Mixture model framework facilitates understanding of zero-inflated and hurdle models for count data.
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Modeling heterogeneity for count data: A study of maternal mortality in health facilities in Mozambique.Using statistical models to assess medical cost of hepatitis C virus.Specific count model for investing the related factors of cost of GERD and functional dyspepsia.Statistical models for longitudinal zero-inflated count data with applications to the substance abuse field.Statistical models for predicting number of involved nodes in breast cancer patients.Hot spots in mortality from drug poisoning in the United States, 2007-2009.Trends and geographic patterns in drug-poisoning death rates in the U.S., 1999-2009.Bayesian Zero- Inflated Poisson model for prognosis of demographic factors associated with using crystal meth in Tehran population
P2860
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P2860
Mixture model framework facilitates understanding of zero-inflated and hurdle models for count data.
description
2007 nî lūn-bûn
@nan
2007 թուականի Յունուարին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի հունվարին հրատարակված գիտական հոդված
@hy
2007年の論文
@ja
2007年学术文章
@wuu
2007年学术文章
@zh-cn
2007年学术文章
@zh-hans
2007年学术文章
@zh-my
2007年学术文章
@zh-sg
2007年學術文章
@yue
name
Mixture model framework facili ...... hurdle models for count data.
@ast
Mixture model framework facili ...... hurdle models for count data.
@en
type
label
Mixture model framework facili ...... hurdle models for count data.
@ast
Mixture model framework facili ...... hurdle models for count data.
@en
prefLabel
Mixture model framework facili ...... hurdle models for count data.
@ast
Mixture model framework facili ...... hurdle models for count data.
@en
P1476
Mixture model framework facili ...... hurdle models for count data.
@en
P2093
Baughman AL
P2860
P304
P356
10.1080/10543400701514098
P577
2007-01-01T00:00:00Z