GENERALIZED VARIANCE FUNCTIONS FOR BINOMIAL VARIABLES IN STRATIFIED TWO-STAGE SAMPLING

      This empirical study evaluates the application of Generalized Variance Functions (GVFs) for binomial variables in the 1998 Indonesian Labor Force Survey. The survey employs stratified two-stage cluster sampling for selecting samples from a population of households. The study covers all provinces in Java to produce estimates at the level of Java Island. The relative variance estimates resulted from the GVF models are compared to the relative variance estimates which are computed directly. The results illustrate that  model  expressed by logarithmic model  log = log c + d log () gives a good approximation to estimate the variances for the nonagricultural employment group, especially for working male category both in urban and rural areas. It is also good for the total employment group differentiated by age group, educational attainment, and employment status. On the other hand, the model gives poor results for the agricultural employment group. Based on the empirical results, the GVF models may not perform particularly well for the common characteristics which have relatively dissimilar deff values to majority of characteristics in the same group, since these characteristics usually come out among all persons in the sample household and often among all households in the sample cluster as well. The success of the GVF technique depends critically on the grouping of the estimates total () and amount of characteristics involved as the observations for fitting the model. Furthermore, observations with relatively large residuals will also determine the performance of goodness-of-fit of the model. Application of GVF technique to obtain an approximate standard error on numerous binomial characteristics in large scale survey should be carried out further using extensive data. The better performance of GVF model may also be accomplished by utilizing, for examples, weighted least squares procedure or robust regression method. Additionally, the data users should be warned that there will inevitably be survey characteristics for which GVF's will give poor results or even no GVF will be appropriate. Keywords :  Generalized Variance Functions, Stratified Two-Stage Samplin

OPTIMASI PENENTUAN LOKASI STASIUN PEMANTAU KUALITAS UDARA AMBIEN DI KOTA SURABAYA

The ambient air quality monitoring system in Surabaya has five fixed monitoring stations. Monitoring provides important information for public, but is expensive to purchase, utilize, and maintain. Based on result from spatial prediction of spatio-temporal additive model for air pollutant PM10, it is necessary  to move the  existing monitoring stations at other locations. In this study, we develop a methodology for reallocation of existing monitoring network to find an optimal configuration. The result of reallocation shows that new location of monitoring network can increase the accuracy of spatial prediction,  especially at area with high concentration of PM10   Key words::  spatio-temporal data, spatio-temporal additive model, spatial prediction,  reallocation monitoring networ

Penerapan Multivariate Cusum Time Series untuk Mendeteksi Kegagalan Bank di Indonesia

Bank merniliki peran penting dalam pengalokasian sumberdaya keuangan. Kondisi bank yangtidak sehat dapat menyebabkan bank tidak dapat menjalatzkan peran tersebut, sehingga akanmenghanlbat kelancaran akt$tas perekonomian nasional. Dalam mengevaluasi kinerja bank,beberapa pendekatan metodologi terutama metodologi statistik telah banyak dilakukan. Nalnunselama ini nzetodologi tersebut tidak mengikutsertakan perilaku deret waktu dari peubah-;7eubahnya. Padahal peubah-peubah keuangan suatu perusahaan secara serial berkorelasi tinggi.Tulisan ini bertujuan untuk mendeteksi kegagalan bank dengan menggunakan multivariatecztsunz tiine series.Model kegagalan bank yang dibangun oleh multivariate clrsunz time series, cukup mampu dalanzrnendeteksi adanya gejala memburuk pada kondisi kesehatan bank. Hal ini sejalan dengansenzangat pendeteksian krisis perbankan secara dini (early warning banking crises).Kata kunci : Multivariate Cusum Time Series, Kegagalan Ban

LASSO : SOLUSI ALTERNATIF SELEKSI PEUBAH DAN PENYUSUTAN KOEFISIEN MODEL REGRESI LINIER

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 validatio

KAJIAN RESPONS PEUBAH TERHADAP BERBAGAI GUNCANGAN DALAM SISTEM PEMBENTUK PDB TANAMAN BAHAN MAKANAN MELALUI MODEL VECTOR AUTOREGRESSION

It has been known that Gross Domestic Product (GDP) is one of the tools which can be used to measure the agriculture sector's performance. Since the agriculture's GDP is influenced by food crops' GDP, the measurement of agriculture's GDP is based on production value of the prior commodities of food crops such as rice and corn. Production is influenced by harvested area, yield, producer's price of commodities, and price of input factors such as fertilizers and pesticides. Vector Autoregression (VAR) model is one of the multivariate model that used to examine the relationship among variables which affect food crop's GDP. VAR can also be used for giving information about behavior of the variables in response to the various shocks.   The objective of the study is to examine the response of a variable to the various shocks of the other variables by using response impulse function of VAR model. The study shows that harvested area significantly affects food crops' GDP on all term, but commodity prices and input factors prices just affect on short term. The results also show that inflations in agriculture sector will increase farmer's income and production effectively but the inflations are affected by input factor prices.   Key words:: Food Crops' GDP, Vector Autoregression, Response  Impulse Functio

PEMILIHAN MODEL REGRESI LINIER MULTILEVEL TERBAIK

Linear regression models is used to describe relationship between dependent variable and independent variables. In a survey research, data was used often have hierarchical structure or nested structure. In this research, independent variables can be defined at any level of the hierarchy but dependent variable can only be defined at the lowest level of the hierarchy. Multilevel regression models is one of the methods can be used to analyze this data. Some authors purpose many models can be used to analyze data with hierarchical structures. Deviance as -2 log likelihood was defined as the measure goodness of fit. The difference of the deviance for two nested models was a method for comparing that two models

PENELUSURAN NILAI KORELASI PADA PROSES PRODUKSI TEPUNG BAKU SEMEN

Penelitian mengenai pengendalian mutu terhadap proses produksi tepung baku semen telah dilakukan sebelumnya. Penelitian tersebut menyimpulkan bahwa proses produksi tidak terkendali karena adanya perpindahan blok pada saat penambangan bahan baku semen (Puspitasari 2005). Selain itu, penelitian tersebut juga menyebutkan bahwa nilai korelasi antar karakteristik mutunya terlalu kecil sehingga selain penggunaan bagan kendali peubah ganda, penggunaan bagan kendali peubah tunggal juga n bagan kendali peubah ganda  proses tidak terkendali pada data awal i peubah tunggal bisa digunakan pada penelitian ytersebisa digunakan pada penelitian tersebut. Dalam tulisan ini akan dilihat lebih lanjut mengenai struktur korelasi yang terjadi antar karakteristik mutunya. Hasil penelusuran nilai korelasi antar karakteristik mutu pada kondisi awal, kondisi proses tidak terkendali dan kondisi proses terkendali menunjukkan adanya perubahan jika dibandingkan satu sama lainnya. Namun besarnya perubahan nilai tersebut relatif kecil, dan jika dilihat dari kedekatannya dapat dikatakan bahwa nilai korelasi pada saat kondisi proses terkendali lebih dekat dengan nilai korelasi pada saat kondisi proses tidak terkendali   Kata kunci: Korelasi, Tepung baku semen, Karakteristik mut

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)

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, overdispersio

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)

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 metho