What belongs where? Variable selection for zero-inflated count models with an application to the demand for health care

Jochmann, Markus

doi:10.1007/s00180-012-0388-z

Cited by 10 publications

(18 citation statements)

References 27 publications

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“…Sensitivities for the zero components are typically lower than the corresponding sensitivities for the NB components. As argued in Jochmann (), this is to be anticipated since the data generally are not very informative about the hidden components of the observations.…”

Section: A Simulation Studymentioning

confidence: 96%

“…Determining predictors in zero components is more difficult than determining predictors in the count components (Buu et al., ; Jochmann, ; Wang et al., ). Sensitivities for the zero components are typically lower than the corresponding sensitivities for the NB components.…”

Section: A Simulation Studymentioning

confidence: 99%

“…We focus on zero‐inflated models and alternative models may be found in Riphahn et al. (); Jochmann (). The predictor variables are illustrated in Table .…”

Section: Health Care Demandmentioning

confidence: 99%

“…The predictor variables are illustrated in Table . This dataset was recently analyzed using a Bayesian variable selection procedure (Jochmann, ). Following Jochmann (), instead of the original variable age and its square, we study more complex effect of age.…”

Section: Health Care Demandmentioning

confidence: 99%

“…This dataset was recently analyzed using a Bayesian variable selection procedure (Jochmann, ). Following Jochmann (), instead of the original variable age and its square, we study more complex effect of age. Namely, we include the linear spline variables age30 to age60 and their interaction terms with the health satisfaction health (for instance health:age30 ).…”

Section: Health Care Demandmentioning

confidence: 99%

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Variable selection for zero‐inflated and overdispersed data with application to health care demand in Germany

Wang

2015

Biometrical J

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In health services and outcome research, count outcomes are frequently encountered and often have a large proportion of zeros. The zero-inflated negative binomial (ZINB) regression model has important applications for this type of data. With many possible candidate risk factors, this paper proposes new variable selection methods for the ZINB model. We consider maximum likelihood function plus a penalty including the least absolute shrinkage and selection operator (LASSO), smoothly clipped absolute deviation (SCAD) and minimax concave penalty (MCP). An EM (expectation-maximization) algorithm is proposed for estimating the model parameters and conducting variable selection simultaneously. This algorithm consists of estimating penalized weighted negative binomial models and penalized logistic models via the coordinated descent algorithm. Furthermore, statistical properties including the standard error formulae are provided. A simulation study shows that the new algorithm not only has more accurate or at least comparable estimation, also is more robust than the traditional stepwise variable selection. The proposed methods are applied to analyze the health care demand in Germany using an open-source R package .

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Section: A Simulation Studymentioning

confidence: 96%

Section: A Simulation Studymentioning

confidence: 99%

“…We focus on zero‐inflated models and alternative models may be found in Riphahn et al. (); Jochmann (). The predictor variables are illustrated in Table .…”

Section: Health Care Demandmentioning

confidence: 99%

Section: Health Care Demandmentioning

confidence: 99%

Section: Health Care Demandmentioning

confidence: 99%

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Variable selection for zero‐inflated and overdispersed data with application to health care demand in Germany

Wang

2015

Biometrical J

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Group regularization for zero‐inflated negative binomial regression models with an application to health care demand in Germany

Chatterjee

Chowdhury

Mallick

et al. 2018

Statistics in Medicine

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In many biomedical applications, covariates are naturally grouped, with variables in the same group being systematically related or statistically correlated. Under such settings, variable selection must be conducted at both group and individual variable levels. Motivated by the widespread availability of zero-inflated count outcomes and grouped covariates in many practical applications, we consider group regularization for zero-inflated negative binomial regression models. Using a least squares approximation of the mixture likelihood and a variety of group-wise penalties on the coefficients, we propose a unified algorithm (Gooogle: Group Regularization for Zero-inflated Count Regression Models) to efficiently compute the entire regularization path of the estimators. We investigate the finite sample performance of these methods through extensive simulation experiments and the analysis of a German health care demand dataset. Finally, we derive theoretical properties of these methods under reasonable assumptions, which further provides deeper insight into the asymptotic behavior of these approaches. The open source software implementation of this method is publicly available at: https://github.com/himelmallick/Gooogle.

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Detecting over- and under-dispersion in zero inflated data with the hyper-Poisson regression model

Sáez-Castillo

Sánchez

2015

Stat Papers

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What belongs where? Variable selection for zero-inflated count models with an application to the demand for health care

Cited by 10 publications

References 27 publications

Variable selection for zero‐inflated and overdispersed data with application to health care demand in Germany

Variable selection for zero‐inflated and overdispersed data with application to health care demand in Germany

Group regularization for zero‐inflated negative binomial regression models with an application to health care demand in Germany

Detecting over- and under-dispersion in zero inflated data with the hyper-Poisson regression model

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