Least-squares variance component estimation
Least-squares variance component estimation (LS-VCE) is a simple, flexible and attractive method for the estimation of unknown variance and covariance components. LS-VCE is simple because it is based on the well-known principle of LS; it is flexible because it works with a user-defined weight matrix...
| Main Authors: | , |
|---|---|
| Format: | Journal Article |
| Published: |
Springer - Verlag
2008
|
| Subjects: | |
| Online Access: | http://hdl.handle.net/20.500.11937/11278 |
| _version_ | 1848747762884018176 |
|---|---|
| author | Teunissen, Peter Amiri-Simkooei, A. |
| author_facet | Teunissen, Peter Amiri-Simkooei, A. |
| author_sort | Teunissen, Peter |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Least-squares variance component estimation (LS-VCE) is a simple, flexible and attractive method for the estimation of unknown variance and covariance components. LS-VCE is simple because it is based on the well-known principle of LS; it is flexible because it works with a user-defined weight matrix; and it is attractive because it allows one to directly apply the existing body of knowledge of LS theory. In this contribution, we present the LS-VCE method for different scenarios and explore its various properties. The method is described for three classes of weight matrices: a general weight matrix, a weight matrix from the unit weight matrix class; and a weight matrix derived from the class of elliptically contoured distributions. We also compare the LS-VCE method with some of the existing VCE methods. Some of them are shown to be special cases of LS-VCE. We also show how the existing body of knowledge of LS theory can be used to one’s advantage for studying various aspects of VCE, such as the precision and estimability of VCE, the use of a-priori variance component information, and the problem of nonlinear VCE. Finally, we show how the mean and the variance of the fixed effect estimator of the linear model are affected by the results of LS-VCE. Various examples are given to illustrate the theory. |
| first_indexed | 2025-11-14T06:54:18Z |
| format | Journal Article |
| id | curtin-20.500.11937-11278 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T06:54:18Z |
| publishDate | 2008 |
| publisher | Springer - Verlag |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-112782017-09-13T15:54:08Z Least-squares variance component estimation Teunissen, Peter Amiri-Simkooei, A. Least-squares variance component estimation (LS-VCE) - Elliptically contoured distribution - Best linear unbiased estimator (BLUE) - Best invariant quadratic unbiased estimator (BIQUE) - Minimum norm quadratic unbiased estimator (MINQUE) - Restricted maximum likelihood estimator (REML) Least-squares variance component estimation (LS-VCE) is a simple, flexible and attractive method for the estimation of unknown variance and covariance components. LS-VCE is simple because it is based on the well-known principle of LS; it is flexible because it works with a user-defined weight matrix; and it is attractive because it allows one to directly apply the existing body of knowledge of LS theory. In this contribution, we present the LS-VCE method for different scenarios and explore its various properties. The method is described for three classes of weight matrices: a general weight matrix, a weight matrix from the unit weight matrix class; and a weight matrix derived from the class of elliptically contoured distributions. We also compare the LS-VCE method with some of the existing VCE methods. Some of them are shown to be special cases of LS-VCE. We also show how the existing body of knowledge of LS theory can be used to one’s advantage for studying various aspects of VCE, such as the precision and estimability of VCE, the use of a-priori variance component information, and the problem of nonlinear VCE. Finally, we show how the mean and the variance of the fixed effect estimator of the linear model are affected by the results of LS-VCE. Various examples are given to illustrate the theory. 2008 Journal Article http://hdl.handle.net/20.500.11937/11278 10.1007/s00190-007-0157-x Springer - Verlag fulltext |
| spellingShingle | Least-squares variance component estimation (LS-VCE) - Elliptically contoured distribution - Best linear unbiased estimator (BLUE) - Best invariant quadratic unbiased estimator (BIQUE) - Minimum norm quadratic unbiased estimator (MINQUE) - Restricted maximum likelihood estimator (REML) Teunissen, Peter Amiri-Simkooei, A. Least-squares variance component estimation |
| title | Least-squares variance component estimation |
| title_full | Least-squares variance component estimation |
| title_fullStr | Least-squares variance component estimation |
| title_full_unstemmed | Least-squares variance component estimation |
| title_short | Least-squares variance component estimation |
| title_sort | least-squares variance component estimation |
| topic | Least-squares variance component estimation (LS-VCE) - Elliptically contoured distribution - Best linear unbiased estimator (BLUE) - Best invariant quadratic unbiased estimator (BIQUE) - Minimum norm quadratic unbiased estimator (MINQUE) - Restricted maximum likelihood estimator (REML) |
| url | http://hdl.handle.net/20.500.11937/11278 |