Statistical models for longitudinal zero-inflated count data with applications to the substance abuse field.
about
A novel modeling framework for ordinal data defined by collapsed counts.The importance of distribution-choice in modeling substance use data: a comparison of negative binomial, beta binomial, and zero-inflated distributions.Semiparametric models for multilevel overdispersed count data with extra zeros.Joint modeling of longitudinal zero-inflated count and time-to-event data: A Bayesian perspective.Changes in substance use-related health risk behaviors on the timeline follow-back interview as a function of length of recall periodAn efficient genome-wide association test for multivariate phenotypes based on the Fisher combination function.Identifying Pleiotropic Genes in Genome-Wide Association Studies for Multivariate Phenotypes with Mixed Measurement ScalesA Marginalized Zero-inflated Poisson Regression Model with Random EffectsApplication of hurdle model with random effects for evaluating the balance improvement in stroke patientsTwo-stage model for time varying effects of zero-inflated count longitudinal covariates with applications in health behaviour researchLongitudinal associations between social anxiety symptoms and cannabis use throughout adolescence: the role of peer involvementA time-varying effect model for studying gender differences in health behaviorZero-inflated count models for longitudinal measurements with heterogeneous random effects.Identifying pleiotropic genes in genome-wide association studies from related subjects using the linear mixed model and Fisher combination function.A joint modeling and estimation method for multivariate longitudinal data with mixed types of responses to analyze physical activity data generated by accelerometers.Predictive validity of cannabis consumption measures: Results from a national longitudinal study.Joint modeling of zero-inflated panel count and severity outcomes.A time-varying effect model for examining group differences in trajectories of zero-inflated count outcomes with applications in substance abuse research.Weibull mixture regression for marginal inference in zero-heavy continuous outcomes.Logistic regression for dichotomized counts.Three-part joint modeling methods for complex functional data mixed with zero-and-one-inflated proportions and zero-inflated continuous outcomes with skewness.Bivariate geostatistical modelling of the relationship between Loa loa prevalence and intensity of infection
P2860
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P2860
Statistical models for longitudinal zero-inflated count data with applications to the substance abuse field.
description
2012 nî lūn-bûn
@nan
2012 թուականի Յուլիսին հրատարակուած գիտական յօդուած
@hyw
2012 թվականի հուլիսին հրատարակված գիտական հոդված
@hy
2012年の論文
@ja
2012年論文
@yue
2012年論文
@zh-hant
2012年論文
@zh-hk
2012年論文
@zh-mo
2012年論文
@zh-tw
2012年论文
@wuu
name
Statistical models for longitu ...... to the substance abuse field.
@ast
Statistical models for longitu ...... to the substance abuse field.
@en
Statistical models for longitu ...... to the substance abuse field.
@nl
type
label
Statistical models for longitu ...... to the substance abuse field.
@ast
Statistical models for longitu ...... to the substance abuse field.
@en
Statistical models for longitu ...... to the substance abuse field.
@nl
prefLabel
Statistical models for longitu ...... to the substance abuse field.
@ast
Statistical models for longitu ...... to the substance abuse field.
@en
Statistical models for longitu ...... to the substance abuse field.
@nl
P2093
P2860
P356
P1476
Statistical models for longitu ...... to the substance abuse field.
@en
P2093
Robert A Zucker
Xianming Tan
P2860
P304
P356
10.1002/SIM.5510
P407
P50
P577
2012-07-24T00:00:00Z