Kusman Sadik

Mengukur Indeks Kebahagiaan Mahasiswa IPB Menggunakan Analisis Faktor

Undergraduate students of Bogor Agricultural University are spread out in 9 Faculties and 1 School. The difference of faculties and schools illustrate the different characteristics and burdens of student lectures on each faculty and school. This distinction raises various assumptions about the level of student happiness in every faculty and school. Student happiness analysis is measured using loading factor obtained from Factor Analysis. Based on the analysis, found that Faculty of Animal Science is the happiest faculty with happiness index reaching 66.88 and the lowest index of happiness found in the Faculty of Human Ecology with happiness index of 62.39.

Cluster information of non-sampled area in small area estimation of poverty indicators using Empirical Bayes

Survey is one of data collection method which sampling of individual units from a population. However, national survey only provides limited information which impacts on low precision in small area level. In fact, when the area is not selected as sample unit, estimation cannot be made. Therefore, small area estimation method is required to solve this problem. One of model-based estimation methods is empirical Bayes which has been widely used to estimate parameter in small area, even in non-sampled area. Yet, problems occur when this method is used to estimate parameter of non-sampled area which is solely based on synthetic model which ignore the area effects. This paper proposed an approach to cluster area effects of auxiliary variable by assuming that there are similar among particular area. Direct estimates in several sub-districts in regency and city of Bogor are zero because no household which are under poverty in the sample that selected from these sub-districts. Empirical Bayes method is used to get the estimates are not zero. Empirical Bayes method on FGT poverty measures both Molina & Rao and information clusters have the same estimates in the sub-districts selected as samples, but have different estimates on non-sampled sub-districts. Empirical Bayes methods with information cluster has smaller coefficient of variation. Empirical Bayes method with cluster information is better than empirical Bayes methods without cluster information on non-sampled sub-districts in regency and city of Bogor in terms of coefficient of variation.Survey is one of data collection method which sampling of individual units from a population. However, national survey only provides limited information which impacts on low precision in small area level. In fact, when the area is not selected as sample unit, estimation cannot be made. Therefore, small area estimation method is required to solve this problem. One of model-based estimation methods is empirical Bayes which has been widely used to estimate parameter in small area, even in non-sampled area. Yet, problems occur when this method is used to estimate parameter of non-sampled area which is solely based on synthetic model which ignore the area effects. This paper proposed an approach to cluster area effects of auxiliary variable by assuming that there are similar among particular area. Direct estimates in several sub-districts in regency and city of Bogor are zero because no household which are under poverty in the sample that selected from these sub-districts. Empirical Bayes method is used to get...

A comparative study of approximation methods for maximum likelihood estimation in generalized linear mixed models (GLMM)

Maximum likelihood estimates in GLMM are often difficult to be obtained since the calculation involves high dimensional integrals. It is not easy to find analytical solutions for the integral so that the approximation approach is needed. In this paper, we discuss several approximation methods to solve high dimension integrals including the Laplace, Penalized Quasi likelihood (PQL) and Adaptive Gaussian Quadrature (AGQ) approximations. The performance of these methods was evaluated through simulation studies. The ‘true’ parameter in the simulation was set to be similar with parameter estimates obtained by analyzing a real data, particularly salamander data (McCullagh & Nelder, 1989). The simulation results showed that the Laplace approximation produced better estimates when compared to PQL and AGQ approximations in terms of their relative biases and mean square errors.

Small area estimation of per capita expenditures using robust empirical best linear unbiased prediction (REBLUP)

A small area is an area with small sample size to estimate parameters in survey sampling. The direct estimation will produce inaccurate estimation since the sample size is not enough to produce estimation with acceptable precision. Small Area Estimation (SAE) is a solution to obtain more precise estimation in a small area. A well-known method in SAE is an empirical best linear unbiased prediction (EBLUP). EBLUP is the estimator of small area means. It will provide an accurate estimation under normality assumptions but it can be sensitive when the data are contaminated by outliers. In this article, we discussed a resistant method in SAE, i.e. robust empirical best linear unbiased prediction (REBLUP). We apply REBLUP from unit-level models to the data obtained from the National Socio-economic Survey (SUSENAS). The means of per capita expenditures are calculated for all small areas. We compare the estimates of per capita expenditure in the small area using direct estimation, EBLUP and REBLUP methods using da...

Post-stratification sampling in small area estimation (SAE) model for unemployment rate estimation by Bayes approach

This research implemented unemployment rate in Indonesia that based on Poisson distribution. It would be estimated by modified the post-stratification and Small Area Estimation (SAE) model. Post-stratification was one of technique sampling that stratified after collected survey data. It’s used when the survey data didn’t serve for estimating the interest area. Interest area here was the education of unemployment which separated in seven category. The data was obtained by Labour Employment National survey (Sakernas) that’s collected by company survey in Indonesia, BPS, Statistic Indonesia. This company served the national survey that gave too small sample for level district. Model of SAE was one of alternative to solved it. According the problem above, we combined this post-stratification sampling and SAE model. This research gave two main model of post-stratification sampling. Model I defined the category of education was the dummy variable and model II defined the category of education was the area rando...

Small area estimation (SAE) model: Case study of poverty in West Java Province

This paper showed the comparative of direct estimation and indirect/Small Area Estimation (SAE) model. Model selection included resolve multicollinearity problem in auxiliary variable, such as choosing only variable non-multicollinearity and implemented principal component (PC). Concern parameters in this paper were the proportion of agricultural venture poor households and agricultural poor households area level in West Java Province. The approach for estimating these parameters could be performed based on direct estimation and SAE. The problem of direct estimation, three area even zero and could not be conducted by directly estimation, because small sample size. The proportion of agricultural venture poor households showed 19.22% and agricultural poor households showed 46.79%. The best model from agricultural venture poor households by choosing only variable non-multicollinearity and the best model from agricultural poor households by implemented PC. The best estimator showed SAE better then direct estimation both of the proportion of agricultural venture poor households and agricultural poor households area level in West Java Province. The solution overcame small sample size and obtained estimation for small area was implemented small area estimation method for evidence higher accuracy and better precision improved direct estimator.