Andi Kresna Jaya
Universitas Hasanuddin

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Bayesian Conditional Autoregressive (CAR) dengan Model Localised dalam Menaksir Risiko Relatif DBD di Kota Makassar Rusydah Khaerati; Andi Kresna Jaya
ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 1, Januari, 2020 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (714.809 KB) | DOI: 10.20956/ejsa.v1i1.9298

Abstract

Bayesian Conditional Autoregressive (CAR) is used in disease mapping because it is able to model relative risks by taking into account the smoothing of the estimated relative risk and entering spatial information to reduce the errors of the estimated relative risk parameters so that a more reliable relative risk model is obtained. In this study, the relative risk value of the spread of dengue fever will be calculated using Bayesian CAR with the localised model. These results were obtained using the OpenBUGS program and are illustrated in the 2016 dengue fever case data. Based on the model, mapping of dengue fever in Makassar can be identified in each district and shows that Makassar City is still very vulnerable to dengue fever.
Pemodelan Regresi Logistik Menggunakan Metode Momen Diperumum Grace Oktavia Yusuf; Andi Kresna Jaya; Nirwan Ilyas
ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 2, Juli, 2020 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (342.836 KB) | DOI: 10.20956/ejsa.v1i2.9304

Abstract

Regresi logistik merupakan model regresi yang sering digunakan dalam pemodelan data kategori, namun dalam menentukan modelnya terkadang tidak dapat diselesaikan dengan cara biasa dikarenakan variabel respon yang bersifat kategorikal mengikuti distribusi bernoulli. Sehingga dalam menentukan model diperlukan suatu estimasi parameter untuk  mendapatkan informasi mengenai parameter populasi. Metode momen diperumum (Generalized method of moments/GMM) adalah salah satu metode estimasi parameter yang digunakan untuk mengeksploitasi informasi bentuk kondisi momen populasi yang merupakan perluasan dari metode momen. Dari penggunaan estimasi parameter GMM diperoleh bahwa dengan menggunakan kondisi momen yang sama dengan metode momen pada umumnya menghasilkan estimasi yang sama dengan metode momen ataupun dengan estimasi OLS. Dalam mengestimasi parameter regresi logistik pun diperlukan suatu algoritma untuk menyelesaikan bentuk nonlinear-nya, sehingga digunakan iterasi Reweighted least square yang pembobotnya berubah setiap pengiterasian.Kata Kunci: Regresi Logistik Biner, Metode Momen Diperumum, Iterasi Reweighted Least Square.Regresi logistik merupakan model regresi yang sering digunakan dalam pemodelan data kategori, namun dalam menentukan modelnya terkadang tidak dapat diselesaikan dengan cara biasa dikarenakan variabel respon yang bersifat kategorikal mengikuti distribusi bernoulli. Sehingga dalam menentukan model diperlukan suatu estimasi parameter untuk  mendapatkan informasi mengenai parameter populasi. Metode momen diperumum (Generalized method of moments/GMM) adalah salah satu metode estimasi parameter yang digunakan untuk mengeksploitasi informasi bentuk kondisi momen populasi yang merupakan perluasan dari metode momen. Dari penggunaan estimasi parameter GMM diperoleh bahwa dengan menggunakan kondisi momen yang sama dengan metode momen pada umumnya menghasilkan estimasi yang sama dengan metode momen ataupun dengan estimasi OLS. Dalam mengestimasi parameter regresi logistik pun diperlukan suatu algoritma untuk menyelesaikan bentuk nonlinear-nya, sehingga digunakan iterasi Reweighted least square yang pembobotnya berubah setiap pengiterasian. Kata Kunci: Regresi Logistik Biner, Metode Momen Diperumum, Iterasi Reweighted Least Square.
Pemodelan Semiparametrik Geographical Weighted Logistic Regression pada Data Kemiskinan di Provinsi Sulawesi Selatan Tahun 2017 Fitriatusakiah Fitriatusakiah; Andi Kresna Jaya; La Podje Talangko
ESTIMASI: Journal of Statistics and Its Application Vol. 2, No. 2, Juli, 2021 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v2i2.11309

