Modeling Over-dispersed Binary Outcome Data

Name: Modeling Over-dispersed Binary Outcome Data
Brand: LAP Lambert Academic Publishing
SKU: 9783330067622
Price: 187.68 RON
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Autor: Babaniyi Olaniyi

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Editura: LAP Lambert Academic Publishing

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Many a time data admit more variability than expected under the assumed distribution. The greater va...

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Autor

Babaniyi Olaniyi

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2017

Pagini

EAN

9783330067622

ISBN

3330067624

Enbook ID

16268875

Editura

LAP Lambert Academic Publishing

Greutate

136

Dimensiuni

150 x 220 x 5

Descriere completă

Many a time data admit more variability than expected under the assumed distribution. The greater variability than predicted by the generalized linear model random component reflects overdispersion. Overdispersion occurs because the mean and variance components of a GLM are related and depends on the same parameter that is being predicted through the independent vector. In the context of logistic regression, overdispersion occurs when the discrepancies between the observed responses and their predicted values are larger than what the binomial model would predict. The problem of overdispersion may also be confounded with the problem of omitted covariates. If overdispersion is present in a data set, the estimated standard errors and test statistics the overall goodness-of-fit will be distorted and adjustments must be made and the interpretation of the model will be incorrect and any predictions will be too imprecise. In this book, we applied Quasilikelihood techniques (Scaling), William's procedure, Generalized Estimating Equation (GEE) to real-life datasets and proved it overcome the problem of overdispersion. We employed the free statistical software R version 3.1.

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Modeling Over-dispersed Binary Outcome Data

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