Linear Discriminant Analysis of Multivariate Spatial-Temporal Regressions

We consider classification of the realization of a multivariate spatial-temporal Gaussian random field into one of two populations with different regression mean models and factorized covariance matrices. Unknown means and common feature vector covariance matrix are estimated from training samples w...

Full description

Saved in:
Bibliographic Details
Published in:Scandinavian journal of statistics 2005-06, Vol.32 (2), p.281-294
Main Authors: SALTYTE-BENTH, JURATE, DUCINSKAS, KESTUTIS
Format: Article
Language:English
Subjects:
asymptotic expansion
Bayes classification rule
Correlations
Covariance matrices
Data analysis
Discriminant analysis
error rate
Error rates
Estimators
Exact sciences and technology
Linear discriminant analysis
Linear inference, regression
Linear regression
Mathematical independent variables
Mathematical vectors
Mathematics
Multivariate analysis
Probability and statistics
Probability theory and stochastic processes
Regression analysis
Sciences and techniques of general use
Spatial models
spatial-temporal
Statistics
Stochastic processes
Studies
Time series
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:We consider classification of the realization of a multivariate spatial-temporal Gaussian random field into one of two populations with different regression mean models and factorized covariance matrices. Unknown means and common feature vector covariance matrix are estimated from training samples with observations correlated in space and time, assuming spatial-temporal correlations to be known. We present the first-order asymptotic expansion of the expected error rate associated with a linear plug-in discriminant function. Our results are applied to ecological data collected from the Lithuanian Economic Zone in the Baltic Sea.
ISSN:0303-6898
1467-9469
DOI:10.1111/j.1467-9469.2005.00421.x