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A bootstrap method for estimating bias and variance in statistical fisheries modelling frameworks using highly disparate datasets ArchiMer
Elvarsson, B. P.; Taylor, L.; Trenkel, Verena; Kupca, V.; Stefansson, G..
Statistical models of marine ecosystems use a variety of data sources to estimate parameters using composite or weighted likelihood functions with associated weighting issues and questions on how to obtain variance estimates. Regardless of the method used to obtain point estimates, a method is required for variance estimation. A bootstrap technique is introduced for the evaluation of uncertainty in such models, taking into account inherent spatial and temporal correlations in the datasets, which are commonly transferred as assumptions from a likelihood estimation procedure into Hessian-based variance estimation procedures. The technique is demonstrated on a real dataset and the effects of the number of bootstrap samples on estimation bias and variance...
Tipo: Text Palavras-chave: Bootstrapping; Correlated data; Fish population dynamics; Non-linear models.
Ano: 2014 URL: http://archimer.ifremer.fr/doc/00193/30459/29913.pdf
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Spatial statistical analysis and selection of genotypes in plant breeding PAB
Duarte,João Batista; Vencovsky,Roland.
The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes...
Tipo: Info:eu-repo/semantics/article Palavras-chave: Augmented design; Mixed model; Information recovery; Autocorrelation; Correlated data; Geostatistics.
Ano: 2005 URL: http://www.scielo.br/scielo.php?script=sci_arttext&pid=S0100-204X2005000200002
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Improved generalized estimating equation analysis via xtqls for quasi–least squares in Stata AgEcon
Shults, Justine; Ratcliffe, Sarah J.; Leonard, Mary.
Quasi–least squares (QLS) is an alternative method for estimating the correlation parameters within the framework of the generalized estimating equation (GEE) approach for analyzing correlated cross-sectional and longitudinal data. This article summarizes the development of QLS that occurred in several reports and describes its use with the user-written program xtqls in Stata. Also, it demonstrates the following advantages of QLS: (1) QLS allows some correlation structures that have not yet been implemented in the framework of GEE, (2) QLS can be applied as an alternative to GEE if the GEE estimate is infeasible, and (3) QLS uses the same estimating equation for estimation of Β as GEE; as a result, QLS can involve programs already available for GEE. In...
Tipo: Article Palavras-chave: Xtqls; Correlated data; Clustered data; Longitudinal data; Generalized estimating equations; Quasi–least squares; Research Methods/ Statistical Methods.
Ano: 2007 URL: http://purl.umn.edu/119265
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