Results 41 to 50 of about 1,274,632 (180)
Bayesian inference for generalized linear models for spiking neurons
Frontiers in Computational Neuroscience, 2010 Generalized Linear Models (GLMs) are commonly used statistical methods for modelling the relationship between neural population activity and presented stimuli.
Sebastian Gerwinn +6 more
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Partially Linear Generalized Single Index Models for Functional Data (PLGSIMF)
Stats, 2021 Single-index models are potentially important tools for multivariate non-parametric regression analysis. They generalize linear regression models by replacing the linear combination α0⊤X with a non-parametric component η0α0⊤X, where η0(·) is an unknown ...
Mohamed Alahiane +3 more
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Design Issues for Generalized Linear Models: A Review
, 2006 Generalized linear models (GLMs) have been used quite effectively in the modeling of a mean response under nonstandard conditions, where discrete as well as continuous data distributions can be accommodated.
Ghosh, Malay +3 more
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Robust estimates in generalized partially linear models [PDF]
, 2006 In this paper, we introduce a family of robust estimates for the parametric and nonparametric components under a generalized partially linear model, where the data are modeled by $y_i|(\mathbf{x}_i,t_i)\sim F(\cdot,\mu_i)$ with $\mu_i=H(\eta(t_i)+\mathbf{
Boente, Graciela +2 more
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Model averaging for generalized linear models in fragmentary data prediction
Statistical Theory and Related Fields, 2022 Fragmentary data is becoming more and more popular in many areas which brings big challenges to researchers and data analysts. Most existing methods dealing with fragmentary data consider a continuous response while in many applications the response ...
Chaoxia Yuan, Yang Wu, Fang Fang
doaj +1 more source
Mixtures of g-priors in Generalized Linear Models
, 2018 Mixtures of Zellner's g-priors have been studied extensively in linear models and have been shown to have numerous desirable properties for Bayesian variable selection and model averaging. Several extensions of g-priors to Generalized Linear Models (GLMs)
Clyde, Merlise A., Li, Yingbo
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Spatial-temporal rainfall simulation using generalized linear models [PDF]
, 2005 We consider the problem of simulating sequences of daily rainfall at a network of sites in such a way as to reproduce a variety of properties realistically over a range of spatial scales.
Chandler, RE +3 more
core +1 more source
A Note on the Identifiability of Generalized Linear Mixed Models [PDF]
, 2014 I present here a simple proof that, under general regularity conditions, the standard parametrization of generalized linear mixed model is identifiable.
Labouriau, Rodrigo
core
, 2002 Abstract While this model is important for normally distributed data, it is less useful for other distributions such as the binomial, Poisson and gamma. The context in which such distributions are used often means that we need to model E [Y ] as a non-linear function of Xβ .
Paul Garthwaite +2 more
openaire +4 more sources
Partitioned conditional generalized linear models for categorical data [PDF]
, 2014 In categorical data analysis, several regression models have been proposed for hierarchically-structured response variables, e.g. the nested logit model. But they have been formally defined for only two or three levels in the hierarchy.
Guédon, Yann +2 more
core +4 more sources