论文检索

非参数回归中的相变AI 解读

When the unknown regression function of a single variable is known to have derivatives up to the (γ+1)th order bounded in absolute values by a common constant everywhere or a.e. (i.e., (γ+1)th degree of smoothness), the minimax optimal rate of the mean integrated squared error (MISE) is stated as 1n2γ+22γ+3 in the literature. This paper shows that: (i) if n≤γ+12γ+3, the minimax optimal MISE rate is lognnlog(logn) and the optimal degree of smoothness to exploit is roughly…

近似稀疏相关随机效应面板Probit模型的统计推断AI 解读

We propose a simple procedure based on an existing “debiased” l1-regularized method for inference of the average partial effects (APEs) in approximately sparse probit and fractional probit models with panel data, where the number of time periods is fixed and small relative to the number of cross-sectional observations. Our method is computationally simple and does not suffer from the incidental parameters problems that come from attempting to estimate as a parameter the…

高维内生回归变量与工具变量下的稀疏线性模型及 ℓ₁ 正则化两阶段最小二乘AI 解读

We explore the validity of the 2-stage least squares estimator with l1-regularization in both stages, for linear triangular models where the numbers of endogenous regressors in the main equation and instruments in the first-stage equations can exceed the sample size, and the regression coefficients are sufficiently sparse. For this l1-regularized 2-stage least squares estimator, we first establish finite-sample performance bounds and then provide a simple practical method…

高维参数系数半参数回归模型的非渐近分析AI 解读

We consider a two-step projection based Lasso procedure for estimating a partially linear regression model where the number of coefficients in the linear component can exceed the sample size and these coefficients belong to the $l_{q}$-“balls” for $q\in[0,1]$. Our theoretical results regarding the properties of the estimators are nonasymptotic. In particular, we establish a new nonasymptotic “oracle” result: Although the error of the nonparametric projection per se (with…