Residual Analysis for Linear Mixed Models
نویسندگان
چکیده
منابع مشابه
Residual analysis for linear mixed models.
Residuals are frequently used to evaluate the validity of the assumptions of statistical models and may also be employed as tools for model selection. For standard (normal) linear models, for example, residuals are used to verify homoscedasticity, linearity of effects, presence of outliers, normality and independence of the errors. Similar uses may be envisaged for three types of residuals that...
متن کاملAssessing Generalized Linear Mixed Models Using Residual Analysis
A nonparametric smoothing method for assessing the adequacy of generalized linear mixed models (GLMMs) is developed. The proposed method is based on smoothing the residuals over continuous covariates to avoid the partition of continuous covariates on model checking. The global test statistic has a quadratic form and its formulae of expectation as well as variance are derived. The sampling distr...
متن کاملBayesian Inference for Spatial Beta Generalized Linear Mixed Models
In some applications, the response variable assumes values in the unit interval. The standard linear regression model is not appropriate for modelling this type of data because the normality assumption is not met. Alternatively, the beta regression model has been introduced to analyze such observations. A beta distribution represents a flexible density family on (0, 1) interval that covers symm...
متن کاملPractical likelihood analysis for Spatial Generalized Linear Mixed Models
We propose a standard approach to make inference for spatial generalized linear mixed models using Laplace approximation. Based on analysis of two datasets previous analysed in literature, we compare our approach with different approaches. The first the rhizoctonia root rot dataset is an example of Binomial SGLMM and the second rongelap dataset is an example of Poisson or Negative Binomial SGLM...
متن کاملGeneralized Linear Mixed Models
Generalized linear models (GLMs) represent a class of fixed effects regression models for several types of dependent variables (i.e., continuous, dichotomous, counts). McCullagh and Nelder [32] describe these in great detail and indicate that the term ‘generalized linear model’ is due to Nelder and Wedderburn [35] who described how a collection of seemingly disparate statistical techniques coul...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Biometrical Journal
سال: 2007
ISSN: 0323-3847,1521-4036
DOI: 10.1002/bimj.200790008