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001 978-3-031-32800-8
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008 230816s2023 sz | s |||| 0|eng d
020 _a9783031328008
_9978-3-031-32800-8
024 7 _a10.1007/978-3-031-32800-8
_2doi
050 4 _aQH323.5
072 7 _aPBT
_2bicssc
072 7 _aMED090000
_2bisacsh
072 7 _aPBT
_2thema
082 0 4 _a570.15195
_223
100 1 _aSalinas Ruíz, Josafhat.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aGeneralized Linear Mixed Models with Applications in Agriculture and Biology
_h[electronic resource] /
_cby Josafhat Salinas Ruíz, Osval Antonio Montesinos López, Gabriela Hernández Ramírez, Jose Crossa Hiriart.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aXIII, 427 p. 48 illus., 5 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aChapter 1) Elements of the Generalized Linear Mixed Models -- Chapter 2) Generalized Linear Models -- Chapter 3) Objectives in Model Inference -- Chapter 4) Generalized Linear Mixed Models for non-normal responses -- Chapter 5) Generalized Linear Mixed Models for Count response -- Chapter 6) Generalized Linear Mixed Models for Proportions and Percentages response -- Chapter 7) Times of occurrence of an event of interest -- Chapter 8) Generalized Linear Mixed Models for Categorial and Ordinal responses -- Chapter 9) Generalized Linear Mixed Models for Repeated Measurements.
506 0 _aOpen Access
520 _aThis open access book offers an introduction to mixed generalized linear models with applications to the biological sciences, basically approached from an applications perspective, without neglecting the rigor of the theory. For this reason, the theory that supports each of the studied methods is addressed and later - through examples - its application is illustrated. In addition, some of the assumptions and shortcomings of linear statistical models in general are also discussed. An alternative to analyse non-normal distributed response variables is the use of generalized linear models (GLM) to describe the response data with an exponential family distribution that perfectly fits the real response. Extending this idea to models with random effects allows the use of Generalized Linear Mixed Models (GLMMs). The use of these complex models was not computationally feasible until the recent past, when computational advances and improvements to statistical analysis programs allowed users to easily, quickly, and accurately apply GLMM to data sets. GLMMs have attracted considerable attention in recent years. The word "Generalized" refers to non-normal distributions for the response variable and the word "Mixed" refers to random effects, in addition to the fixed effects typical of analysis of variance (or regression). With the development of modern statistical packages such as Statistical Analysis System (SAS), R, ASReml, among others, a wide variety of statistical analyzes are available to a wider audience. However, to be able to handle and master more sophisticated models requires proper training and great responsibility on the part of the practitioner to understand how these advanced tools work. GMLM is an analysis methodology used in agriculture and biology that can accommodate complex correlation structures and types of response variables.
650 0 _aBiometry.
650 0 _aMultivariate analysis.
650 0 _aRegression analysis.
650 0 _aAgriculture.
650 1 4 _aBiostatistics.
650 2 4 _aMultivariate Analysis.
650 2 4 _aLinear Models and Regression.
650 2 4 _aAgriculture.
700 1 _aMontesinos López, Osval Antonio.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aHernández Ramírez, Gabriela.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
700 1 _aCrossa Hiriart, Jose.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031327995
776 0 8 _iPrinted edition:
_z9783031328015
776 0 8 _iPrinted edition:
_z9783031328022
856 4 0 _uhttps://doi.org/10.1007/978-3-031-32800-8
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
912 _aZDB-2-SOB
999 _c37776
_d37776