New variable selection methods for zero‐inflated count data with applications to the substance abuse field
dc.contributor.author | Buu, Anne | en_US |
dc.contributor.author | Johnson, Norman J. | en_US |
dc.contributor.author | Li, Runze | en_US |
dc.contributor.author | Tan, Xianming | en_US |
dc.date.accessioned | 2011-11-10T15:31:16Z | |
dc.date.available | 2012-10-01T18:34:18Z | en_US |
dc.date.issued | 2011-08-15 | en_US |
dc.identifier.citation | Buu, Anne; Johnson, Norman J.; Li, Runze; Tan, Xianming (2011). "New variable selection methods for zero‐inflated count data with applications to the substance abuse field." Statistics in Medicine 30(18): 2326-2340. <http://hdl.handle.net/2027.42/86814> | en_US |
dc.identifier.issn | 0277-6715 | en_US |
dc.identifier.issn | 1097-0258 | en_US |
dc.identifier.uri | https://hdl.handle.net/2027.42/86814 | |
dc.description.abstract | Zero‐inflated count data are very common in health surveys. This study develops new variable selection methods for the zero‐inflated Poisson regression model. Our simulations demonstrate the negative consequences which arise from the ignorance of zero‐inflation. Among the competing methods, the one‐step SCAD method is recommended because it has the highest specificity, sensitivity, exact fit, and lowest estimation error. The design of the simulations is based on the special features of two large national databases commonly used in the alcoholism and substance abuse field so that our findings can be easily generalized to the real settings. Applications of the methodology are demonstrated by empirical analyses on the data from a well‐known alcohol study. Copyright © 2011 John Wiley & Sons, Ltd. | en_US |
dc.publisher | John Wiley & Sons, Ltd. | en_US |
dc.subject.other | LASSO | en_US |
dc.subject.other | One‐Step SCAD | en_US |
dc.subject.other | Variable Selection | en_US |
dc.subject.other | Zero‐Inflated Poisson Distribution | en_US |
dc.title | New variable selection methods for zero‐inflated count data with applications to the substance abuse field | en_US |
dc.type | Article | en_US |
dc.rights.robots | IndexNoFollow | en_US |
dc.subject.hlbsecondlevel | Medicine (General) | en_US |
dc.subject.hlbsecondlevel | Statistics and Numeric Data | en_US |
dc.subject.hlbsecondlevel | Public Health | en_US |
dc.subject.hlbtoplevel | Health Sciences | en_US |
dc.subject.hlbtoplevel | Science | en_US |
dc.subject.hlbtoplevel | Social Sciences | en_US |
dc.description.peerreviewed | Peer Reviewed | en_US |
dc.contributor.affiliationum | Department of Psychiatry, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI 48109, U.S.A. | en_US |
dc.contributor.affiliationum | Department of Psychiatry, University of Michigan, 4250 Plymouth Road, Ann Arbor, MI 48109, U.S.A. | en_US |
dc.contributor.affiliationother | U.S. Census Bureau, Suitland, MD 20746, U.S.A. | en_US |
dc.contributor.affiliationother | Department of Statistics and The Methodology Center, Pennsylvania State University, University Park, PA 16802, U.S.A. | en_US |
dc.contributor.affiliationother | The Methodology Center, Pennsylvania State University, University Park, PA 16802, U.S.A. | en_US |
dc.identifier.pmid | 21563207 | en_US |
dc.description.bitstreamurl | http://deepblue.lib.umich.edu/bitstream/2027.42/86814/1/4268_ftp.pdf | |
dc.identifier.doi | 10.1002/sim.4268 | en_US |
dc.identifier.source | Statistics in Medicine | en_US |
dc.owningcollname | Interdisciplinary and Peer-Reviewed |
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