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Titlebook: Generalized Linear Mixed Models with Applications in Agriculture and Biology; Josafhat Salinas Ruíz,Osval Antonio Montesinos Lóp Book‘‘‘‘‘

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發(fā)表于 2025-3-21 17:16:06 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Generalized Linear Mixed Models with Applications in Agriculture and Biology
編輯Josafhat Salinas Ruíz,Osval Antonio Montesinos Lóp
視頻videohttp://file.papertrans.cn/383/382221/382221.mp4
概述Generalized Linear Models are an alternative statistical solution to no normal distribution of response variables.In agriculture and biology several responses variable are no continuous and no normall
圖書封面Titlebook: Generalized Linear Mixed Models with Applications in Agriculture and Biology;  Josafhat Salinas Ruíz,Osval Antonio Montesinos Lóp Book‘‘‘‘‘
描述.This 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 varia
出版日期Book‘‘‘‘‘‘‘‘ 2023
關鍵詞Generalized Linear Mixed Models; non normal distribution; GLM; GLMM; Model Inference; non normal response
版次1
doihttps://doi.org/10.1007/978-3-031-32800-8
isbn_softcover978-3-031-32802-2
isbn_ebook978-3-031-32800-8
copyrightThe Editor(s) (if applicable) and The Author(s) 2023
The information of publication is updating

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Dennis Chiwele,Christopher Colcloughthe values of all the predictor variables, and are linear functions of the predictor variables. Transformations of data are used to try to force the data into a normal linear regression model or to find a non-normal-type response variable transformation (discrete, categorical, positive continuous sc
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Conflict and Conciliation Dynamics,e increased use of such sophisticated statistical tools with broader applicability and flexibility. This family of models can be applied to a wide range of different data types (continuous, categorical (nominal or ordinal), percentages, and counts), and each is appropriate for a specific type of dat
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https://doi.org/10.1007/978-3-658-39942-9ds in agricultural or agroecological studies; the number of plants transformed or regenerated using modern breeding techniques; the number of individuals with a certain disease in a medical study; and the number of defective products in a quality improvement study, among others. These counts can be
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