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Spatial Econometric Model for Economics Development in Archipelago of Riau, as a Defense System Development in Republic of Indonesia Susanti Linuwih; Setiawan Setiawan; Dwiatmono A. W Dwiatmono A. W; Wiryadi Wiryadi
IPTEK The Journal for Technology and Science Vol 21, No 3 (2010)
Publisher : IPTEK, LPPM, Institut Teknologi Sepuluh Nopember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j20882033.v21i3.40

Abstract

Province of Archipelago of Riau is a region in Indonesia which is adjacent to Singapore and Malaysia. This province has a great potential conditions diversity and natural resources. Planning on public prosperity improvement is necessary in order to increase loyalty and nationalism to Republic of Indonesia. The aim of this research is to build a spatial econometric model of economic growth in Province of Archipelago of Riau. One of the results shows that in recent 4 years Batam always gives the largest contribution to GRDP in Province of Archipelago of Riau. This can be understood that the contribution is more than 72.0% not only based on GRDP at current prices, but also based on GRDP at constant prices. Economic growth rate in regions in Province of Archipelago of Riau is higher than national economic growth rate. The model fits well because the coefficient of determination R2 is more than 85%. There are only 3 worse models, i.e. based on building construction in Batam (with R2= 59.6%), in Tanjungpinang (with R2=74.0%), and based on transportation and communication in Tanjungpinang (with R2=37.1%).
Outlier Detection in Observation at Multivariate Linear Models with Likelihood Displacement Statistic-Lagrange Method Makkulau Makkulau; Susanti Linuwih; Purhadi Purhadi; Muhammad Mashuri
Jurnal ILMU DASAR Vol 12 No 1 (2011)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (200.54 KB)

Abstract

There are two different outliers, i.e outlier in observations and outlier in models. The existing outlier detection method in models is using common Likelihood method. The limitation of this method is the optimal value produced might be not the real optimal values. This research yields a method for outlier detection in multivariate linear models with Likelihood Displacement Statistic-Lagrange method (LDL method). This method uses multiplier Lagrange with constraint the confidence interval of parameter’s vector. This parameter’s vector is obtained from the data set which is outlier free. This parameter estimation process uses numerical method with Karush-Kuhn Tucker condition in nonlinear programming. This method compares between LDL value and the table F value that follows the distribution of F value to indentify the outlier in models.
RANCANGAN 2K , 2K-L FAKTORIAL YANG OPTIMALPADA MODEL PERMUKAAN MULTIRESPON ORDE SATU Purhadi Purhadi; Suryo Guritno; Susanti Linuwih
STATISTIKA: Forum Teori dan Aplikasi Statistika Vol 4, No 2 (2004)
Publisher : Program Studi Statistika Unisba

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29313/jstat.v4i2.895

Abstract

parameter pada model permukaan multirespon yang bersifat tidak bias, konsisten dan efisien. Kriteria lain agar matrikrancangan percobaan optimal adalah variansi dari penaksir respon-responnya bernilai minimum. Beberapa rancanganpercobaan model orde satu yaitu rancangan Faktorial, Fraksional faktorial, Simplek dan Placket Burman. Denganmenggunakan pembobotan pada titik-titik percobaan sehingga memenuhi kriteria optimum-D, A, E maka didapatkanmatrik rancangan percobaan yang optimal untuk model permukaan multirespon orde satu. Dengan mengunakan ketigakriteria tersebut didapat hasil nilai determinan matrik informasi yang hampir sama. Eff-D digunakan untukmembandingkan beberapa rancangan percobaan.Apabila penambahan titik-titik percobaan dilakukan hal ini dapat secara proposional sesuai dengan nilai pembobotannyasehingga rancangan percobaan masih optimal. Hal diatas bisa juga dilakukan dengan cara menerapkan Algoritma Fedorovatau Algoritma Fedorov yang dimodifikasi jika matrik variansi kovariansi dari error tidak diketahui.