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LASSO : SOLUSI ALTERNATIF SELEKSI PEUBAH DAN PENYUSUTAN KOEFISIEN MODEL REGRESI LINIER Agus Mohamad Soleh; _ Aunuddin
FORUM STATISTIKA DAN KOMPUTASI Vol. 18 No. 1 (2013)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

A new method, known as LASSO, has recently developed for selections and shrinkage linear regression methods. The method gives an alternative solution on high correlated data between independent variables, where the least squares produces high variance. Based on simulation this method is not better than forward selection (in the case the parameters contains many zero values) and ridge regression (in the case all parameter values close to zero). Unknowing the true parameter and consistency estimates for all conditions that put the LASSO is better than ridge or forward selection.Keywords : LASSO, least square, forward selection, ridge, cross validation
PENDEKATAN KEKAR UNTUK MODEL BERSAMA (JOINT MODEL) ATAS DASAR SEBARAN t (A Robust Approach for Joint Model Based on t Distribution) _ Indahwati; _ Aunuddin; Khairil Anwar Notodiputro; I Gusti Putu Purnaba
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 1 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Existing methods for joint modeling are usually based on normality assumption of random effects and intra subject errors. We propose a joint model based on t distribution of the intra subject errors  to improve robustness of the estimation. Our model consists of two submodels: a mixed linear mixed effects model for the longitudinal data, and a generalized linear model for continuous/binary primary response. The proposed method is evaluated by means of simulation studies as well as application to HIV data. Keywords:  joint modeling, longitudinal data, robust, t distribution
PEMODELAN KASUS DEMAM BERDARAH DENGUE DI JAWA TIMUR DENGAN MODEL POISSON DAN BINOMIAL NEGATIF (Dengue Fever Case Modelling in East Java with Poisson and Negative Binomial Models) Theresia M D N L Tobing; _ Aunuddin; La Ode Abdul Rahman
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 2 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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The total number of dengue fever victims in East Java can be assumed to have a Poisson distribution. The Poisson regression method can be used to model the relationship of the environmental factors and dengue fevers incidents. The model of this method assumes equidispersion, that is the equality of mean and variance of the response variables. If variance of the response variable exceeds the mean, it is called overdispersion. Negative binomial regression model is used to overcome the overdispersion. Negative binomial regression model shows that the quantity of dengue fever victims in every kabupaten (district) is influenced by the quantity of flood and the quantity of malnutrition victims. Negative binomial regression shows that the increasing number of flood will enhance the quantity of dengue fever victims in East Java district whereas the increasing quantity of malnutrition victims will enhance the quantity of dengue fever victims in East Java district. Keywords : Poisson regression, negative binomial regression, overdispersion
PENERAPAN PEMBOBOTAN KOMPONEN UTAMA UNTUK PEREDUKSIAN PEUBAH PADA ADDITIVE MAIN EFFECT AND MULTIPLICATIVE INTERACTION (Application of Weighted Principal Component for Variable Reduction in Additive Main Effect and Multiplicative Interaction) Geri Zanuar Fadli; _ Aunuddin; Aji Hamim Wigena
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 2 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Indonesia is the country with the largest level of rice consumption in the world. Therefore, it need to be done an effort to increase the production of rice. One way to increase rice production is land management as well as conducting an intensive new superior varieties which has a high yield. Hybrid rice is a type of rice which has a higher result among superior varieties. Hybrid rice breeding can be done with multi-locations trials that involves two main factors, plant and environmental conditions. AMMI (Additive Main Effects and Multiplicative Interaction) is a method of multivariate used in plant breeding research to examine the interaction of genotype × environment on multi-locations trials. Generally, AMMI analysis is still using a single response. Whereas, the adaptation level of the plant is not only seen from the aspect of its yield. Therefore, this study based on combined response using AMMI analysis. The Data in this study is secondary data multi-locations trials on hybrid rice planting season 2008/2009 which involved four sites and 12 genotype. The measured response are = yield (ton/ha), = 1000 grain weight (gram), = the number of penicles per m2, dan  = length of penicle (cm). The merger of response using weighted method by principal component. AMMI analysis with  as response produce five stable genotypes in any location, that are IH804, IH805, IH806, Hibrindo, and Ciherang. AMMI is also generating specific genotypes are those that perform good adaptability at certain environment condition. IH802, IH803, and IH809 genotypes in Jember planting season 2, IH808 and Maro genotypes in Ngawi. Keywords : AMMI, the merger of response, weighted principal component method
MODEL REGRESI BINOMIAL NEGATIF TERBOBOTI GEOGRAFIS UNTUK DATA KEMATIAN BAYI (Studi Kasus 38 Kabupaten/Kota di Jawa Timur) (Geographically Weighted Negative Binomial Regression for Infant Mortality Data) (Case Study 38 Regency/City in East Java) Lusi Eka Afri; _ Aunuddin; Anik Djuraidah
FORUM STATISTIKA DAN KOMPUTASI Vol. 17 No. 2 (2012)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Negative binomial regression model is used to overcome the overdispersion in Poisson regression model. This model can be used to model the relationship of the infant mortality and the factors incidence. Geographical conditions, socio cultural and economic differ one of location another location causes the factors that influence infant mortality is different locally. Geographically Weighted Negative Binomial Regression (GWNBR) is one of methods for modeling that count data have spatial heterogeneity and overdispersion. The basic idea of this model considers of geography or location as the weight in parameter estimation. The parameter estimator is obtained from Iteratively Newton Raphson method. This research will determine the factors that influence infant mortality. GWNBR model with a weighting adaptive bi-square kernel function classifies regency/city in East Java into 16 groups based on the factors that significantly influence the number of infant mortality. This model is better used to analyze the number of infant mortality in East Java in 2008 due to a smallest deviance value.Keywords : Negative binomial regression, geographically weighted negative binomial regression, adaptive bi-square, overdispersion
Co-Authors Agus Mohamad Soleh Aji Hamim Wigena Anik Djuraidah Geri Zanuar Fadli I Gusti Putu Purnaba Indahwati Laksono Trisnantoro Lusi Eka Afri Theresia M D N L Tobing