MODEL REGRESI BINOMIAL NEGATIF TERBOBOTI GEOGRAFIS UNTUK DATA KEMATIAN BAYI

Authors

  • Lusi Eka Afri Program Studi Pendidikan Matematika Fakultas Keguruan dan Ilmu Pendidikan Universitas Pasir Pengaraian

Keywords:

Negative binomial regression, geographically weighted negative binomial regression, adaptive bi-square, overdispersion

Abstract

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.

Author Biography

Lusi Eka Afri, Program Studi Pendidikan Matematika Fakultas Keguruan dan Ilmu Pendidikan Universitas Pasir Pengaraian

Program Studi Pendidikan Matematika Fakultas Keguruan dan Ilmu PendidikanUniversitas Pasir Pengaraian

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.

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Published

2014-10-20