國立高雄大學圖資館 |

Language: English

Back

Modeling count data

Hilbe, Joseph M., (1944-)

Modeling count data

Record Type:	Electronic resources : Monograph/item
Title/Author:	Modeling count dataJoseph M. Hilbe.
Author:	Hilbe, Joseph M.,
Published:	Cambridge :Cambridge University Press,2014.
Description:	xv, 283 p. :ill., digital ;24 cm.
Subject:	Multivariate analysis.
Online resource:	https://doi.org/10.1017/CBO9781139236065
ISBN:	9781139236065$q(electronic bk.)

Modeling count data
Hilbe, Joseph M.,1944-

Modeling count data[electronic resource] /Joseph M. Hilbe. - Cambridge :Cambridge University Press,2014. - xv, 283 p. :ill., digital ;24 cm.

Machine generated contents note: Preface; 1. Varieties of count data; 2. Poisson regression; 3. Testing overdispersion; 4. Assessment of fit; 5. Negative binomial regression; 6. Poisson inverse Gaussian regression; 7. Problems with zeros; 8. Modeling under-dispersed count data - generalized Poisson; 9. Complex data: more advanced models; Appendix A: SAS code; References; Index.

This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of modeling count data, including a thorough presentation of the Poisson model. It then works up to an analysis of the problem of overdispersion and of the negative binomial model, and finally to the many variations that can be made to the base count models. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in health, ecology, econometrics, transportation, and other fields.

ISBN: 9781139236065$q(electronic bk.)Subjects--Topical Terms:

181905
Multivariate analysis.

LC Class. No.: QA278 / .H56 2014

Dewey Class. No.: 519.535

Modeling count data
LDR:01977nmm a2200265 a 4500 001 534396
003 UkCbUP
005 20181005153155.0
006 m d
007 cr nn 008maaau
008 181206s2014 enk s 0 eng d
020 $a 9781139236065$q(electronic bk.)
020 $a 9781107028333$q(hardback)
020 $a 9781107611252$q(paperback)
035 $a CR9781139236065
040 $a UkCbUP $b eng $c UkCbUP $d GP
041 0 $a eng
050 4 $a QA278 $b .H56 2014
082 0 4 $a 519.535 $2 23
090 $a QA278 $b .H641 2014
100 1 $a Hilbe, Joseph M., $d 1944- $3 606172
245 1 0 $a Modeling count data $h [electronic resource] / $c Joseph M. Hilbe.
260 $a Cambridge : $b Cambridge University Press, $c 2014.
300 $a xv, 283 p. : $b ill., digital ; $c 24 cm.
505 8 $a Machine generated contents note: Preface; 1. Varieties of count data; 2. Poisson regression; 3. Testing overdispersion; 4. Assessment of fit; 5. Negative binomial regression; 6. Poisson inverse Gaussian regression; 7. Problems with zeros; 8. Modeling under-dispersed count data - generalized Poisson; 9. Complex data: more advanced models; Appendix A: SAS code; References; Index.
520 $a This entry-level text offers clear and concise guidelines on how to select, construct, interpret, and evaluate count data. Written for researchers with little or no background in advanced statistics, the book presents treatments of all major models using numerous tables, insets, and detailed modeling suggestions. It begins by demonstrating the fundamentals of modeling count data, including a thorough presentation of the Poisson model. It then works up to an analysis of the problem of overdispersion and of the negative binomial model, and finally to the many variations that can be made to the base count models. Examples in Stata, R, and SAS code enable readers to adapt models for their own purposes, making the text an ideal resource for researchers working in health, ecology, econometrics, transportation, and other fields.
650 0 $a Multivariate analysis. $3 181905
650 0 $a Statistics. $3 182057
650 0 $a Linear models (Statistics) $3 181866
856 4 0 $u https://doi.org/10.1017/CBO9781139236065