Weighted least squares estimates in partly linear regression models

DOI10.1016/0167-7152(95)00086-0zbMath0847.62032OpenAlexW2068402023MaRDI QIDQ1916207

Anton Schick

Publication date: 30 September 1996

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/0167-7152(95)00086-0

zbMATH Keywords

random weightsheteroscedastic errorspartly linear regression modelspline estimatesroot-\(n\) consistent weighted least squares estimates

Mathematics Subject Classification ID

Density estimation (62G07) Asymptotic properties of nonparametric inference (62G20)

Related Items (9)

Prediction with Additive Models—Simulation and Application with Real Data ⋮ Root-n-consistent semiparametric estimation of partially linear models for weakly dependent observations ⋮ Efficiency for heteroscedastic regression with responses missing at random ⋮ Truncated estimator of asymptotic covariance matrix in partially linear models with heteroscedastic errors ⋮ Semiparametric modeling and estimation of heteroscedasticity in regression analysis of cross-sectional data ⋮ Uniform convergence rate of estimators of autocovariances in partly linear regression models with correlated errors ⋮ Jackknife Estimation for Smooth Functions of the Parametric Component in Partially Linear Regression Models ⋮ Plug-in bandwidth choice in partial linear models with autoregressive errors ⋮ Asymptotic normality in a Semiparametric partially linear model with right–censored data

Cites Work

• Unnamed Item
• Convergence rates for parametric components in a partly linear model
• Correction to ``A note on the construction of asymptotically linear estimators

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