Articles
<![CDATA[Analisis Data Panel Model Efek Acak pada Data Kemiskinan di Provinsi Sulawesi Selatan ]]>
<![CDATA[Anisa Anisa]]>;
<![CDATA[Nirwan Ilyas]]>
<![CDATA[ Jurnal Matematika, Statistika dan Komputasi Vol. 8 No. 2: January 2012 ]]>
Publisher : <![CDATA[Department of Mathematics, Hasanuddin University ]]>
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DOI: 10.20956/jmsk.v8i2.3391
<![CDATA[Analisis data panel adalah analisis regresi untuk data panel yang merupakan gabungan dari data cross-section dan data time series. Terdapat tiga pendekatan yang dapat digunakan pana analisis data panel, salah satunya adalah pendekatan model efek acak. Parameter-parameter pada model efek acak diestimasi dengan metode Generalized Least Square. Dalam tulisan ini, aplikasi diterapkan pada data kemiskinan di Provinsi Sulawesi Selatan pada tahun 2005-2008. Hasil penelitian menunjukkan bahwa terdapat 4 variabel independen yang berpengaruh secara negatif pada angka kemiskinan di Provinsi Sulawesi Selatan yaitu angka buta huruf, pertumbuhan ekonomi, angka kematian bayi, dan angka harapan hidup sehingga variabel-variabel tersebut perlu mendapat perhatian dan penanganan khusus. Hasil juga menunjukkan bahwa terdapat 2 variabel independen yang berpengaruh secara positif pada angka kemiskinan di Sulawesi Selatan yaitu tingkat pengangguran terbuka dan tingkat partisipasi angkatan kerja. Hasil lain yang diperoleh menunjukkan bahwa Kabupaten Soppeng dan Wajo berpengaruh secara signifikan dalam menurunkan angka kemiskinan di Provinsi ini]]>
<![CDATA[Modifikasi Penaksir Robust dalam Pelabelan Outlier Multivariat ]]>
<![CDATA[Erna Tri Herdiani]]>;
<![CDATA[Nirwan Ilyas]]>
<![CDATA[ Jurnal Matematika, Statistika dan Komputasi Vol. 14 No. 1 (2017): July 2017 ]]>
Publisher : <![CDATA[Department of Mathematics, Hasanuddin University ]]>
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DOI: 10.20956/jmsk.v14i1.3537
<![CDATA[Outlier adalah suatu observasi yang polanya tidak mengikuti mayoritas data. Outlier dalam kasus multivariat sangat sulit untuk dideteksi, khususnya ketika dimensi lebih dari 2. Kesulitan ini meningkat ketika data set berukuran besar, yakni jumlah variabel menjadi besar. Metode-metode pendeteksian outlier telah lama berkembang dan beberapa digunakan untuk pelabelan outlier sehingga data dapat dipisahkan antara data yang dicurigai sebagai outlier dan data set pada umumnya. Metode-metode tersebut adalah minimum volume ellipsoid disingkat MVE, minimun covariance determinant disingkat MCD, dan minimum vector variance disingkat MVV. Dari ketiga metode tersebut MVV memiliki waktu perhitungan yang paling cepat. Berdasarkan algoritma MVV, kriteria mengurutkan data menggunakan jarak mahalanobis, maka pada paper ini akan dimodifikasi kriteria pengurutan data dengan menghindari penulisan dalam bentuk invers dari matriks variansi kovariansi. Hasil yang diperoleh adalah metode MVV menjadi lebih cepat dengan menggunakan kriteria baru dengan kecermatan yang sama dengan MVV sebelumnya serta akan diaplikan untuk data real dan data simulasi.]]>
<![CDATA[Estimasi Model Regresi Kuantil Spline Kuadratik pada Data Trombosit dan Hematokrit Pasien DBD ]]>
<![CDATA[Bunga Aprilia]]>;
<![CDATA[Anna Islamiyati]]>;
<![CDATA[Anisa Anisa]]>;
<![CDATA[Nirwan Ilyas]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 2, Juli, 2020 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v1i2.9264
<![CDATA[Nonparametric quantile regression is used to estimate the regression function when assumptions about the shape of the regression curve are unknown. It is only assumed to be subtle by involving quantile values. One estimator in nonparametric regression is spline. The segmented properties of the spline provide more flexibility than ordinary polynomials. Therefore, the nature of the spline makes it possible to adapt more effectively to the local characteristics of a function or data. This study proposes to get the results of the estimation platelet count model to the hematocrit value of DHF. The optimal model obtained from the estimation of quadratic spline quantile regression is at quantile 0.5 with one knot and the GCV value is 41.5. The results of the estimation show that there is a decrease in platelet counts as the percentage of hematocrit increase.]]>
