational data are often subject to informative missingness. For example, in healthcare research using electronic health records, covariates and outcomes are measured only when patients interact with the healthcare system. Whether these data are observed usually depends on the unobserved variable values themselves, introducing a common form of data missing not at random. We develop identification and estimation methods for the effects of longitudinal…

微观 · 因果推断面板数据机器学习与 DML

被引：暂无数据

AI 解读阅读原文 ↗BibTeX ↓

共同方法：高维与机器学习、矩估计与似然、检验与推断、一般估计、GMM 与矩条件

Regularized Exponentially Tilted Empirical Likelihood for Bayesian Inference

Eunseop Kim · Steven N. MacEachern · Mario Peruggia首次提交 2023-12-28 · 修订 2026-10-01 · v5arXiv预印本

用于贝叶斯推断的正则化指数倾斜经验似然AI 解读

Bayesian inference with empirical likelihood faces a challenge as the posterior domain is a proper subset of the original parameter space due to the convex hull constraint. We propose a regularized exponentially tilted empirical likelihood to address this issue. Our method removes the convex hull constraint using a novel regularization technique, incorporating a continuous exponential family distribution to satisfy a Kullback--Leibler divergence criterion. The regularization…

贝叶斯正则化与变量选择 GMM 与矩条件

关联记录被引 0 次 ↗OpenAlex · 核验 2026-10-07

AI 解读阅读原文 ↗BibTeX ↓

共同方法：高维与机器学习、矩估计与似然、检验与推断、一般估计、GMM 与矩条件

A Simple Measure of Robustness for External Validity under Covariate Shifts

Pietro Emilio Spini收录 2026-09-29Econometrica作者接收稿

协变量漂移下外部有效性的简单稳健性度量AI 解读

This paper studies the robustness of estimated policy effects to changes in the distribution of covariates, a key determinant of the external validity of (quasi)experimental results. I propose a novel robustness metric δ ∗ which measures the smallest covariate shift needed to invalidate an empirical claim about the policy effect (e.g., AT E > 0). I estimate δ ∗ via de-biased GMM, achieving a parametric rate of convergence while accommodating machine-learning estimators of…

微观 · 因果推断正则化与变量选择机器学习与 DML

被引：暂无数据

AI 解读阅读原文 ↗BibTeX ↓