Nur Iriawan

Bayesian mixture model averaging for identifying the different gene expressions of chickpea (Cicer arietinum) plant tissue

ABSTRACT Identification of different gene expressions of chickpea (Cicer arietinum) plant tissue is needed in order to develop new varieties of chickpea plant which is resistant to disease through the insertion of genes. This plant is the third legume plant of the Leguminosae (Fabaceae) family and is much needed in the world due to its high-protein seeds and roots that contain symbiotic nitrogen-fixing bacteria. This paper has succeeded to demonstrate the work of Bayesian mixture model averaging (BMMA) approach to identify the different gene expressions of chickpea plant tissue in Indonesia. The results show that the best BMMA normal models contain from 727 (73%) up to 939 (94%) models from 1,000 generated mixture normal models. The fitted BMMA models to gene expression differences data on average is 0.2878511 for Kolmogorov–Smirnov (KS) and 0.1278080 for continuous rank probability score (CRPS). Based on these BMMA models, there are three groups of gene IDs: downregulated, regulated, and upregulated. The results of this grouping can be useful to find new varieties of chickpea plants that are more resistant to disease. The BMMA normal models coupled with Occams window as a data-driven modeling have succeed to demonstrate the work of building the gene expression differences microarray experiments data.

On The Value at Risk Using Bayesian Mixture Laplace Autoregressive Approach for Modelling the Islamic Stock Risk Investment

Stocks are known as the financial instruments traded in the capital market which have a high level of risk. Their risks are indicated by their uncertainty of their return which have to be accepted by investors in the future. The higher the risk to be faced, the higher the return would be gained. Therefore, the measurements need to be made against the risk. Value at Risk (VaR) as the most popular risk measurement method, is frequently ignore when the pattern of return is not uni-modal Normal. The calculation of the risks using VaR method with the Normal Mixture Autoregressive (MNAR) approach has been considered. This paper proposes VaR method couple with the Mixture Laplace Autoregressive (MLAR) that would be implemented for analysing the first three biggest capitalization Islamic stock return in JII, namely PT. Astra International Tbk (ASII), PT. Telekomunikasi Indonesia Tbk (TLMK), and PT. Unilever Indonesia Tbk (UNVR). Parameter estimation is performed by employing Bayesian Markov Chain Monte Carlo (MCMC) approaches.

Bayesian mixture modeling for blood sugar levels of diabetes mellitus patients (case study in RSUD Saiful Anwar Malang Indonesia)

Bayesian statistics proposes an approach that is very flexible in the number of samples and distribution of data. Bayesian Mixture Model (BMM) is a Bayesian approach for multimodal models. Diabetes Mellitus (DM) is more commonly known in the Indonesian community as sweet pee. This disease is one type of chronic non-communicable diseases but it is very dangerous to humans because of the effects of other diseases complications caused. WHO reports in 2013 showed DM disease was ranked 6th in the world as the leading causes of human death. In Indonesia, DM disease continues to increase over time. These research would be studied patterns and would be built the BMM models of the DM data through simulation studies where the simulation data built on cases of blood sugar levels of DM patients in RSUD Saiful Anwar Malang. The results have been successfully demonstrated pattern of distribution of the DM data which has a normal mixture distribution. The BMM models have succeed to accommodate the real condition of the DM data based on the data driven concept.

Development of reversible jump Markov Chain Monte Carlo algorithm in the Bayesian mixture modeling for microarray data in Indonesia

In the Bayesian mixture modeling requires stages the identification number of the most appropriate mixture components thus obtained mixture models fit the data through data driven concept. Reversible Jump Markov Chain Monte Carlo (RJMCMC) is a combination of the reversible jump (RJ) concept and the Markov Chain Monte Carlo (MCMC) concept used by some researchers to solve the problem of identifying the number of mixture components which are not known with certainty number. In its application, RJMCMC using the concept of the birth/death and the split-merge with six types of movement, that are w updating, θ updating, z updating, hyperparameter β updating, split-merge for components and birth/death from blank components. The development of the RJMCMC algorithm needs to be done according to the observed case. The purpose of this study is to know the performance of RJMCMC algorithm development in identifying the number of mixture components which are not known with certainty number in the Bayesian mixture model...

Model criticism for log-normal hierarchical Bayesian models on household expenditure in Indonesia

Hierarchical models are formulated for analyzing data with complex sources of variation. In many cases, those complex sources of variation refer to hierarchical structure of data. Since, the hierarchical modeling process takes into account the characteristics of each data level, it leads to a complex model. Commonly, the issues of interest are how well the model fit the data and how well the random effects fit their assumed distribution. In that case, the problem is often viewed on hierarchical Bayesian modeling is confounding across level which means whether the problem comes due to mis-specification of likelihood on the lowest level of mis-specification prior on higher level. In general, there are two different proposed methods for Bayesian model criticism, i.e. Bayes factors and Deviance Information Criterion (DIC). However, there is practical and theoretical limitation of Bayes factors due to complexity of model. This paper proposes to discuss and generate a Bayesian predictive model criticism based on trade off between model fit and complexity through DIC and graphs for two alternative Lognormal hierarchical Bayesian models on household expenditure data. Result shows that there is a slightly different result between the two-parameter log-normal hierarchical model and the three-parameter log-normal hierarchical model. However, the three-parameter log-normal hierarchical model yields a better fit and a bit lower complexity compare to the two-parameter Log-Normal hierarchical model.

Hybrid state-space model and adjusting procedure based on Bayesian approaches for spatio-temporal rainfall disaggregation

Disaggregation is the transforming process from highlevel scale data into low-level which preserves the consistency of the high-level statistic characteristics. This process, considering the dependence between spatial and temporal, is known as the spatio-temporal disaggregation. In general, this method is divided into two stages, namely the data modeling and preserving of consistency the high level scale statistic characteristics. This study proposes a hybrid model that combines a state-space model and adjusting procedure to disaggregate spatio-temporal rainfall through Bayesian approach using WinBUGS. The results show that the generated hourly rainfall data are consistent with the observed daily rainfall data at some locations which have only the daily rainfall data in the watershed Sampean, Bondowoso, Indonesia.