Root-n-consistent semiparametric estimation of partially linear models for weakly dependent observations
From MaRDI portal
Publication:4269518
DOI10.1080/03610929908832400zbMath0931.62020OpenAlexW2063659182MaRDI QIDQ4269518
Publication date: 15 February 2000
Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1080/03610929908832400
asymptotic normalityexponential inequalitylocal polynomial regressionrandom weightsabsolutely regular process
Density estimation (62G07) Estimation in multivariate analysis (62H12) Linear regression; mixed models (62J05) Point estimation (62F10)
Cites Work
- Achieving information bounds in non and semiparametric models
- Estimation of heteroscedasticity in regression analysis
- Convergence rates for parametric components in a partly linear model
- Local linear estimation in partly linear models
- Strong convergence of sums of \(\alpha \)-mixing random variables with applications to density estimation
- Multivariate regression estimation: Local polynomial fitting for time series
- The functional law of the iterated logarithm for stationary strongly mixing sequences
- Efficient estimation in a semiparametric additive regression model with autoregressive errors
- Weighted least squares estimates in partly linear regression models
- Root-n consistent estimation in partly linear regression models
- Root-N-Consistent Semiparametric Regression
- Limiting behavior of U-statistics for stationary, absolutely regular processes
- MULTIVARIATE LOCAL POLYNOMIAL REGRESSION FOR TIME SERIES:UNIFORM STRONG CONSISTENCY AND RATES
- Local Polynomial Estimation of Regression Functions for Mixing Processes
- Root-n-consistent semiparametric estimation of partially linear models based on k-nn method
- Second Order Approximation in the Partially Linear Regression Model
- Consistent Model Specification Tests: Omitted Variables and Semiparametric Functional Forms
This page was built for publication: Root-n-consistent semiparametric estimation of partially linear models for weakly dependent observations
