Judul: Pemodelan Kematian yang Diakibatkan oleh Pembunuhan dan Bunuh Diri di Lithuania pada Tahun 2003-2004
Abstrak: Many daily problems can be assumed as stochastic processes. Therefore stochastic processes are very important subjects. One of them is modeling the geographical variation of mortality risk. This paper considers numerical issues of the empirical Bayesian approach model applied to the low rate estimation. The condition for nonsingularity of Bayesian estimation is given and the convenient iterative algorithm for the estimation is described. The clustering algorithm is also developed. It uses the property of Poisson-Gaussian model to treat probabilities of events in populations being the same, if the variance of probabilities is small. If the population size is not taken into account, then the estimation of relative risk (RR), which is only based on a few cases to produce a map, is not good. Empirical Bayes estimation has been proven to have smaller squared error than that of RR estimator. Integrating and maximizing likelihood functions is done by using a mathematical software. This paper refers mainly to the paper of Sakalauskas (2010) entitled On the Empirical Bayesian Approach for the Poisson-Gaussian model.
Keyword: Poisson-Gaussian model, mortality modeling, Empirical Bayesian

Judul: Pemodelan area kecil untuk menduga angka kematian bayi melalui pendekatan model regresi poisson bayes berhirarki dua-leve
Abstrak: Hierarchical Bayes (HB) approach has been recently proposed for small area estimation problems. However, in this area, there is still a limited use of hierarchical Bayesian models though they have many advantages: (i) their specification is straightforward and allows to take into account the different sources of variation and (ii) inferences are clear-cut and computationally feasible in most cases by using standard Markov Chain Monte Carlo (MCMC) techniques. Within this approach, when the variable of interest is a count or a proportion, alternative model specifications can be considered. In the HB approach, a prior distribution on the model parameters is specified and the posterior distributrion of the parameters of interest is obtained. Inferences are based on the posterior distribution, in particular, a parameter of interestis estimated by its posterior mean and its precision is measured by its posterior variance. Even though the HB is straightforward, and HB inferences are clear-cut and „exact‟, but the HB requires the specification of a subjective prior ( ) on the model parameter . Priors on maybe informative or „diffuse‟. Informative priors are based on substantial prior information, such previous studies judged relevant to the current data set y. However, informative priors are seldom available in real HB application, particularly those related to public policy. This dissertasion analyses and discusses the structure of alternative models when the variable of interest is a count, both on a theoretical side as well as by a simulation study. Bayesian specifications derived from classical models for SAE, e.g. the Fay-Harriot model (Fay and Herriot, 1979), and then consider a generalized linear Poisson model. The combination of these two models has formed a model called the hierarchical Bayes Poisson regression model. Development of HB models that extend the Fay-Herriot models and generalized linear model by using two different priors distribution on the model parameter of interest. First, we used gamma distribution as a conjugate priors for Poisson likelihood, and second we used inverse gamma distribution which is non conjugate prior for Poisson likelihood. The proposed models are implemented using the Gibbs sampling method for fully Bayesian inference. We apply the proposed models to the analysis of infant mortality rate for sub-district level in Bojonegoro district, East Java Province. Based on the application result, we found that hierarchical Bayes Poisson regression model with inverse-gamma prior distribution gave the better prediction of infant mortality rate than model with gamma prior distribution. Our conclusion were made based on various criterion on diagnostic models, such as convergence of Markov chain diagnostic tests, a Bayesian measure of fit or adequacy, residual analysis, and other summary statistics (bias and mean square error).
Keyword: small area estimation, Fay-Herriot model, generalized linear models, Poisson distribution, Markov chain Monte Carlo, Gibbs sampling.

Judul: The effect of oil source combination to the quality of transparent soap
Abstrak: The oil selection as the fatty acid source will determine the transparent soap characteristic because each fatty acid type will gift the different soap characteristic. The use of oil combinations in one formula on the transparent soap made because the characteristic of the oil is not owned by itself but substitute by other oils. Moreover, the probability of the undesirable characteristics on the soap can be reduce until the minimum level to increase the quality of transparent soap. This research is to determine the effect of using the oil combinations on the certain composition, through the transparent soap quality product, to determine the panelist respond through the transparent soap product, to reach the best formula in transparent soap making by using more than one oil combination in one soap formula. The oil that used is coconut oil, RBDPO (refined bleached deodorized palm oil) and castor oil. The diverse analysis shows the different result significantly. The organoleptic test that has been done is to determine the panelist respond through the transparent soap product. Based on the chemicalphysic characteristic and organoleptic test, the best formulas are the transparent soap made from coconut oil and RDBPO with the comparison of 15:5. The sopa characteristic that being made are: water content and evaporate substance (12.13%), fatty acid content (33.41%), unsaponifiable fraction content (1.66%), insolulable in alcohol content alcohol (0.75%), free alkali content as NaOH (0.21%), pH (10.57), hardness (2.10 mm/second), emulsion stability (82.64%), foam stability (33.64%), and cleaning power (285.50 ftu turbidity).
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