Modelling alcohol consumption during adolescence using zero inflated negative binomial and decision trees

Modelling alcohol consumption during adolescence using zero inflated negative binomial and decision trees

Alcohol is currently the most consumed substance among the Spanish adolescent population. Some of the variables that bear an influence on this consumption include ease of access, use of alcohol by friends and some personality factors. The aim of this study was to analyze and quantify the predictive...

Full description

Bibliographic Details
Main Authors:	Alfonso Palmer, Jona Roca, Berta Cajal, Elena Gervilla
Format:	Article
Language:	English
Published:	Sociedad Española de Psicología Jurídica y Forense 2010-07-01
Series:	European Journal of Psychology Applied to Legal Context
Subjects:	Zero inflated negative binomial model drinking behavior social drinking data mining Decision trees Adolescence.
Online Access:	http://webs.uvigo.es/sepjf/index.php?option=com_docman&task=doc_download&gid=28&Itemid=110&lang=en

Similar Items

NBZIMM: negative binomial and zero-inflated mixed models, with application to microbiome/metagenomics data analysis
by: Xinyan Zhang, et al.
Published: (2020-10-01)

Modeling the Number of Attacks in Multiple Sclerosis Patients Using Zero-Inflated Negative Binomial Model
by: Marjan Jamalian, et al.
Published: (2020-01-01)

The zero inflated negative binomial – Crack distribution: some properties and parameter estimation
by: Pornpop Saengthong, et al.
Published: (2015-12-01)

Risk Factors Influencing Cyberbullying Perpetration among Middle School Students in Korea: Analysis Using the Zero-Inflated Negative Binomial Regression Model
by: Kyung Im Kang, et al.
Published: (2021-02-01)

Comparison of linear and zero-inflated negative binomial regression models for appraisal of risk factors associated with dental caries
by: Manu Batra, et al.
Published: (2016-01-01)

A Dynamical and Zero-Inflated Negative Binomial Regression Modelling of Malaria Incidence in Limpopo Province, South Africa
by: Gbenga J. Abiodun, et al.
Published: (2019-06-01)

Statistical analysis of variability in TnSeq data across conditions using zero-inflated negative binomial regression
by: Siddharth Subramaniyam, et al.
Published: (2019-11-01)

Niacin, lutein and zeaxanthin and physical activity have an impact on Charlson comorbidity index using zero-inflated negative binomial regression model: National Health and Nutrition Examination Survey 2013–2014
by: Hantong Zhao, et al.
Published: (2019-11-01)

Using zero inflated models to analyze dental caries with many zeroes
by: Javali Shivalingappa, et al.
Published: (2010-01-01)

Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
by: Koen Van den Berge, et al.
Published: (2018-02-01)

Using Count Data Models to Predict Epiphytic Bryophyte Recruitment in <i>Schima superba</i> Gardn. et Champ. Plantations in Urban Forests
by: Dexian Zhao, et al.
Published: (2020-02-01)

Zero inflated negative binomial-generalized exponential distributionand its applications
by: Sirinapa Aryuyuen, et al.
Published: (2014-08-01)

Count data regression modeling: an application to spontaneous abortion
by: Prashant Verma, et al.
Published: (2020-07-01)

Comparing Bayesian regression and classic zero-inflated negative binomial on size estimation of people who use alcohol
by: Sepideh Roohi, et al.
Published: (2017-10-01)

The Negative Binomial regression
by: Shengping Yang, et al.
Published: (2015-04-01)

Zero-inflated negative binomial modeling, efficiency for analysis of length of maternity hospitalization
by: mohammad Rafiee, et al.
Published: (2005-02-01)

An improved bound for negative binomial approximation with -functions
by: K. Teerapabolarn
Published: (2017-12-01)

Measurement error effect on the power of control chart for Zero truncated Negative Binomial Distribution (ZTNBD)
by: Chakraborty A.B., et al.
Published: (2017-01-01)

Bayesian negative binomial logit hurdle and zero-inflated model for characterizing smoking intensity
by: Mekuanint Simeneh Workie, et al.
Published: (2021-04-01)

Distraction towards contextual alcohol cues and craving are associated with levels of alcohol use among youth
by: Timo Lehmann Kvamme, et al.
Published: (2018-10-01)

Zero-Inflated Censored Regression Models: An Application with Episode of Care Data
by: Prasad, Jonathan P.
Published: (2009)

Frequency of seizure attack and associated factors among patients with epilepsy at University of Gondar Referral Hospital: a cross-sectional study, Gondar, North West Ethiopia, 2017
by: Mekdes Tigistu, et al.
Published: (2018-09-01)

Predicting casualty-accident count by highway design standards compliance
by: Arief Rizaldi, et al.
Published: (2017-09-01)

Double Generalized Beta-Binomial and Negative Binomial Regression Models
by: EDILBERTO CEPEDA-CUERVO, et al.
Published: (2017-01-01)

Influence of Sociodemographic, Premorbid, and Injury-Related Factors on Post-Concussion Symptoms after Traumatic Brain Injury
by: Marina Zeldovich, et al.
Published: (2020-06-01)

Comparing Performance of ANOVA to Poisson and Negative Binomial Regression When Applied to Count Data
by: Soumare, Ibrahim
Published: (2021)

A comparison of zero-inflated and hurdle models for modeling zero-inflated count data
by: Cindy Xin Feng
Published: (2021-06-01)

Vad påverkar tiden som en mamma ammar? : -en empirisk studie
by: Brundin, Robert, et al.
Published: (2006)

Multiplicative-Binomial Distribution: Some Results on Characterization, Inference and Random Data Generation
by: Elsayed A.H. Elamir
Published: (2013-05-01)

A generalization of the negative binomial distribution
by: Maryam Nazemipour, et al.
Published: (2017-08-01)

Regression Models for Count Data in R
by: Christian Kleiber, et al.
Published: (2008-06-01)

An examination of data from the American Gut Project reveals that the dominance of the genus Bifidobacterium is associated with the diversity and robustness of the gut microbiota
by: Yuqing Feng, et al.
Published: (2019-12-01)

Regression Models for Count Data in R
by: Zeileis, Achim, et al.
Published: (2008)

The role of negative urgency in risky alcohol drinking and binge-eating in United Kingdom male and female students
by: Christina Ralph-Nearman, et al.
Published: (2020-06-01)

Justification for considering zero-inflated models in crash frequency analysis
by: Timo Pew, et al.
Published: (2020-11-01)

A note on negative λ-binomial distribution
by: Yuankui Ma, et al.
Published: (2020-10-01)

Factors associated with mortality from tuberculosis in Iran: an application of a generalized estimating equation-based zero-inflated negative binomial model to national registry data
by: Fatemeh Sarvi, et al.
Published: (2019-07-01)

EM Estimation for Zero- and <i>k</i>-Inflated Poisson Regression Model
by: Monika Arora, et al.
Published: (2021-08-01)

Binomial sequences
by: Andrzej Nowicki
Published: (2019-07-01)

INVESTIGATION OF E-MAIL TRAFFIC BY USING ZERO-INFLATED REGRESSION MODELS
by: Yılmaz KAYA, et al.
Published: (2012-06-01)

Cannot write session to /tmp/vufind_sessions/sess_vu4uqmm27u4qv04ho63rgmeuh3