Results 271 to 280 of about 4,151,630 (320)
Some of the next articles are maybe not open access.
Parallel computing in linear mixed models
Computational Statistics, 2020zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Fulya Gokalp-Yavuz, Barret Schloerke
openaire +1 more source
Model comparison of generalized linear mixed models
Statistics in Medicine, 2005AbstractGeneralized linear mixed models (GLMMs) have been widely appreciated in biological and medical research. Maximum likelihood estimation has received a great deal of attention. Comparatively, not much has been done on model comparison or hypotheses testing.
Xin-Yuan, Song, Sik-Yum, Lee
openaire +2 more sources
2009
In the early 1950s, C.R. Henderson developed mixed model estimation, something he began in the 1940s with his Ph.D. thesis. He wanted to analyze data for a linear model with fixed environmental and random genetic factors in the breeding of swine (Van Vleck, 1998).
openaire +1 more source
In the early 1950s, C.R. Henderson developed mixed model estimation, something he began in the 1940s with his Ph.D. thesis. He wanted to analyze data for a linear model with fixed environmental and random genetic factors in the breeding of swine (Van Vleck, 1998).
openaire +1 more source
Testing transformations for the linear mixed model
Computational Statistics & Data Analysis, 2007zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Matthew J. Gurka +2 more
openaire +1 more source
2012
Fixed effects and random effects models are introduced with examples. Maximum likelihood (ML), restricted maximum likelihood (REML) and ANOVA methods of estimation of variance components are described, illustrating with the examples of one-way and two-way classification.
openaire +2 more sources
Fixed effects and random effects models are introduced with examples. Maximum likelihood (ML), restricted maximum likelihood (REML) and ANOVA methods of estimation of variance components are described, illustrating with the examples of one-way and two-way classification.
openaire +2 more sources
2013
The simplest form of the linear mixed model is the random-effects model, which represents data using the regression equation: $$\displaystyle{ \mathbf{y}_{i} =\boldsymbol{\alpha } +\mathbf{b}_{i} +\boldsymbol{\epsilon } _{i} (1 \leq i \leq m), }$$ where \(\boldsymbol{\alpha }\), y i , b i , and \(\boldsymbol{\epsilon }_{i}\) are column matrices ...
openaire +1 more source
The simplest form of the linear mixed model is the random-effects model, which represents data using the regression equation: $$\displaystyle{ \mathbf{y}_{i} =\boldsymbol{\alpha } +\mathbf{b}_{i} +\boldsymbol{\epsilon } _{i} (1 \leq i \leq m), }$$ where \(\boldsymbol{\alpha }\), y i , b i , and \(\boldsymbol{\epsilon }_{i}\) are column matrices ...
openaire +1 more source
Mixed Linear Model with Uncertain Paternity
Applied Statistics, 1992Summary: In animal breeding applications, mixed linear models are often used to estimate genetic parameters and to predict the breeding value of sires, under the assumption that paternity can be attributed without error. This paper considers a mixed linear model for situations in which paternity is uncertain.
openaire +2 more sources
Linear and generalized linear mixed models
2015AbstractGeneralized linear mixed models (GLMMs) are a powerful class of statistical models that combine the characteristics of generalized linear models and mixed models (models with both fixed and random predictor variables). This chapter: reviews the conceptual and theoretical background of GLMMs, focusing on the definition and meaning of random ...
openaire +1 more source
On Inverse Prediction in Mixed Linear Models
Communications in Statistics - Simulation and Computation, 2014Given training data, a model relating a multivariate response y to x, and y* from a mystery specimen, the objective is to infer what values x* might have given rise to y*. Two approaches are investigated and illustrated here. In one, inverse prediction, tenable values of x* are those at which y* does not test as an outlier.
openaire +1 more source
Generalized linear mixed models: a practical guide for ecology and evolution.
Trends in Ecology & Evolution, 2009Benjamin M. Bolker +6 more
semanticscholar +1 more source