MODEL REGRESI BINOMIAL NEGATIF TERBOBOTI GEOGRAFIS UNTUK DATA KEMATIAN BAYI
Keywords:
Negative binomial regression, geographically weighted negative binomial regression, adaptive bi-square, overdispersionAbstract
Negative binomial regression model is used to overcome the overdispersion in Poisson regression model. This model can be used to model therelationship of the infant mortality and the factors incidence. Geographical conditions, socio cultural and economic differ one of location another locationcauses the factors that influence infant mortality is different locally. Geographically Weighted Negative Binomial Regression (GWNBR) is one ofmethods for modeling that count data have spatial heterogeneity and overdispersion.References
Agresti A. 2002. Categorical Data
Analysis Second Edition. New
York : John Wiley & Sons.
Anselin L. 1988. Spatial
Econometrics : Methods and
Models. Dordrecht : Kluwer
Academic Publishers.
[BPS] Badan Pusat Statistik. 2008.
Data dan Informasi Kemiskinan
Jakarta: Badan Pusat
Statistik.
Berk D, MacDonald J. 2007.
Overdispersion and Poisson
Regression. Departement of
Statistics, Department of
Criminology, University of
Pennsylvania.
Brunsdon C, Fotheringham AS,
Charlton M. 1998.
Geographically Weighted
Regression: a Method for
Exploring Spatial
Nonstationarity. Geographical
Analysis 28: 281-298.
Cameron AC, Trivedi PK. 1998.
Regression Analysis of Count
Data. United Kingdom :
Cambridge University Press.
Casella G, Berger RL. 1990.
Statistiscal Inference. California
: Brooks/Cole.
[Dinkes] Dinas Kesehatan. 2009.
Profil Kesehatan Provinsi Jawa
Timur 2009. Surabaya : Dinas
Kesehatan Provinsi Jawa Timur
Fleiss JL, Levin B, Paik MC. 2003.
Statistical Methods for Rates And
Proportions. Ed ke-3. USA :
Columbia University.
Fotheringham AS, Brunsdon C,
Charlton M. 2002.
Geographically Weighted
Regression,the Analysis of
Spatially Varying Relationships.
Chichester : John Wiley and
Sons.
Hardin JW, Hilbe JM. 2007.
Generalized Linier Models and
Extensions. Texas : Stata press.
Hinde J, Dem’etrio CGB. 1998.
Overdispersion: Models and
Estimation. Computational
Statistics and Data Analisis 27 :
-170.
[Kemenkes] Kementrian Kesehatan.
Lima Provinsi
Penyumbang Angka Kematian
Ibu dan Anak Tertinggi.
Kementrian Kesehatan RI.
Kleinbaum DG, Kupper LL, Muller
KE. 1988. Apllied Regression
Analysis and Other Multivariabel
Methods. Boston : PWS-KENT
Publishing Company.
McCullagh P, Nelder JA. 1989.
Generalized Linear Models
Second Edition, London:
Chapman and Hall.
McCulloch CE, Searle SR. 2001.
Generalized Linear and Mixed
Models. Canada : John Wiley &
Sons, Inc.
Myers RH. 1990. Classical and
Modern Regression with
Applications Second Edition.
New York: PWS-KENT.
Osgood D Wayne. 2000. Poisson-
Based Regression Analysis of
Aggregate Crime Rates.
Journal of Quantitative
Criminology ,16 : 21–43.
Rahmawati R. 2010. Model Terboboti
Geografis dengan Pembobot
Kernel Normal dan Kernel
Kuadrat Ganda untuk Data
Kemiskinan (Kasus 35 Desa atau
Kelurahan di Kabupaten Jember)
[tesis]. Bogor : Program
Pascasarjana, Institut Pertanian
Bogor.
Rizky F. 2009. Pemodelan Jumlah
Kematian Bayi dengan Faktor
PDRB dan Indikator Kesehatan Jawa Timur. Surabaya : Program
Sarjana, Institut Teknologi
Sepuluh Nopember.
Rohimah S. 2011. Model Spasial
Otoregresif Poisson untuk
Mendeteksi Factor-Faktor yang
Berpengaruh terhadap Jumlah
Penderita Gizi Buruk di Provinsi
Jawa Timur [tesis]. Bogor :
Program Pascasarjana, Institut
Pertanian Bogor.
Schabenberger O dan Gotway CA.
Statistical Methods For
Spatial Data Analysis. Chapman
& Hall/CRC.
Yrigoyen CC., Rodriguez IG. 2008.
“Modelling Spatial Variations in
House-hold Disposable Income
With Geographically Weighted
Regression(1)”, Estadistica
Espanola vol. 50.168:321-360.
