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Anneke Iswani A.
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Computer Science & IT Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Industrial & Manufacturing Engineering Mathematics

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Kombinasi Regresi Tak Bias Ridge dengan Regresi Komponen Utama untuk Mengatasi Masalah Multikolinieritas Fitriana Novitasari; Suliadi Suliadi; Anneke Iswani A.
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 17, No 1 (2017)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v17i1.2713

Abstract

Metode kuadrat terkecil (MKT) merupakan salah satu metode penaksir parameter regresi. Dalam metode MKT terdapat salah satu asumsi yang harus dipenuhi yaitu tidak adanya multikolinieritas. Multikolinieritas merupakan hubungan linier antara sesama variabel bebas () yang akan berakibat pada ragam penduga koefisisen regresi menjadi besar sehingga menyebabkan selang kepercayaan untuk parameter regresi cenderung akan lebih lebar. Salah satu metode yang dapat digunakan untuk mengatasi multikolinieritas adalah regresi MCRR, dimana metode MCRR ini merupakan kombinasi antara regresi tak bias ridge (URR) dan regresi komponen utama. Makalah ini akan membahas penanganan multikolinieritas dengan menggunakan MCRR, sebagai bahan aplikasi akan digunakan data tingkat produksi Crude Palm Oil (CPO).
Pendugaan Angka Kematian Bayi Melalui Model Regresi Poisson Bayes Berhirarki Dua-Level (Studi Kasus pada Kota Bandung, Provinsi Jawa Barat) Nusar Hajarisman; Aceng Komarudin Mutaqin; Anneke Iswani A.
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 13, No 2 (2013)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v13i2.1077

Abstract

In this paper, we address the issue of estimation of the hierarchical Bayesian models, especially forcount data in small area estimation problem. This model was developed by combining the existingterminology in generalized linear models with the concept of Bayes methods, especially hierarchicalBayes methods, such that it can be implemented to address the problem of small area estimation forsurvey data in the form of the count data. Development of this model starts by assuming that theobserved random variable is a member of the exponential family conditional on a certain parameter.The main objective of the development of this model is to make inference on these parameters are alsoconsidered as random variables. Then these parameters are modeled with the Fay-Herriot model asthe basic model of the small area estimation. Furthermore, the combination of both models will bestandardized in such a way as to represent a model within the framework of Bayes methods that willeventually form a two-level hierarchical Bayes Poisson model to solve problems in small areaestimation. The results of the development of this model is implemented to estimate the infantmortality rate in Bandung district, West Java Province.
Co-Authors Aceng Komarudin Mutaqin Fitriana Novitasari Nusar Hajarisman Suliadi Suliadi