ESTIMASI MAXIMUM LIKELIHOOD DAN RESTRICTED MAXIMUM LIKELIHOOD UNTUK MODEL LINEAR EFEK CAMPURAN

Linier mixed effects model (LME) is one of parametric linier regression for clustered, longitudinal, or repeated measures data that quantifies the relationship between a continous dependent variabel and independent variable. LME may include both fixed effects parameters and random effects. Estimation parameter of LME can be achieved using maximum likelihood estimation and restricted maximum likelihood method with multivariate normal distribution from the marginal model. Random effects coefficient from the model can be predicted using conditional expectation. The best model selection using the likelihood ratio test alongside with Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The best method estimation selection using mean square error. LME is applied to real data, the examination of pregnant woman in Community Health Center in Yogyakarta

PERBANDINGAN UJI T STATISTIK RATA-RATA TERBOBOT DENGAN RATA-RATA TIDAK TERBOBOT PADA DATA KEUANGAN: (Studi Kasus Yield Obligasi Muncipal dan Korporasi Amerika Serikat dengan Rating AAA

Means is technic to explanation the group or size in center set of data. Usually, a common statistical question arises when two random samples are taken from two different normal populations, possibly with different variances, and wish to know whether or not the means of the two populations are equal. To examine the similarity of the average, typically use t-test mean two samples. In this minithesis will describe about expansion of t-test mean two samples using tstatistic weighted means. This methode present a generalization of the two-sample t-test for equality of the means to the case where the sample values are to be given unequal weights. This is a natural situation in financial return modelling where some samples are considered more reliable than others in predicting a common mean. In this case show with an example of yield data that using the standard unweighted t-test can lead to the wrong statistical conclusion

PENENTUAN PREMI TUNGGAL BERSIH UNTUK KONTRAK ASURANSI JIWA ENDOWMEN UNIT LINK DENGAN GARANSI MINIMUM MENGGUNAKAN METODE POINT-TO-POINT

Unit-linked life insurance is an insurance product that combines elements of investment with life insurance. This paper discuss about benefit from unit linked endowment life insurance, started from stock price models followed by geometry brownian motion, constructing fair value from benefit which is will be gotten with minimum guarantee, build single premium form unit linked life insurance

METODE FUZZY ANALYTICAL HIERARCHY PROCESS DALAM PENGAMBILAN KEPUTUSAN PENYALURAN KREDIT (Studi Kasus Penyaluran Kredit pada PD. BPR BKK Kebumen Cabang Puring)

Bank is agency that assemble fund from people in saving and distribute it to people in credit or other form. As time goes on after the credit is realized, it can�t be denied that bank will facing the risk, that is problematical credit. For example, uncapability to pay interest and return credit at the time to maturity. Therefore, bank have to make dicision appropriately and effectively in credit distribution to prospective borrower. In relation, Analytical Hierarchy Process (AHP) can be used to help solve this problem. AHP is used when the taken decision involve many factors, that decision maker get difficulty in making weight to each factors. So that, AHP can solve a complex situation, unstructured into several component of hierarchy structure, with put on subjective value about the importance of each variable relatively, and decide what variable that has highest priority to influence the result on that situation. Although, the used of AHP in Multi Criteria Decision Making (MCDM) often get criticism because the uncapability of AHP approach to solve unprecisely factor that decision makers feel when give precise value in comparison matrix. Therefore, Fuzzy AHP method is developed to solve the existing weakness of AHP. It is merge of AHP and Fuzzy approach

ANALISIS DATA MINING CUACA MENGGUNAKAN ATURAN ASOSIASI DAN REGRESI LOGISTIK KERNEL

Knowledge about patterns and relationships plays an important role in agriculture policy making. The level of rainfall is one of the factors affect the productivity of plants for food commodities. Association rules and kernel logistic regression (KLR) are data mining techniques to assist in making a decision. Model utilization depicts a probability and finds the rules occur in large amounts of the data. The author conduct a study of the data obtained from the Indonesian Meteorological, Climatological and Geophysical - Badan Meteorologi, Klimatologi, dan Geofisika (BMKG) and Statistics Indonesia - Badan Pusat Statistik (BPS). This final project aims to give conclusion that the kernel logistic regressio