156 related articles for article (PubMed ID: 21518631)
1. [Application of zero-inflated models on regression analysis of count data: a study of sub-health symptoms].
Xu T; Li W; Chen T
Zhonghua Liu Xing Bing Xue Za Zhi; 2011 Feb; 32(2):187-91. PubMed ID: 21518631
[TBL] [Abstract][Full Text] [Related]
2. Study of Zero-Inflated Regression Models in a Large-Scale Population Survey of Sub-Health Status and Its Influencing Factors.
Xu T; Zhu GJ; Han SM
Chin Med Sci J; 2017 Dec; 32(4):218-225. PubMed ID: 29301596
[TBL] [Abstract][Full Text] [Related]
3. Social hidden groups size analyzing: application of count regression models for excess zeros.
Jalali M; Nikfarjam A; Haghdoost AA; Memaryan N; Tarjoman T; Baneshi MR
J Res Health Sci; 2013 Sep; 13(2):143-50. PubMed ID: 24077471
[TBL] [Abstract][Full Text] [Related]
4. Study of depression influencing factors with zero-inflated regression models in a large-scale population survey.
Xu T; Zhu G; Han S
BMJ Open; 2017 Nov; 7(11):e016471. PubMed ID: 29187409
[TBL] [Abstract][Full Text] [Related]
5. Comparison of methods for modelling a count outcome with excess zeros: application to Activities of Daily Living (ADL-s).
Zaninotto P; Falaschetti E
J Epidemiol Community Health; 2011 Mar; 65(3):205-10. PubMed ID: 20675703
[TBL] [Abstract][Full Text] [Related]
6. Statistical modelling of falls count data with excess zeros.
Khan A; Ullah S; Nitz J
Inj Prev; 2011 Aug; 17(4):266-70. PubMed ID: 21653652
[TBL] [Abstract][Full Text] [Related]
7. Zero inflated statistical count models for analysing the costs imposed by GERD and dyspepsia.
Akbarzadeh Baghban A; Pourhoseingholi A; Zayeri F; Ashtari S; Zali MR
Arab J Gastroenterol; 2013 Dec; 14(4):165-8. PubMed ID: 24433646
[TBL] [Abstract][Full Text] [Related]
8. Statistical modelling for falls count data.
Ullah S; Finch CF; Day L
Accid Anal Prev; 2010 Mar; 42(2):384-92. PubMed ID: 20159058
[TBL] [Abstract][Full Text] [Related]
9. The utility of the zero-inflated Poisson and zero-inflated negative binomial models: a case study of cross-sectional and longitudinal DMF data examining the effect of socio-economic status.
Lewsey JD; Thomson WM
Community Dent Oral Epidemiol; 2004 Jun; 32(3):183-9. PubMed ID: 15151688
[TBL] [Abstract][Full Text] [Related]
10. What statistical method should be used to evaluate risk factors associated with dmfs index? Evidence from the National Pathfinder Survey of 4-year-old Italian children.
Solinas G; Campus G; Maida C; Sotgiu G; Cagetti MG; Lesaffre E; Castiglia P
Community Dent Oral Epidemiol; 2009 Dec; 37(6):539-46. PubMed ID: 19845715
[TBL] [Abstract][Full Text] [Related]
11. Modelling count data with excessive zeros: the need for class prediction in zero-inflated models and the issue of data generation in choosing between zero-inflated and generic mixture models for dental caries data.
Gilthorpe MS; Frydenberg M; Cheng Y; Baelum V
Stat Med; 2009 Dec; 28(28):3539-53. PubMed ID: 19902494
[TBL] [Abstract][Full Text] [Related]
12. Evaluation of negative binomial and zero-inflated negative binomial models for the analysis of zero-inflated count data: application to the telemedicine for children with medical complexity trial.
Lee KH; Pedroza C; Avritscher EBC; Mosquera RA; Tyson JE
Trials; 2023 Sep; 24(1):613. PubMed ID: 37752579
[TBL] [Abstract][Full Text] [Related]
13. Assessment and Selection of Competing Models for Zero-Inflated Microbiome Data.
Xu L; Paterson AD; Turpin W; Xu W
PLoS One; 2015; 10(7):e0129606. PubMed ID: 26148172
[TBL] [Abstract][Full Text] [Related]
14. On performance of parametric and distribution-free models for zero-inflated and over-dispersed count responses.
Tang W; Lu N; Chen T; Wang W; Gunzler DD; Han Y; Tu XM
Stat Med; 2015 Oct; 34(24):3235-45. PubMed ID: 26078035
[TBL] [Abstract][Full Text] [Related]
15. Count data distributions and their zero-modified equivalents as a framework for modelling microbial data with a relatively high occurrence of zero counts.
Gonzales-Barron U; Kerr M; Sheridan JJ; Butler F
Int J Food Microbiol; 2010 Jan; 136(3):268-77. PubMed ID: 19913934
[TBL] [Abstract][Full Text] [Related]
16. Poisson, Poisson-gamma and zero-inflated regression models of motor vehicle crashes: balancing statistical fit and theory.
Lord D; Washington SP; Ivan JN
Accid Anal Prev; 2005 Jan; 37(1):35-46. PubMed ID: 15607273
[TBL] [Abstract][Full Text] [Related]
17. On the use of zero-inflated and hurdle models for modeling vaccine adverse event count data.
Rose CE; Martin SW; Wannemuehler KA; Plikaytis BD
J Biopharm Stat; 2006; 16(4):463-81. PubMed ID: 16892908
[TBL] [Abstract][Full Text] [Related]
18. Models for analyzing zero-inflated and overdispersed count data: an application to cigarette and marijuana use.
Pittman B; Buta E; Krishnan-Sarin S; O'Malley SS; Liss T; Gueorguieva R
Nicotine Tob Res; 2018 Apr; 22(8):1390-8. PubMed ID: 29912423
[TBL] [Abstract][Full Text] [Related]
19. Zero-state Markov switching count-data models: an empirical assessment.
Malyshkina NV; Mannering FL
Accid Anal Prev; 2010 Jan; 42(1):122-30. PubMed ID: 19887152
[TBL] [Abstract][Full Text] [Related]
20. Comparing statistical methods for analyzing skewed longitudinal count data with many zeros: an example of smoking cessation.
Xie H; Tao J; McHugo GJ; Drake RE
J Subst Abuse Treat; 2013 Jul; 45(1):99-108. PubMed ID: 23453482
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
