Bottom-up estimation and top-down prediction: solar energy prediction combining information from multiple sources
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Publication:1728637
DOI10.1214/18-AOAS1145zbMath1411.62375OpenAlexW2794607217WikidataQ128989334 ScholiaQ128989334MaRDI QIDQ1728637
Siyuan Lu, Youngdeok Hwang, Jae Kwang Kim
Publication date: 25 February 2019
Published in: The Annals of Applied Statistics (Search for Journal in Brave)
Full work available at URL: https://projecteuclid.org/euclid.aoas/1542078038
Inference from stochastic processes and prediction (62M20) Applications of statistics in engineering and industry; control charts (62P30) Applications of statistics to physics (62P35)
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Cites Work
- Parametric fractional imputation for missing data analysis
- A statistical modeling approach for air quality data based on physical dispersion processes and its application to ozone modeling
- Calibrating a large computer experiment simulating radiative shock hydrodynamics
- Parametric bootstrap approximation to the distribution of EBLUP and related prediction intervals in linear mixed models
- The design and analysis of computer experiments.
- A split-and-conquer approach for analysis of
- A practical approach to spatio-temporal analysis
- On Parametric Bootstrap Methods for Small Area Prediction
- Multilevel mixed linear model analysis using iterative generalized least squares
- Post-Fisherian Experimentation: From Physical to Virtual
- A Frequentist Approach to Computer Model Calibration
- Modularization in Bayesian analysis, with emphasis on analysis of computer models
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