Data-driven local bandwidth selection for additive models with missing data
From MaRDI portal
Publication:555483
DOI10.1016/J.AMC.2011.05.040zbMath1221.62065OpenAlexW2036111752MaRDI QIDQ555483
R. Raya-Miranda, María Dolores Martínez Miranda
Publication date: 22 July 2011
Published in: Applied Mathematics and Computation (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.amc.2011.05.040
Nonparametric regression and quantile regression (62G08) Estimation in multivariate analysis (62H12) Nonparametric estimation (62G05) Nonparametric statistical resampling methods (62G09)
Related Items (3)
Marginal integration \(M\)-estimators for additive models ⋮ Computation and application of robust data-driven bandwidth selection for gradient function estimation ⋮ Estimating additive models with missing responses
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- The existence and asymptotic properties of a backfitting projection algorithm under weak conditions
- Imputation of missing values using density estimation
- Linear smoothers and additive models
- Missing data methods in longitudinal studies: a review
- Optimal estimation in additive regression models
- The choice of smoothing parameter in nonparametric regression through wild bootstrap
- Robust nonparametric estimation with missing data
- Logistic regression with missing values in the covariates
- Fitting a bivariate additive model by local polynomial regression
- Asymptotic properties of backfitting estimators
- The additive model affected by missing completely at random in the covariate
- Local linear regression for generalized linear models with missing data.
- Nonparametric regression estimation with missing data
- Integration and backfitting methods in additive models -- finite sample properties and comparison
- Bandwidth selection for smooth backfitting in additive models
- Missing responses in generalised linear mixed models when the missing data mechanism is nonignorable
- Nonparametric Mean Estimation with Missing Data
- Inference and missing data
- Missing Covariates in Generalized Linear Models When the Missing Data Mechanism is Non-ignorable
- Nonparametric Estimation of Mean Functionals with Data Missing at Random
- Nonparametric Identification of Nonlinear Time Series: Projections
- Weighted Semiparametric Estimation in Regression Analysis With Missing Covariate Data
- Local multiple imputation
- Smooth Backfitting in Practice
- A kernel method of estimating structured nonparametric regression based on marginal integration
- Model Selection Criteria for Missing-Data Problems Using the EM Algorithm
- Semiparametric Models for Missing Covariate and Response Data in Regression Models
- Nonparametric conditional mean imputation
This page was built for publication: Data-driven local bandwidth selection for additive models with missing data