Abstract

The level of poverty in a Regency/city in South Sulawesi in 2017 is different. The grouping of poverty status can be done based on the value of the HeadCount Index (HCI) of South Sulawesi. Factors affecting poverty will differ for each area being observed. The statistical modeling method developed for data analysis by taking into account the location factor is semiparametric Geographical Weighted Logistic Regression (GWLR). The GWLR semiparametric Model consists of parameters that are affected by the location and not affected by the location. The parameter estimator of the GWLR semiparametric model used in this research was obtained using the maximum method likelihood estimation. The result of a semiparametric model of GWLR each district/city in South Sulawesi in 2017 has the value Estimator parameter for global parameters is the same value for each location, namely, a3 = 0.1724, a4 = 0.0204, and a6 = 0.0261 whereas the parameter estimator for local parameters has different values so that GWLR semiparametric model of each district/city.
Rancangan Faktorial Model Campuran Menggunakan Metode Maksimum Likelihood Andi Tenri Riski Amalia; Raupong Raupong; Andi Kresna Jaya
ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 1, Januari, 2022 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.11406

Abstract

Variance is the amount of statistics which measures how far a set of numbers in observation are spread out from its mean. In experimental design, variance are caused by the effect of treatment, block and error of experimental can be estimated by variability of error that commonly referred to variance component. In this study, the maximum likelihood method with Hartley Rou modification was used followed by the Newton Raphson method which was applied to a complete randomized block factorial design mixed model with factor A being fixed and factor B being random. The results of this study for rice production data showed that there is a significant effect on the interaction of genotype and location on rice production. The estimated value of the variance component obtained indicates that there are variations in the influence of location factors, and genotype and location interaction factors on rice production.
Model Regresi Bivariate Zero-Inflated Poisson Pada Kematian Ibu dan Bayi Andi Isna Yunita; Andi Kresna Jaya; Georgina Maria Tinungki
ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 1, Januari, 2022 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.11557

Abstract

Overdispersion is a condition with greater variance than the mean. One of the causes overdispersion is more zero-value observations so the Zero-Inflated Poisson (ZIP) regression model can be used. As for modeling a pair of discrete data is correlated and overdispersion, it can be used the Bivariate Zero-Inflated Poisson (BZIP) regression model. The BZIP regression model is a model with response variables with mixed distributions between Bivariate Poisson distribution and a point probability at (0,0). Parameters of the BZIP regression model are estimated using maximum likelihood estimation (MLE) with expectation maximization (EM) algorithm. This research was applied to data on number of maternal and infant mortality in the city of Makassar in 2017. The result obtained is the AIC value of the BZIP regression model is 170.976 smaller than the Bivariate Poisson regression model is 198.120. This shows that the BZIP regression model is better used for data with overdispersion.
Nilai Risiko Terkondisi pada Return Finansial Menggunakan Metode Copula Gumbel Najiha Alimatun; Anisa Anisa; Andi Kresna Jaya
ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 1, Januari, 2022 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.vi.12246

Abstract

The calculation of VaR is assumed normal distribution while the conditions in the real world distribution conditions of the return value depends on the market conditions that occurred at the time. Thus, this makes VaR estimates invalid which results in portfolio risk occurring greater than the predetermined risk. Therefore, In this study, the estimated risk value uses the Conditional Value at Risk (CVaR), which measures the expected value depending on what is the worst percentage of the risk loss, and using Copula Gumbel to model financial return in the investment data of PT. Telkomunikasi Indonesia tbk and PT. XL Axiata tbk. for the period March 11, 2019 to March 10, 2020. In this study, the CVaR estimation results for the 99% confidence level is 0.231, while for the VaR estimate it is 0.192. This indicates that risk value with CVaR estimate is better able to show higher risk than VaR.