LEADER 04656nam 22006855 450 001 9910853993903321 005 20240521114943.0 010 $a3-031-32800-0 010 $a9783031328008 024 7 $a10.1007/978-3-031-32800-8 035 $a(CKB)5700000000428112 035 $a(MiAaPQ)EBC30702995 035 $a(Au-PeEL)EBL30702995 035 $a(DE-He213)978-3-031-32800-8 035 $a(PPN)27227321X 035 $a(OCoLC)1395078576 035 $a(EXLCZ)995700000000428112 100 $a20230816d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aGeneralized Linear Mixed Models with Applications in Agriculture and Biology$b[electronic resource] /$fby Josafhat Salinas Ruíz, Osval Antonio Montesinos López, Gabriela Hernández Ramírez, Jose Crossa Hiriart 205 $a1st ed. 2023. 210 $cSpringer International Publishing$d2023 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (434 pages) 311 $a3-031-32799-3 311 $a9783031327995 327 $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. 330 $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. 606 $aBiometry 606 $aMultivariate analysis 606 $aRegression analysis 606 $aAgriculture 606 $aBiostatistics 606 $aMultivariate Analysis 606 $aLinear Models and Regression 606 $aAgriculture 615 0$aBiometry. 615 0$aMultivariate analysis. 615 0$aRegression analysis. 615 0$aAgriculture. 615 14$aBiostatistics. 615 24$aMultivariate Analysis. 615 24$aLinear Models and Regression. 615 24$aAgriculture. 676 $a570.15195 700 $aSalinas Ruíz$b Josafhat$01736974 701 $aMontesinos López$b Osval Antonio$01078558 701 $aHernández Ramírez$b Gabriela$01736975 701 $aCrossa Hiriart$b Jose$01736976 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910853993903321 996 $aGeneralized Linear Mixed Models with Applications in Agriculture and Biology$94157897 997 $aUNINA