Results 31 to 40 of about 574,217 (170)

Penalized Composite Likelihood Estimation for Spatial Generalized Linear Mixed Models [PDF]

Journal of Sciences, Islamic Republic of Iran
When discussing non-Gaussian spatially correlated variables, generalized linear mixed models have enough flexibility for modeling various data types. However, the maximum likelihood methods are plagued with substantial calculations for large data sets ...
Mohsen Mohammadzadeh, Leyla Salehi
doaj   +1 more source

Regularization for Generalized Additive Mixed Models by Likelihood-Based Boosting [PDF]

, 2011
With the emergence of semi- and nonparametric regression the generalized linear mixed model has been expanded to account for additive predictors. In the present paper an approach to variable selection is proposed that works for generalized additive mixed
Groll, Andreas, Tutz, Gerhard
core   +2 more sources

Simultaneous Inference in General Parametric Models [PDF]

, 2008
Simultaneous inference is a common problem in many areas of application. If multiple null hypotheses are tested simultaneously, the probability of rejecting erroneously at least one of them increases beyond the pre-specified significance level ...
Bates, Bates, Bretz, Bretz, Bullinger, Bönsch, Garcia, Genz, Genz, Genz, Hochberg, Hothorn, Hothorn, Hsu, Marcus, R Development Core Team, Rousseeuw, Salib, Searle, Serfling, Shaffer, Stefanski, Todorov, Tong, Westfall, Westfall, Westfall, Yohai, Zeileis, Zeileis   +29 more
core   +3 more sources

Extension of Nakagawa & Schielzeth's R2GLMM to random slopes models [PDF]

, 2014
1.Nakagawa & Schielzeth extended the widely used goodness-of-fit statistic R2 to apply to generalized linear mixed models (GLMMs). However, their R2GLMM method is restricted to models with the simplest random effects structure, known as random ...
Johnson, Paul C.D.
core   +2 more sources

Macro vs. Micro Methods in Non-Life Claims Reserving (an Econometric Perspective)

Risks, 2016
Traditionally, actuaries have used run-off triangles to estimate reserve (“macro” models, on aggregated data). However, it is possible to model payments related to individual claims. If those models provide similar estimations, we investigate uncertainty
Arthur Charpentier, Mathieu Pigeon
doaj   +1 more source

Using linear mixed model and dummy variable model approaches to construct compatible single-tree biomass equations at different scales - A case study for Masson pine in Southern China

Journal of Forest Science, 2012
The estimation of forest biomass is important for practical issues and scientific purposes in forestry. The estimation of forest biomass on a large-scale level would be merely possible with the application of generalized single-tree biomass models.
L.Y. Fu, W.S. Zeng, S.Z. Tang, R.P. Sharma, H.K. Li   +4 more
doaj   +1 more source

Varying coefficient models as Mixed Models : reparametrization methods and bayesian estimation [PDF]

, 2013
Non-linear relationships are accommodated in a regression model using smoothing functions. Interaction may occurs between continuous variable, in this case interaction between nonlinear and linear covariate leads to varying coefficent model (VCM), a ...
Freni Sterrantino, Anna
core   +1 more source

Half-Normal Plots and Overdispersed Models in R: The hnp Package

Journal of Statistical Software, 2017
Count and proportion data may present overdispersion, i.e., greater variability than expected by the Poisson and binomial models, respectively. Different extended generalized linear models that allow for overdispersion may be used to analyze this type of
Rafael A Moral, John Hinde, Clarice G B Demétrio   +2 more
doaj   +1 more source

Fast stable direct fitting and smoothness selection for Generalized Additive Models [PDF]

, 2007
Existing computationally efficient methods for penalized likelihood GAM fitting employ iterative smoothness selection on working linear models (or working mixed models).
Akaike H., Anderson E., Bowman A. W., Brezger A., Dennis J. E., Dongarra J. J., Fahrmeir L., Gill P. E., Golub G. H., Green P. J., Gu C, Gu C., Hastie T., Hastie T., McCullagh P., Parker R. L., Pinheiro J. C., R Development Core Team, Ramsay T., Ruppert D., Stone M., Wahba G., Wahba G., Watkins D. S., Wood S. N., Xiang D.   +25 more
core   +3 more sources

Generalized Linear Mixed Models: Part II

, 2021
As mentioned in Sect. 3.4, the likelihood function under a GLMM typically involves integrals with no analytic expressions. Such integrals may be difficult to evaluate, if the dimensions of the integrals are high. For relatively simple models, the likelihood function may be evaluated by numerical integration techniques.
Jiming Jiang, Thuan Nguyen
openaire   +1 more source