Curve fitting and jump detection on nonparametric regression with missing data

DOI10.1080/02664763.2021.2004580OpenAlexW3217604959MaRDI QIDQ6107631

Unnamed Author, Jianbo Li, Ri-quan Zhang, Unnamed Author

Publication date: 3 July 2023

Published in: Journal of Applied Statistics (Search for Journal in Brave)

Full work available at URL: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10013526

zbMATH Keywords

missing datajump detectioninverse probability weighted methodjump-preserving curvelocal piecewise-linear kernel

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Inverse probability weighted estimation for general missing data problems
• Jump detection in time series nonparametric regression models: a polynomial spline approach
• Jump-preserving regression and smoothing using local linear fitting: a compromise
• Variable bandwidth and local linear regression smoothers
• Kernel-type estimators of jump points and values of a regression function
• Curve Fitting Under Jump and Peak Irregularities Using Local Linear Regression
• Smoothing with Split Linear Fits
• Nonparametric Estimation of Mean Functionals with Data Missing at Random
• A jump-preserving curve fitting procedure based on local piecewise-linear kernel estimation
• Empirical likelihood for linear regression models under imputation for missing responses
• INCOMPLETE DATA IN GENERALIZED LINEAR MODELS WITH CONTINUOUS COVARIATES
• Estimation of the number of jumps of the jump regression functions
• A Generalization of Sampling Without Replacement From a Finite Universe
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