Confidence Intervals for the Quantiles of a Finite Population: Simple Random and Stratified Simple Random Sampling
有限总体分位数的置信区间:简单随机抽样与分层简单随机抽样AI 解读 ↓
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有限总体分位数的置信区间:简单随机抽样与分层简单随机抽样AI 解读 ↓
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Summary Given a finite population consisting of N units, it is desired to have a confidence interval for the (t/N)th quantile, Y (t), of the population. Assuming simple random sampling, a confidence interval procedure is suggested and several expressions for the associated confidence coefficient are given. With stratified simple random sampling, for L = 2 strata, three confidence interval procedures are suggested. The associated confidence coefficients are derived under each of two (illustrative) assumptions about the nature of the stratification. (These assumptions refer to a probabilistic knowledge about the stratification.) The procedures are then compared using data from several finite populations.
The conditional average treatment effect (CATE) is frequently estimated in clinical studies to refute a homogeneous treatment effect hypothesis. Under this regime, all patients making up the population experience identical benefit from a given treatment relative to a comparator. Uncovering heterogeneous treatment effects through inference about the CATE, however, requires that covariates truly modifying the treatment effect be reliably collected at baseline. CATE-based…
异质性高维分位数回归的全局推断
Quantile regression has received considerable attention in high dimensional methodology due to its robustness and ability to accommodate heteroskedasticity. Despite this attention, current inferential methods for high dimensional quantile regression have significant limitations---only testing significance, testing at a single quantile at a time, or assuming homoskedasticity. To generalize the inferences that can be drawn in high dimensional quantile regression, we introduce…
面板数据分位数回归的GMM方法
This paper develops a smoothed generalized method of moments (GMM) framework for panel quantile models with individual-specific fixed-effects and endogenous regressors. By smoothing the indicator in the quantile moment conditions and profiling out the individual effects through smoothed individual quantile restrictions, the proposed estimator can be implemented using standard GMM algorithms. We establish consistency, asymptotic normality, and feasible inference under weak…
非精确匹配何时能在无需调整的情况下保证平衡与有效推断?
One-to-one matching without replacement is a classical approach to constructing comparable treated and control samples in the design of observational studies. It pairs each treated unit with a distinct control while minimizing a covariate distance objective. With continuous covariates, the matched pairs generally remain inexact, which contributes to bias in downstream analysis. Its key theoretical properties, such as the resulting imbalance between matched pairs and when it…
超越来源域整体迁移:高维分位数回归的样本级学习
This paper studies transfer learning for high-dimensional quantile regression with limited target samples and heterogeneous source domains. We propose a sample-level transfer learning method (SL-TL) that combines sample selection and importance weighting. Unlike existing source-level approaches that include or exclude entire source domains, SL-TL identifies transferable samples within each source domain and incorporates them through adaptive importance weights. The proposed…
极端分位数回归加权最大似然估计量的存在性与一致性
Estimating extreme conditional quantiles faces two major challenges: understanding complex nonlinear relationships between variables and accurately extrapolating into the tails of the distribution, where data are sparse. To address both issues, we introduce a novel approach that combines the theoretical framework of block maxima with the predictive power of generalized random forests. The conditional distribution of maxima is modeled by a generalized extreme value (GEV)…