PENDEKATAN METODE BAYES UNTUK PENDUGAAN PENGARUH INTERAKSI PADA MODEL AMMI

Multi-locations trials play an important role in plant breeding and agronomic research. Study concerning genotype-environment interaction needed in selection of genotype to be released. AMMI (Additive Main effect and Multiplicative Interaction) is one of statistical technique to analyze data from multi-locations trials. The analysis of AMMI is a combining analysis between additive main effect and principal component analysis. Multi-location sampling data which were collected several years on several planting season used to be analyzed separately. To obtain more comprehensive information of multi-location sampling data, an analysis which combines all the information in several years is needed. One of the alternatives is the Bayesian approach. This method utilizes initial information on the estimated parameters and information from samples. The simulation states that prediction with Bayesian methods will produce a better estimator, because MSE of the Bayesian estimator is smaller the MSE estimator generated using least squares method. Keywords: AMMI, Baye

PENDEKATAN GENERAL LINEAR MIXED MODEL PADA SMALL AREA ESTIMATION

Small area estimation is commonly used to describe smaller domain or sub-population. Small area estimation is an important measuring instrument to estimate parameter of smaller domain borrowing strength of population parameter estimate through statistical models with random influence. In this paper we showed the contribution of statistical methods in small area estimation using general linear mixed models.   Keywords :       small area estimation,  general linear mixed mode

The Estimation of Price Sensitivity Curves Using Generalized Linear Models

The estimation of price sensitivity curves is usually based on historical data of the product. The estimates obtained, however, are potentially biased especially if the previous condition does not reflect the current market situation. alternativety, the estimation could be based on preference data. This paper introduces the use of Generalized Linear models to estimate the curve based on preference data

MODEL SPASIAL BAYES DALAM PENDUGAAN AREAKECIL DENGAN PEUBAH RESPON BINER

Model-model dalam pendugaan area kecil mengasumsikan bahwa pengaruh acak galat area saling bebas. Namun dalam beberapa kasus, asumsi ini sering dilanggar. Penyebabnya adalah keragaman suatu area dipengaruhi area sekitarnya, sehingga efek spasial dapat dimasukkan ke dalam pengaruh acak area. Rao (2003) menyatakan bahwa salah satu model dalam pendugaan area kecil yang dapat dipengaruhi oleh efek spasial adalah model Logit- Normal. Model tersebut digunakan untuk menduga proporsi melalui metode Bayes berhirarki (Hierarchical Bayes/BB). Tu.luan pertama dari penelitian ini adalah mengembangkan metode Bayes untuk data peubah respon biner dengan menambahkan efek spasial. Tujuan selanjutnya adalah membandingkan sifat-sifat statistik penduga proporsi Logit-Normal Bayes berhirarki dengan pembobot spasial tetangga terdekat (BB1) dan model Logit-Normal Bayes berhirarki tanpa pembobot spasial (BB2). Studi kasus dilakukan pada data simulasi. Hasil penelitian menunjukkan bahwa pengaruh spasial dapat memperbaiki pendugaan parameter pada area kecil yang diindikasikan dengan nilai menurunnya nilai Root mean Square Error/RNISE (29%). Bila dilihat darirata-rata persentase bias relatif (Rbias), BB1 memiliki nilai Rbias lebih kecil yaitu sebesar 33.36%o. sedangkan Rbias BB2 sebesar 44.54%. Sehingga dapat disimpulkan penduga proporsi Logit-Normal Bayes berhirarki dengan pembobot spasial tetangga terdekat lebih baik daripada penduga proporsi Logit-Normal Bayes berhirarki tanpa pembobot spasial

Nested Generalized Linear Model with Ordinal Response for Correlated Data

In this paper, we discuss the generalized linear models with ordinal response for correlated data in nested area. Some basic concepts are described, that is nested spatial, threshold model, and cumulative link function. Due to correlated data used for this modeling, Generalized Estimating Eequation (GEE) is used as model parameters estimation method. Nested is shown by the model building and its application on nested spatially data. In this method, some Working Correlation Matrices (WCM) are able to be specified depend on the nature and type of the data. In this study, 3 types of WCM and 2 types of parameters estimation covariance are used to see the results of parameters estimation from these combinations. As a conclusion, independent WCM is appropriate to the data