Kernel classification with missing data and the choice of smoothing parameters
From MaRDI portal
Publication:2010808
DOI10.1007/s00362-017-0883-yzbMath1432.62179OpenAlexW2585242274MaRDI QIDQ2010808
Levon Demirdjian, Majid Mojirsheibani
Publication date: 28 November 2019
Published in: Statistical Papers (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1007/s00362-017-0883-y
Classification and discrimination; cluster analysis (statistical aspects) (62H30) Missing data (62D10)
Related Items (2)
On statistical classification with incomplete covariates via filtering ⋮ Robust estimation of single index models with responses missing at random
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Bayesian spatial regression models with closed skew normal correlated errors and missing observations
- On the correct regression function (in \(L_{2}\)) and its applications when the dimension of the covariate vector is random
- Pseudolikelihood ratio test with biased observations
- Kernel regression estimation for incomplete data with applications
- The tight constant in the Dvoretzky-Kiefer-Wolfowitz inequality
- On the amount of noise inherent in bandwidth selection for a kernel density estimator
- Distribution-free consistency results in nonparametric discrimination and regression function estimation
- Asymptotically efficient solutions to the classification problem
- Consistent nonparametric regression from recursive partitioning schemes
- On the almost everywhere convergence of nonparametric regression function estimates
- Bounds for the uniform deviation of empirical measures
- An equivalence theorem for \(L_ 1\) convergence of the kernel regression estimate
- Estimation of regression models with equi-correlated responses when some observations on the response variable are missing
- Bandwidth choice for nonparametric classification
- A distribution-free theory of nonparametric regression
- Marginal density estimation from incomplete bivariate data
- Kernel classification rules from missing data
- The rates of convergence of kernel regression estimates and classification rules
- Statistical Classification with Missing Covariates
- Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
- Semiparametric Regression Analysis With Missing Response at Random
- Convergence of stochastic processes
- Discriminant analysis when a block of observations is missing
This page was built for publication: Kernel classification with missing data and the choice of smoothing parameters