Bivariate relationships incorporating method comparison: A review of linear regression methods

Bivariate relationships incorporating method comparison: A review of linear regression methods

Artículo de revisión.

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Main Authors: Dhanoa, M. S., Sanderson, R., López, Secundino, France, J.
Format: artículo biblioteca
Published: CABI Publishing 2016
Subjects:Measurement errors, Mean-square prediction error, Method comparison, Functional regression models, Concordance correlation,
Online Access:http://hdl.handle.net/10261/149093
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spelling dig-igm-es-10261-1490932021-03-18T16:23:21Z Bivariate relationships incorporating method comparison: A review of linear regression methods Dhanoa, M. S. Sanderson, R. López, Secundino France, J. Measurement errors Mean-square prediction error Method comparison Functional regression models Concordance correlation Artículo de revisión. In this review, we describe and illustrate the selection and use of some appropriate regression models for bivariate statistical relationships. The most commonly used method, ordinary least squares (OLS) or type I regression, may be inappropriate when the predictor variable is subject to measurement errors since this violates a fundamental assumption of OLS and as a result estimates of slope are likely to be biased or attenuated. The y-axis intercept will be biased too as it is a function of slope estimate and the means of y-and x-variables. This bias can have some undesirable consequences if OLS regression parameters and/or functions of them are used further with meaningful interpretations. For example, in animal energy balance studies, slope estimate represents efficiency of metabolizable energy utilization for body mass growth or milk production in dairy cows. The x-axis intercept, a function of y-intercept and slope, gives an estimate of the animal's body mass maintenance energy requirement. The choice of an alternative type II or functional regression model (e.g. maximum likelihood solution, major axis, reduced major axis and others) depends on the availability and ratio of measurement or precision variances of both y-and x-variables; otherwise non-parametric models (e.g. Theil-Sen non-parametric regression or Bartlett's three-group method) can be used. When the ratio of y-and x-variable error variances is not constant over the data range then the reiterated weighted functional model as described by Ripley and Thompson in 1987 may be necessary. Application of these models and other tests (e.g. mean-square prediction error, analysis of concordance) in analytical method comparisons is outlined. Data scrutiny and outlier diagnostics are included because outliers affect most of the non-robust statistics. Peer Reviewed 2017-05-04T10:15:31Z 2017-05-04T10:15:31Z 2016 2017-05-04T10:15:32Z artículo http://purl.org/coar/resource_type/c_6501 CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition and Natural Resources 11 (2016) 1749-8848 http://hdl.handle.net/10261/149093 10.1079/PAVSNNR11028 http://dx.doi.org/10.1079/PAVSNNR11028 Sí none CABI Publishing
institution IGM ES
collection DSpace
country España
countrycode ES
component Bibliográfico
access En linea
databasecode dig-igm-es
tag biblioteca
region Europa del Sur
libraryname Biblioteca del IGM España
topic Measurement errors
Mean-square prediction error
Method comparison
Functional regression models
Concordance correlation
Measurement errors
Mean-square prediction error
Method comparison
Functional regression models
Concordance correlation
spellingShingle Measurement errors
Mean-square prediction error
Method comparison
Functional regression models
Concordance correlation
Measurement errors
Mean-square prediction error
Method comparison
Functional regression models
Concordance correlation
Dhanoa, M. S.
Sanderson, R.
López, Secundino
France, J.
Bivariate relationships incorporating method comparison: A review of linear regression methods
description Artículo de revisión.
format artículo
topic_facet Measurement errors
Mean-square prediction error
Method comparison
Functional regression models
Concordance correlation
author Dhanoa, M. S.
Sanderson, R.
López, Secundino
France, J.
author_facet Dhanoa, M. S.
Sanderson, R.
López, Secundino
France, J.
author_sort Dhanoa, M. S.
title Bivariate relationships incorporating method comparison: A review of linear regression methods
title_short Bivariate relationships incorporating method comparison: A review of linear regression methods
title_full Bivariate relationships incorporating method comparison: A review of linear regression methods
title_fullStr Bivariate relationships incorporating method comparison: A review of linear regression methods
title_full_unstemmed Bivariate relationships incorporating method comparison: A review of linear regression methods
title_sort bivariate relationships incorporating method comparison: a review of linear regression methods
publisher CABI Publishing
publishDate 2016
url http://hdl.handle.net/10261/149093
work_keys_str_mv AT dhanoams bivariaterelationshipsincorporatingmethodcomparisonareviewoflinearregressionmethods
AT sandersonr bivariaterelationshipsincorporatingmethodcomparisonareviewoflinearregressionmethods
AT lopezsecundino bivariaterelationshipsincorporatingmethodcomparisonareviewoflinearregressionmethods
AT francej bivariaterelationshipsincorporatingmethodcomparisonareviewoflinearregressionmethods
_version_ 1777664004578082816

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