NDLI: Estimability analysis of variance and covariance components

Content Provider	Springer Nature Link
Author	Xu, Peiliang Liu, Yumei Shen, Yunzhong Fukuda, Yoichi
Copyright Year	2006
Abstract	Although variance and covariance components have been extensively investigated and a number of elegant formulae to compute them have been derived, nothing is known, without any ambiguity, about their estimability in the case of a fully unknown variance–covariance matrix. We prove that variance and covariance components in this case are not estimable, thus clarifying the ambiguity of the literature on the topic and correcting some erroneous statements in the literature. We also give a new theorem on the estimability of a linear function of variance and covariance components. Then we propose a new method to estimate the variance–covariance matrix with special structure, which can presumably be represented by, at most, r(r + 1)/2 independent parameters to guarantee its estimability in such a subspace, by directly implementing the positive definiteness of the matrix as constraint to the restricted maximum likelihood method, where r is the number of redundant measurements. Therefore, our estimates of the variance and covariance components always reconstruct a positive definite matrix and are always physically meaningful.
Starting Page	593
Ending Page	602
Page Count	10
File Format	PDF
ISSN	09497714
Journal	Journal of Geodesy
Volume Number	81
Issue Number	9
e-ISSN	14321394
Language	English
Publisher	Springer-Verlag
Publisher Date	2006-12-12
Publisher Place	Berlin, Heidelberg
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Estimability Variance and covariance components Restricted maximum likelihood Math. Applications in Geosciences Geophysics/Geodesy
Content Type	Text
Resource Type	Article
Subject	Geophysics Geochemistry and Petrology Computers in Earth Sciences

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Variance components in errors-in-variables models: estimability, stability and bias analysis

Generalized maximum-likelihood estimation of variance–covariance components with non-informative prior

Error Analysis of Weekly Station Coordinates in the DORIS Network

Least-squares variance component estimation

Compactly supported radial covariance functions

Least-squares collocation with covariance-matching constraints

Generalized maximum-likelihood estimation of variance components with inverted gamma prior

Geodetic intersection on the ellipsoid

Geodesy and relativity

Estimability analysis of variance and covariance components

Similar Documents

Variance components in errors-in-variables models: estimability, stability and bias analysis

Generalized maximum-likelihood estimation of variance–covariance components with non-informative prior

Error Analysis of Weekly Station Coordinates in the DORIS Network

Least-squares variance component estimation

Compactly supported radial covariance functions

Least-squares collocation with covariance-matching constraints

Generalized maximum-likelihood estimation of variance components with inverted gamma prior

Geodetic intersection on the ellipsoid

Geodesy and relativity

Estimability analysis of variance and covariance components