Triyanto, . (2017) Geographically Weighted Multivariate Poisson Regression (Studi Kasus : Pemodelan Jumlah Kematian Ibu, Neonatal Dini, dan Neonatal Lanjut di Provinsi Jawa Tengah). Doctoral thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Tujuan penelitian ini adalah mengembangkan model multivariate Poisson
regression (MPR) dan geographically weighted multivariate Poisson regression
(GWMPR), yang mencakup estimasi parameter dan pengujian hipotesis.
Pengembangan model dilakukan untuk kovariansi merupakan konstanta dan
kovariansi merupakan fungsi variabel bebas. Selanjutnya, model MPR dan
GWMPR yang telah dikembangkan diterapkan untuk pemodelan jumlah kematian
ibu, neonatal dini, dan neonatal lanjut di Provinsi Jawa Tengah. Hasil penelitian
ini, estimasi parameter dilakukan dengan metode maximum likelihood estimation
(MLE). Permasalahan dalam estimasi parameter dari model ini, metode MLE
tidak dapat menemukan penyelesaian analitis, sehingga diterapkan prosedur iterasi
dengan algoritma Newton-Raphson. Pengujian hipotesis dalam model MPR dan
GWMPR yang meliputi : uji kesamaan model GWMPR dan MPR, uji parameter
serentak, dan uji parameter parsial dilakukan dengan metode likelihood ratio test
(LRT). Hasil studi empiris menunjukkan ada perbedaan antara model GWMPR
dan MPR. Memperhatikan nilai AIC dan MSE, model GWMPR lebih baik
daripada model MPR dalam pemodelan jumlah kematian ibu, neonatal dini, dan
neonatal lanjut. Sementara itu, model dengan kovariansi merupakan fungsi
variabel bebas lebih baik daripada model dengan kovariansi merupakan konstanta.
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The aim of this study is to develop a model of multivariate Poisson regression
(MPR) and geographically weighted multivariate Poisson regression (GWMPR)
which include parameter estimation and hypothesis testing. Development of the
models are done for a constant covariance and covariance as a function of the
independent variables. Furthermore, the MPR and GWMPR models that have
been developed will be applied to modelling the number of mortality of maternal,
early neonatal, and late neonatal in Central Java Province. The results of this
study, the parameters estimation of the MPR and GWMPR models are done by
using maximum likelihood estimation (MLE) method. The problem of parameters
estimation for these models, MLE method can not find an analytical solution, so it
is applied iterative procedure by the Newton-Raphson algorithm. The hypothesis
testing in GWMPR model which include a goodness of fit test, an overall test,
and test of individual parameters are done by using Likelihood Ratio Test (LRT)
method. The results of empirical studies show the difference between the models
of GWMPR and MPR. Considering the value of AIC and MSE, the model of
GWMPR is better than the MPR model to modelling of the number of mortality of
maternal, early neonatal, and late neonatal. Meanwhile, the model with the
covariance as a function of the independent variable is better than a model with a
constant covariance.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Additional Information: | RDSt 519.537 Tri g |
| Uncontrolled Keywords: | geographically weighted, multivariate Poisson regression, data spasial, kematian ibu, neonatal dini, neonatal lanjut. |
| Subjects: | H Social Sciences > HA Statistics |
| Divisions: | Faculty of Mathematics and Science > Statistics > 49001-(S3) PhD Thesis |
| Depositing User: | Users 13 not found. |
| Date Deposited: | 29 May 2017 02:45 |
| Last Modified: | 28 May 2019 02:57 |
| URI: | http://repository.its.ac.id/id/eprint/41391 |
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