Spatial sampling design based on stochastic complexity.
type: Item
instance of: scholarly article
title: Spatial sampling design based on stochastic complexity.
zbMATH Open document ID: 1070.62500
author: Yanyan Li
published in: Journal of Multivariate Analysis
publication date: 2000-02-09T00:00:00Z
Mathematics Subject Classification ID: 62B10
Mathematics Subject Classification ID: 60G35
Mathematics Subject Classification ID: 62M30
zbMATH DE Number: 1369821
zbMATH Keywords: M algorithms
zbMATH Keywords: incomplete data
zbMATH Keywords: minimum description length
zbMATH Keywords: network design
zbMATH Keywords: stochastic complexity
MaRDI profile type: Publication
OpenAlex ID: W2164995005
cites work: Estimation and smoothing from incomplete data for a class of lattice processes
cites work: Application of em-type algorithms to spatial data
cites work: Stochastic complexity and model selection from incomplete data
cites work: Optimal monitoring network designs
cites work: Q4023085
cites work: Q4002793
cites work: Q4139463
cites work: Q4850604
cites work: On the equivalence of kriging and maximum entropy estimators
cites work: Q3816821
cites work: Q4850610
cites work: A subclass of lattice processes applied to a problem in planar sampling
cites work: Some notes on Rissanen's stochastic complexity
cites work: A universal prior for integers and estimation by minimum description length
cites work: Q3995624
cites work: Fisher information and stochastic complexity
DOI: 10.1006/JMVA.1999.1834
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description: scientific article; zbMATH DE number 1369821