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INDONESIA
FORUM STATISTIKA DAN KOMPUTASI
Published by Institut Pertanian Bogor
ISSN : 08538115     EISSN : -     DOI : -
Core Subject : Education,
Mathematics
Forum Statistika dan Komputasi (ISSN:0853-8115) was published scientific papers in the area of statistical science and the applications. It is issued twice in a year. The papers should be research papers with, but not limited to, following topics: experimental design and analysis, survey methods and analysis, operation research, data mining, statistical modeling, computational statistics, time series and econometrics, and statistics education.
Arjuna Subject : -
Articles 4 Documents
 1 
Search results for , issue "Vol. 18 No. 1 (2013)" : 4 Documents clear
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
SURVIVAL ANALYSIS OF CUSTOMER IN POSTPAID TELECOMMUNICATION INDUSTRY Doni Suhartono; Asep Saefuddin; I Made Sumertajaya
FORUM STATISTIKA DAN KOMPUTASI Vol. 18 No. 1 (2013)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Currently, the business competition in mobile telecommunication industry among providers in Indonesia is tighter and it has given rise to a phenomenon of customer defection which has serious consequences for the business performance. In the current circumstances, customers are faced numerous options to be selected that probably cause them at risk to get churn. Therefore, it becomes one of the challenges encountered by Division of Loyalty and Retention to makes the efforts of decreasing customer defection. So that it is important conducting a model of churn practically applied to predict tendency of customer churn and also recognizing the prognostic factors influence customer churn. Survival analysis modelling, such as Cox’s proportional hazard model, was very successful in previous research, which investigatedthe relationship between survival time and possible prognostic factors. Based on the research, Cox’s proportional hazard model of customer lifetime is effective to distinguish relative risk between churn customers and others, and also between which loyal customers and with other short time customers with their significant prognostic factors. Afterwards the simulation of the survival probability estimated over time with particular possible combination of the most significant characteristics affecting tendency of churn, are able to predict such information of lifetime to churn event and compare the survival performance of one another. Finally, the results of this research is able to yield simple, helpful and applicable results as the principle of taking decission for optimizing their customer retention and/or treatment resources in their customer retention efforts for the company.Key words : Churn, Cox’s proportional hazard model, customer retention, survival analysis and telecommunication industry.
PENDEKATAN KUADRAT TERKECIL PARSIAL KEKAR UNTUK PENANGANAN PENCILAN PADA DATA KALIBRASI Enny Keristiana Sinaga; Anik Djuraidah; Aji Hamim Wigena
FORUM STATISTIKA DAN KOMPUTASI Vol. 18 No. 1 (2013)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

The serious problems in the calibration of multivariate estimation are multicollinearity and outliers. Partial Least Squares (PLS) is one of the statistical method used in chemometrics, to handle high or perfect multicollinearity in independent variables. Straightforward Implementation Partial Least Squares (SIMPLS) is the extension of PLS regression proposed by De Jong (1993). The SIMPLS algorithm is based on the empirical cross-variance matrix between the independent variables and the regressors. This method does not resistant toward outlier observations. Robust PLS method is used to handle the multicollinearity and outliers in the data sets. This method can be classified in two groups, there are iteratively reweighting technique and robustication of covariance matrix. Partial Regression-M (PRM) method is one of the robust PLS methods used the idea of iteratively reweighting technique that proposed by Serneels et al. (2005). Robust SIMPLS (RSIMPLS) method is one of the robust PLS methods used the idea of robustication of covariance that proposed by Huber and Branden (2003). A modified RSIMPLS used M estimator with the Huber weight function called RSIMPLS-M was proposed by Ismah (2010). These two methods (RSIMPLS-M and PRM) are applied to Fish data (Naes 1985) to know their performances. The research results indicated that the values of R2 and RMSEP of RSIMPLS-M are higher than those of PRM method. Whereas based on the confidence interval estimation of the regression coefficients by jackknife method, estimation of PRM is narrower than that RSIMPLS-M method. Therefore RSIMPLS-M method is better than PRM method for prediction, whereas PRM method is better than RSIMPLS-M method for estimation.Keywords : Partial least squares regression robust (PLSRR), partial robust M-regression (PRM), straightforward implementation partial least squares robust (RSIMPLS)
BOOTSTRAP PARAMETRIK DAN NONPARAMETRIK UNTUK PENDUGAAN KUADRAT TENGAH GALAT DALAM STATISTIK AREA KECIL DENGAN RESPON BERSEBARAN LOGNORMAL Cempaka Putri; Khairil Anwar Notodiputro; La Ode Abdul Rahman
FORUM STATISTIKA DAN KOMPUTASI Vol. 18 No. 1 (2013)
Publisher : FORUM STATISTIKA DAN KOMPUTASI

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Abstract

Small area estimation is needed to obtain information in small area, that is area containing small size of sample. Direct estimation in small area will result in large variance. Indirect estimation is the solution, with involves auxiliary data from related area or another survey in parameter estimation. One of the prolems found in using this procedure is low precision of Mean Square Error (MSE) estimate caused by non-normal distribution. Parameter of concern in this study is per capita expenditure of village in Bogor regency. Per capita expenditure is non-normal distribution. MSE estimator with bootstrap method has the advantage of potential robustness against sampling from non- normal distribution. Therefore this study used bootstrap method, such as parametric bootstrap and nonparametric bootstrap, in MSE estimation. Generally, the result showed that the MSE estimate of the parametric bootstrap smaller than the nonparametric bootstrap. Both method have better precision, so that they can repair the result of direct estimation.Keywords : small area estimation, parametric bootstrap, nonparametric bootstrap

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