Results 71 to 80 of about 1,274,632 (180)

dglars: An R Package to Estimate Sparse Generalized Linear Models

Journal of Statistical Software, 2014
dglars is a publicly available R package that implements the method proposed in Augugliaro, Mineo, and Wit (2013), developed to study the sparse structure of a generalized linear model.
Luigi Augugliaro, Angelo Mineo, Ernst C. Wit   +2 more
doaj   +1 more source

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 Paths for Generalized Linear Models via Coordinate Descent

Journal of Statistical Software, 2010
We develop fast algorithms for estimation of generalized linear models with convex penalties. The models include linear regression, two-class logistic regression, and multi- nomial regression problems while the penalties include ℓ1 (the lasso), ℓ2 (ridge
Jerome Friedman, Trevor Hastie, Rob Tibshirani   +2 more
doaj  

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

The Empirical Cressie-Read Test Statistics for Longitudinal Generalized Linear Models

Journal of Applied Mathematics, 2014
For the marginal longitudinal generalized linear models (GLMs), we develop the empirical Cressie-Read (ECR) test statistic approach which has been proposed for the independent identically distributed (i.i.d.) case.
Junhua Zhang, Ruiqin Tian, Suigen Yang, Sanying Feng   +3 more
doaj   +1 more source

Sensory analysis of Prato cheeses by generalized linear mixed models

Revista do Instituto de Latícinios Cândido Tostes
Sensory analysis, an area of Food Science, is used to analyze and measure characteristics of foods, being able to evaluate the acceptance of samples. Such assessments can be performed using the 9-point numerical hedonic scale, classified as an ordinal ...
Tatiane Carvalho Alvarenga, Jéssica Ferreira Rodrigues   +1 more
doaj   +1 more source

Online inference in high-dimensional generalized linear models with streaming data. [PDF]

Electron J Stat, 2023
Luo L, Han R, Lin Y, Huang J.
europepmc   +1 more source

Variance estimation for the instrumental variables approach to measurement error in generalized linear models [PDF]

This paper derives and gives explicit formulas for a derived sandwich variance estimate. This variance estimate is appropriate for generalized linear additive measurement error models fitted using instrumental variables.
James W. Hardin, Raymond J. Carroll
core   +1 more source

Neighborhood-level heterogeneity in childhood morbidity through generalized linear mixed models

Frontiers in Public Health
ObjectiveChildhood morbidities are crucial for improving long-term public health outcomes. This study aimed to examine the existence of child-specific and regional variation in childhood morbidity based on the cross-cutting study of the Performance ...
Endeshaw A. Derso, Endeshaw A. Derso, Endeshaw A. Derso, Kassahun A. Gelaye, Maria G. Campolo, Amare T. Woldemariam, Amare T. Woldemariam, Amare T. Woldemariam, Angela Alibrandi   +8 more
doaj   +1 more source

Comments on "A Scale-Free Approach for False Discovery Rate Control in Generalized Linear Models". [PDF]

J Am Stat Assoc, 2023
Li S, Yao Y, Zhang CH.
europepmc   +1 more source