Results 281 to 290 of about 7,333,462 (342)
Some of the next articles are maybe not open access.
Related searches:
Related searches:
WIREs Computational Statistics, 2011
AbstractThis article describes log‐linear models as special cases of generalized linear models. Specifically, log‐linear models use a logarithmic link function. Log‐linear models are used to examine joint distributions of categorical variables, dependency relations, and association patterns.
Von Eye, Alexander +2 more
openaire +1 more source
AbstractThis article describes log‐linear models as special cases of generalized linear models. Specifically, log‐linear models use a logarithmic link function. Log‐linear models are used to examine joint distributions of categorical variables, dependency relations, and association patterns.
Von Eye, Alexander +2 more
openaire +1 more source
Physical Review E, 2003
We study the time-dependent and the stationary properties of the linear Glauber model in a d-dimensional hypercubic lattice. This model is equivalent to the voter model with noise. By using the Green function method, we get exact results for the two-point correlations from which the critical behavior is obtained.
openaire +2 more sources
We study the time-dependent and the stationary properties of the linear Glauber model in a d-dimensional hypercubic lattice. This model is equivalent to the voter model with noise. By using the Green function method, we get exact results for the two-point correlations from which the critical behavior is obtained.
openaire +2 more sources
2014
Chapter Preview . We give a general discussion of linear mixed models and continue by illustrating specific actuarial applications of this type of model. Technical details on linear mixed models follow: model assumptions, specifications, estimation techniques, and methods of inference.
Antonio, K., Zhang, Y.
openaire +2 more sources
Chapter Preview . We give a general discussion of linear mixed models and continue by illustrating specific actuarial applications of this type of model. Technical details on linear mixed models follow: model assumptions, specifications, estimation techniques, and methods of inference.
Antonio, K., Zhang, Y.
openaire +2 more sources
2007
This chapter presents the general linear model as an extension to the two-sample t-test, analysis of variance (ANOVA), and linear regression. We illustrate the general linear model using two-way ANOVA as a prime example. The underlying principle of ANOVA, which is based on the decomposition of the value of an observed variable into grand mean, group ...
openaire +2 more sources
This chapter presents the general linear model as an extension to the two-sample t-test, analysis of variance (ANOVA), and linear regression. We illustrate the general linear model using two-way ANOVA as a prime example. The underlying principle of ANOVA, which is based on the decomposition of the value of an observed variable into grand mean, group ...
openaire +2 more sources
2008
A partially linear model requires the regression function to be a linear function of a subset of the variables and a nonparametric non-specified function of the rest of the variables. Suppose, for example, that one is interested in estimating the relationship between an outcome variable of interest y and a vector of variables (x, z).
openaire +1 more source
A partially linear model requires the regression function to be a linear function of a subset of the variables and a nonparametric non-specified function of the rest of the variables. Suppose, for example, that one is interested in estimating the relationship between an outcome variable of interest y and a vector of variables (x, z).
openaire +1 more source
American Journal of Orthodontics and Dentofacial Orthopedics, 2023
Tomasz Burzykowski +3 more
openaire +2 more sources
Tomasz Burzykowski +3 more
openaire +2 more sources
(Non) linear regression modeling [PDF]
, 2004 We will study causal relationships of a known form between random variables. Given a model, we distinguish one or more dependent (endogenous) variables Y = (Y1,…,Yl), l ∈ N, which are explained by a model, and independent (exogenous, explanatory) variables X = (X1,…,Xp),p ∈ N, which explain or predict the dependent variables by means of the model. Such
openaire +3 more sources