The use of univariate Bayes regression models for spatial smoothing

DOI10.1016/S0167-9473(96)00069-2zbMath0900.62143OpenAlexW2077545572MaRDI QIDQ1391804

Saeko Kusanobu, Takemi Yanagimoto, Ryuei Nishii

Publication date: 23 July 1998

Published in: Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-9473(96)00069-2

zbMATH Keywords

Difference matrixLandsat imagesReduction of stripe noiseSmoothness prior

Mathematics Subject Classification ID

Bayesian inference (62F15) Survival analysis and censored data (62N99) Probabilistic methods, stochastic differential equations (65C99)

Cites Work

• A comparison of GCV and GML for choosing the smoothing parameter in the generalized spline smoothing problem
• Smoothing noisy data with spline functions: Estimating the correct degree of smoothing by the method of generalized cross-validation
• On use of the Kalman filter for spatial smoothing
• Smoothing by spline functions.
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• The Use of Marginal Likelihood for a Diagnostic Test for the Goodness of Fit of the Simple Linear Regression Model
• Robust Locally Weighted Regression and Smoothing Scatterplots
• A Correspondence Between Bayesian Estimation on Stochastic Processes and Smoothing by Splines
• Unnamed Item
• Unnamed Item
• Unnamed Item

This page was built for publication: The use of univariate Bayes regression models for spatial smoothing