Hierarchical integrated spatial process modeling of monotone West Antarctic snow density curves

DOI10.1214/21-AOAS1443zbMath1477.62326arXiv2001.05520MaRDI QIDQ2245128

Durban G. Keeler, Summer Rupper, Philip A. White

Publication date: 15 November 2021

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

Full work available at URL: https://arxiv.org/abs/2001.05520

zbMATH Keywords

spatial statisticsGaussian processsplineBayesian statisticsmonotonic regression

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Numerical computation using splines (65D07) Gaussian processes (60G15) Applications of statistics to environmental and related topics (62P12) Meteorology and atmospheric physics (86A10)

Related Items (1)

Spatial functional data modeling of plant reflectances

Uses Software

• Stan

Cites Work

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• Inference using shape-restricted regression splines
• Exact mean integrated squared error
• Bayesian monotone regression using Gaussian process projection
• Bayesian Isotonic Regression and Trend Analysis
• Kernel and Probit Estimates in Quantal Bioassay
• Nonparametric Bayesian bioassay including ordered polytomous response
• Some Errors Associated with the Non-parametric Estimation of Density Functions
• Scoring Rules for Continuous Probability Distributions
• Spatial Modeling With Spatially Varying Coefficient Processes
• Simultaneous probability statements for Bayesian P-splines
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Tools for statistical inference. Methods for the exploration of posterior distributions and likelihood functions.

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