Statistical inference in the partial linear models with the inverse gaussian kernel

DOI10.1080/03610918.2017.1377240OpenAlexW2758991662MaRDI QIDQ5086146

Juxia Xiao, Xu Li, Jian-Hong Shi

Publication date: 1 July 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2017.1377240

zbMATH Keywords

heteroscedasticitypartial linear modelhomoscedasticitylarge sample propertiesinverse Gaussian kernel

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Asymptotic properties of nonparametric inference (62G20)

Related Items (2)

Adaptive nonparametric regression on finite support ⋮ Asymptotic properties of Dirichlet kernel density estimators

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Beta kernel estimators for density functions
• Local multiplicative bias correction for asymmetric kernel density estimators
• Inverse beta transformation in kernel density estimation
• Wavelet estimation of partially linear models
• Local polynomial estimation in partial linear regression models under dependence
• Convergence rates for parametric components in a partly linear model
• A two-stage spline smoothing method for partially linear models
• Local linear estimation in partly linear models
• Kernel density estimation of actuarial loss functions
• Generalized Birnbaum-Saunders kernel density estimators and an analysis of financial data
• Large sample results for varying kernel regression estimates
• Generalised kernel smoothing for non-negative stationary ergodic processes
• Root-N-Consistent Semiparametric Regression
• Transformations in Density Estimation
• Density estimation using inverse and reciprocal inverse Gaussian kernels
• Asymptotic results in gamma kernel regression
• Probability density function estimation using gamma kernels

This page was built for publication: Statistical inference in the partial linear models with the inverse gaussian kernel