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All Journal Jurnal Ilmu Komputer dan Informasi Jurnal Ilmu Dasar Statistika Jurnal Buana Informatika Jurnal Matematika & Sains CCIT (Creative Communication and Innovative Technology) Journal JURNAL SAINS DAN TEKNOLOGI MARITIM Jurnal Ilmu Administrasi: Media Pengembangan Ilmu dan Praktek Administrasi
Suryo Guritno
Jurusan Matematika, Universitas Gadjah Mada
Astronomy Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Mathematics Mechanical Engineering Social Sciences Transportation

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Journal : Jurnal Ilmu Dasar

 1 
Ordinal Regression Model using Bootstrap Approach Bambang Widjanarko Otok; M. Sjahid Akbar; Suryo Guritno; Subanar Subanar
Jurnal ILMU DASAR Vol 8 No 1 (2007)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

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Abstract

The aim of the research content three part, thus a to know misclassification and model discriminant analysis with bootstrap approach, model regression ordinal with bootstrap approach, and model MARS with bootstrap approach. The data used is data of secondary related to matrix variance covariance is same and unequal that is (The data worker standard of living and banking performance). The result of this research shows that in determining distinguishing variable between groups there are difference of variable at each method. This matter because of at each method has specification either from fulfilled of assumption and also estimation its. So also at accuracy of classification between groups there is difference especially at matrix of variance covariance unequal at worker standard of living case. As a whole can be concluded that the problem accuracy of classification bootstrap approach at each method give small mistake of goodness at matrix variance covariance unequal and equal.Keywords: classification, bootstrap, discriminant analysis, ordinal regression, MARS.
Estimating Parameters of Logit Model on Multivariate Binary Response Using Mle and Gee Jaka Nugraha; Suryo Guritno; Sri Haryatmi
Jurnal ILMU DASAR Vol 10 No 1 (2009)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

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Abstract

In this paper, we discuss binary multivariate response modeling based on extreme value distribution. Independent variables used in these models are some attributes of the alternative (labeled Zijt) and some attributes of the decision maker (labeled Xi). We assumed that n the decision maker observed with T response. Yit is tnd response variables from decision maker i and value Yit is binary. Response of decision maker i can be expressed as Yi = (Yi1,...,YiT). In each of the decision maker, we have data (Yi, Xi, Zi). Models are derived by the assumption that maximum random utility which the decision maker i choose one of the alternatives having greatest utility. Methods of parameter estimation are Maximum Likelihood Estimator (MLE) method and Generalized Estimating Equation (GEE). First discussion in this study is the estimation by MLE with independent assumption among response and then the MLE estimation using joint distribution by Bahadur’s representation. By MLE and GEE, estimating equations are obtained and solved by numerical (like’s Newthon-Rahpson method) in the condition that not all of the parameters of individual attributes can be estimated (identified). Based on testing simulation data with R.2.5.0, we recommend (a) in low correlation, GEE is better than MLE (b) in moderate correlation, MLE is most efficient but not stable (c) in high or moderate correlation, MLE and GEE should be used (d) correlation estimators cannot explain the real correlation because of its bias.
Statistical Inference for Modeling Neural Network in Multivariate Time Series Dhoriva Urwatul Wutsqa; Subanar Subanar; Suryo Guritno; Zanzawi Soejoeti
Jurnal ILMU DASAR Vol 9 No 1 (2008)
Publisher : Fakultas Matematika dan Ilmu Pengetahuan Alam Universitas Jember

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Abstract

We present a statistical procedure based on hypothesis test to build neural networks model in multivariate time series case. The method involved strategies for specifying the number of hidden units and the input variables in the model using inference of R2 increment. We draw on forward approach starting from empty model to gain the optimal neural networks model. The empirical study was employed relied on simulation data to examine the effectiveness of inference procedure. The result showed that the statistical inference could be applied successfully for modeling neural networks in multivariate time series analysis.
Co-Authors Agus Suharsono Ahmad Ashari Ahmad Asharit Bambang Widjanarko Otok Bernard Renaldy Suteja Dhoriva Urwatul Wutsqa Heni Pujiastuti Jaka Nugraha M. Sjahid Akbar Purhadi Purhadi Retantyo Wardoyo Sri Haryatmi Sri Haryatmi subanar subanar Susanti Linuwih Zanzawi Soejoeti