Literature DB >> 29284370

A test of inflated zeros for Poisson regression models.

Hua He1, Hui Zhang2, Peng Ye1,3, Wan Tang4.   

Abstract

Excessive zeros are common in practice and may cause overdispersion and invalidate inference when fitting Poisson regression models. There is a large body of literature on zero-inflated Poisson models. However, methods for testing whether there are excessive zeros are less well developed. The Vuong test comparing a Poisson and a zero-inflated Poisson model is commonly applied in practice. However, the type I error of the test often deviates seriously from the nominal level, rendering serious doubts on the validity of the test in such applications. In this paper, we develop a new approach for testing inflated zeros under the Poisson model. Unlike the Vuong test for inflated zeros, our method does not require a zero-inflated Poisson model to perform the test. Simulation studies show that when compared with the Vuong test our approach not only better at controlling type I error rate, but also yield more power.

Entities:  

Keywords:  Vuong test; Zero-inflated Poisson (ZIP); hypothesis test; power; type I error

Mesh:

Year:  2017        PMID: 29284370      PMCID: PMC6345607          DOI: 10.1177/0962280217749991

Source DB:  PubMed          Journal:  Stat Methods Med Res        ISSN: 0962-2802            Impact factor:   3.021


  9 in total

1.  Zero-inflated Poisson and binomial regression with random effects: a case study.

Authors:  D B Hall
Journal:  Biometrics       Date:  2000-12       Impact factor: 2.571

2.  Zero-inflated models for regression analysis of count data: a study of growth and development.

Authors:  Yin Bin Cheung
Journal:  Stat Med       Date:  2002-05-30       Impact factor: 2.373

3.  Testing the fit of a regression model via score tests in random effects models.

Authors:  S le Cessie; H C van Houwelingen
Journal:  Biometrics       Date:  1995-06       Impact factor: 2.571

Review 4.  Distribution-free models for longitudinal count responses with overdispersion and structural zeros.

Authors:  Q Yu; R Chen; W Tang; H He; R Gallop; P Crits-Christoph; J Hu; X M Tu
Journal:  Stat Med       Date:  2012-12-12       Impact factor: 2.373

5.  On performance of parametric and distribution-free models for zero-inflated and over-dispersed count responses.

Authors:  Wan Tang; Naiji Lu; Tian Chen; Wenjuan Wang; Douglas David Gunzler; Yu Han; Xin M Tu
Journal:  Stat Med       Date:  2015-06-15       Impact factor: 2.373

6.  Zero-inflated Poisson regression with random effects to evaluate an occupational injury prevention programme.

Authors:  K K Yau; A H Lee
Journal:  Stat Med       Date:  2001-10-15       Impact factor: 2.373

7.  Self-management intervention for long-term indwelling urinary catheter users: randomized clinical trial.

Authors:  Mary H Wilde; James M McMahon; Margaret V McDonald; Wan Tang; Wenjuan Wang; Judith Brasch; Eileen Fairbanks; Shivani Shah; Feng Zhang; Ding-Geng Din Chen
Journal:  Nurs Res       Date:  2015 Jan-Feb       Impact factor: 2.381

8.  Motivational and skills training HIV/sexually transmitted infection sexual risk reduction groups for men.

Authors:  Donald A Calsyn; Mary Hatch-Maillette; Susan Tross; Suzanne R Doyle; Paul Crits-Christoph; Yong S Song; Judy M Harrer; Genise Lalos; Sara B Berns
Journal:  J Subst Abuse Treat       Date:  2009-01-15

9.  Predictors and moderators of outcomes of HIV/STD sex risk reduction interventions in substance abuse treatment programs: a pooled analysis of two randomized controlled trials.

Authors:  Paul Crits-Christoph; Robert Gallop; Jaclyn S Sadicario; Hannah M Markell; Donald A Calsyn; Wan Tang; Hua He; Xin Tu; George Woody
Journal:  Subst Abuse Treat Prev Policy       Date:  2014-01-16
  9 in total
  1 in total

1.  A GEE-type approach to untangle structural and random zeros in predictors.

Authors:  Peng Ye; Wan Tang; Jiang He; Hua He
Journal:  Stat Methods Med Res       Date:  2018-11-26       Impact factor: 3.021

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.