A lack-of-fit test for parametric zero-inflated Poisson models

References

• Singh , S. 1963 . A note on inflated Poisson distribution . J. Indian Statist. Assoc. , 1 : 140 – 144 .
   Google Scholar
• Johnson , N. L. , Kotz , S. and Kemp , A. W. 1992 . Univariate Discrete Distributions , New York : Wiley & Sons .
   Google Scholar
• Dietz , K. and Böhning , D. 1997 . The use of two-component mixture models with one completely or partly known component . Comput. Stat. , 12 : 219 – 234 .
   Google Scholar
• El-Shaarawi , A. H. 1985 . Some goodness-of-fit methods for the Poisson plus added zeros distribution . Appl. Environ. Microbiol. , 49 : 1304 – 1306 .
   PubMedGoogle Scholar
• van den Broek , J. 1995 . A score test for zero inflation in a Poisson distribution . Biometrics , 51 : 738 – 743 .
   PubMed Web of Science ®Google Scholar
• Ridout , M. , Hinde , J. and Demétrio , G. 2001 . A score test for testing a zero-inflated Poisson regression model against zero-inflated negative binomial alternatives . Biometrics , 57 : 219 – 223 .
   PubMed Web of Science ®Google Scholar
• Thas , O. and Rayner , J. C.W. 2005 . Smooth tests for the zero-inflated Poisson distribution . Biometrics , 61 : 808 – 815 .
   PubMed Web of Science ®Google Scholar
• Lambert , D. 1992 . Zero-inflated Poisson regression, with an application to defects in manufacturing . Technometrics , 34 : 1 – 14 .
   Web of Science ®Google Scholar
• Hall , D. B. and Zhang , Z. 2004 . Marginal models for zero inflated cluster data . Statist. Model. , 4 : 161 – 180 .
   Web of Science ®Google Scholar
• Min , Y. and Agresti , A. 2005 . Random effect models for repeated measures of zero-inflated count data . Statist. Model. , 5 : 1 – 19 .
   Web of Science ®Google Scholar
• Hall , D. B. and Wang , L. 2005 . Two-component mixtures of generalized linear mixed effects models for cluster correlated data . Statist. Model. , 5 : 21 – 37 .
   Google Scholar
• Böhning , D. , Dietz , E. , Schlattmann , P. , Mendonca , L. and Kirchner , U. 1999 . The zero-inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology . J. R. Stat. Soc., Ser. A , 162 : 195 – 209 .
   Web of Science ®Google Scholar
• Yau , K. K.W. and Lee , A. H. 2001 . Zero-inflated Poisson regression with random effects to evaluate an occupational injury prevention programme . Statist. Med. , 20 : 2907 – 2920 .
   PubMed Web of Science ®Google Scholar
• Cheung , Y. B. 2002 . Zero-inflated models for regression analysis of count data: A study of growth and development . Statist. Med. , 21 : 1461 – 1469 .
   PubMed Web of Science ®Google Scholar
• Lu , S. E. , Lin , Y. and Shih , W. C.J. 2004 . Analyzing excessive no changes in clinical trials with clustered data . Biometrics , 60 : 257 – 267 .
   PubMed Web of Science ®Google Scholar
• Schoenberg , I. J. 1946 . Contributions to the problem of approximation of equidistant data by analytic functions . Quart. Appl. Math. , 4 : 45 – 99 . 112–141
   Web of Science ®Google Scholar
• Curry , H. B. and Schoenberg , I. J. 1966 . On Pólya frequency functions IV: The fundamental splines and their limits . J. Analyse Math. , 17 : 71 – 107 .
   Web of Science ®Google Scholar
• Smith , P. L. 1979 . Splines as a useful and convenient statistical tool . Am. Stat. , 33 : 57 – 62 .
   Web of Science ®Google Scholar
• Wold , S. 1974 . Spline functions in data analysis . Technometrics , 16 : 1 – 11 .
   Web of Science ®Google Scholar
• Lindstrom , M. 1999 . Penalized estimation of free-knot splines . J. Comput. Graph. Statist. , 8 : 333 – 352 .
   Web of Science ®Google Scholar
• Ruppert , D. 2002 . Selecting the number of knots for penalized splines . J. Comput. Graph. Statist. , 11 : 735 – 757 .
   Web of Science ®Google Scholar
• de Boor , C. 2001 . A Practical Guide to Splines , revised , Vol. 27 , New York : Springer-Verlag . Applied Mathematical Sciences
   Google Scholar
• Louis , T. A. 1982 . Finding the observed information matrix when using the EM algorithm . J. R. Statist. Soc. B , 44 : 226 – 233 .
   Web of Science ®Google Scholar