Partially linear models and their applications to change point detection of chemical process data

DOI10.1080/02664763.2016.1247788OpenAlexW2538124215MaRDI QIDQ5138693

Camila Borelli Zeller, Aparecida M. S. Mimura, Júlio C. J. Silva, Clécio S. Ferreira

Publication date: 4 December 2020

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

Full work available at URL: https://doi.org/10.1080/02664763.2016.1247788

zbMATH Keywords

generalized cross validationpartially linear modelschange pointback-fittingSchwarz information criterionchemical methods

Mathematics Subject Classification ID

Statistics (62-XX)

Uses Software

• R
• bootlib

Cites Work

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• Testing for change points in partially linear models
• Detection of a change-point in Student-\(t\) linear regression models
• Local influence for Student-\(t\) partially linear models
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• Estimating the dimension of a model
• Bootstrap methods for standard errors, confidence intervals, and other measures of statistical accuracy. With a comment by J. A. Hartigan and a rejoinder by the authors
• A comparison of estimators for regression models with change points
• Semiparametric additive models under symmetric distributions
• Parametric Statistical Change Point Analysis
• Penalized Likelihood for General Semi-Parametric Regression Models
• Testing for a change point in linear regression models
• ON CHANGE POINT DETECTION AND ESTIMATION
• Aggregated functional data model for near-infrared spectroscopy calibration and prediction

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