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Title of Journal: Stat Comput

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Abbravation: Statistics and Computing

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Springer US

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DOI

10.1007/bf02994286

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ISSN

1573-1375

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Deletion diagnostics for marginal mean and correla

Authors: John S Preisser Jamie Perin
Publish Date: 2007/08/03
Volume: 17, Issue: 4, Pages: 381-393
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Abstract

Regression diagnostics are introduced for parameters in marginal association models for clustered binary outcomes in an implementation of generalized estimating equations Estimating equations for intracluster correlations facilitate computational formulae for onestep deletion diagnostics in an extension of earlier work on diagnostics for parameters in the marginal mean model The proposed diagnostics measure the influence of an observation or a cluster of observations on the estimated regression parameters and on the overall fit of the model The diagnostics are applied to data from four research studies from public health and medicine

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  2. A focused information criterion for graphical models
  3. An advancement in clustering via nonparametric density estimation
  4. Clustering via nonparametric density estimation
  5. Sequential importance sampling of binary sequences
  6. Boosted coefficient models
  7. Optimal SIR algorithm vs. fully adapted auxiliary particle filter: a non asymptotic analysis
  8. Non-parametric maximum likelihood estimation of interval-censored failure time data subject to misclassification
  9. Gaussian proposal density using moment matching in SMC methods
  10. Bayesian inference for Heston-STAR models
  11. Parsimonious Gaussian mixture models
  12. Efficient estimation of variance components in nonparametric mixed-effects models with large samples
  13. On the computation of the noncentral F and noncentral beta distribution
  14. A new variable importance measure for random forests with missing data
  15. Complement to a comparative analysis of heuristics for the  p -median problem
  16. Likelihood-free parallel tempering
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