Results 31 to 40 of about 328,528 (303)
Model Selection in Generalized Linear Models
Symmetry, 2023 The problem of model selection in regression analysis through the use of forward selection, backward elimination, and stepwise selection has been well explored in the literature. The main assumption in this, of course, is that the data are normally distributed and the main tool used here is either a t test or an F test. However, the properties of these
Abdulla Mamun, Sudhir Paul
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Albatross analytics a hands-on into practice: statistical and data science application
Journal of Big Data, 2022 Albatross Analytics is a statistical and data science data processing platform that researchers can use in disciplines of various fields. Albatross Analytics makes it easy to implement fundamental analysis for various regressions with random model ...
Rezzy Eko Caraka +7 more
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Generalized Linear Spatial Models to Predict Slate Exploitability
Journal of Applied Mathematics, 2013 The aim of this research was to determine the variables that characterize slate exploitability and to model spatial distribution. A generalized linear spatial model (GLSMs) was fitted in order to explore relationship between exploitability and different ...
Angeles Saavedra +3 more
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Comparison of predictor approaches for longitudinal binary outcomes: application to anesthesiology data [PDF]
PeerJ, 2014 Longitudinal data with binary repeated responses are now widespread among clinical studies and standard statistical analysis methods have become inadequate in the answering of clinical hypotheses.
Anil Aktas Samur +2 more
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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.
Yang, C +7 more
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Efficient estimation of generalized linear latent variable models.
PLoS ONE, 2019 Generalized linear latent variable models (GLLVM) are popular tools for modeling multivariate, correlated responses. Such data are often encountered, for instance, in ecological studies, where presence-absences, counts, or biomass of interacting species ...
Jenni Niku +5 more
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Modelling Background Noise in Finite Mixtures of Generalized Linear Regression Models [PDF]
, 2008 In this paper we show how only a few outliers can completely break down EM-estimation of mixtures of regression models. A simple, yet very effective way of dealing with this problem, is to use a component where all regression parameters are fixed to zero
Leisch, Friedrich, Brito, Paula
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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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Designs for generalized linear models with several variables and model uncertainty [PDF]
, 2006 Standard factorial designs may sometimes be inadequate for experiments that aim to estimate a generalized linear model, for example, for describing a binary response in terms of several variables.
D. C. Woods +11 more
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Therapeutic Apheresis and Dialysis, EarlyView.ABSTRACT Background Chronic micro‐inflammation in patients with end‐stage renal disease (ESRD) is a significant driver of cardiovascular complications and diminished quality of life. While standard hemodialysis (SHD) effectively manages small‐molecule clearance, its ability to remove medium‐to‐large uremic toxins—the primary catalysts of systemic ...
Hongwei Zuo +5 more
wiley +1 more source