<![CDATA[Regresi Model Data Panel Efek Tetap dengan Metode Within Group pada Data Indeks Pembangunan Manusia Provinsi Sulawesi Selatan ]]>
<![CDATA[Andi Sitti Fahmi Riyanti Hufaini]]>;
<![CDATA[Raupong Raupong]]>;
<![CDATA[Nirwan Ilyas]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 1, Januari, 2020 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v1i1.9276
<![CDATA[This research aims to describe the parameter estimation of the regression model on the panel data by approaches of Fixed Effects Model with within a group method. This research aims to determine the factors that influence the Human Development Index in South Sulawesi Province in 2011-2017 using Panel Data Regression Analysis. The regression model was obtained from the maximum likelihood estimation using within group approach using a mean for each independent variable and the dependent variable to find out the intercept differences in each city or cross-section that explains the effect of regional differences and to find out the intercept differences for cross sectional or time series. The results showed that the average length of the school variable (????1) and life expectancy variable (X2) significantly affects the Human Development Index (Y) in the Province of South Sulawesi in 2011-2017.]]>
<![CDATA[Pemodelan Regresi Logistik Menggunakan Metode Momen Diperumum ]]>
<![CDATA[Grace Oktavia Yusuf]]>;
<![CDATA[Andi Kresna Jaya]]>;
<![CDATA[Nirwan Ilyas]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 2, Juli, 2020 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v1i2.9304
<![CDATA[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.]]>
<![CDATA[Pemodelan Regresi Bivariate Poisson Inverse Gaussian pada Kasus Kematian Ibu dan Neonatal di Sulawesi Selatan ]]>
<![CDATA[Nurul Ikhsani]]>;
<![CDATA[Anisa Kalondeng]]>;
<![CDATA[Nirwan Ilyas]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 1, Januari, 2023 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.vi.24113
<![CDATA[Overdispersion is a state with a variance value greater than the mean value so the Poisson Inverse Gaussian regression model is used. Meanwhile, to model two correlated response variables, the Bivariate Poisson Inverse Gaussian (BPIG) regression model was used. The BPIG model is a mixed- distributed model between the Poisson Bivariate and Gaussian Inverse distributions. The parameters of the BPIG regression model are estimated using Maximum Likelihood Estimation (MLE) with the Fisher Scoring algorithm. This study was applied to data on the number of maternal and neonatal deaths in South Sulawesi in 2019. The results obtained are predictor variables that affect the number of maternal and neonatal deaths in South Sulawesi in 2019, namely K4 services for pregnant women , active birth control participants , handling obstetric complications , handling neonatal complications and the number of health centers .]]>
<![CDATA[Robust Spatial-Temporal Analysis of Toddler Pneumonia Cases and its Influencing Factors ]]>
<![CDATA[Musdalifah Musdalifah]]>;
<![CDATA[Siswanto Siswanto]]>;
<![CDATA[Nirwan Ilyas]]>
<![CDATA[ Jurnal Varian Vol 6 No 2 (2023) ]]>
Publisher : <![CDATA[Universitas Bumigora ]]>
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DOI: 10.30812/varian.v6i2.2599
<![CDATA[Pneumonia is a disease that causes inflammation of the lungs and is one of the most common diseases infecting toddlers. As a directly infectious disease, there is a possibility of the influence of location diversity on the number of pneumonia sufferers. Robust Geographically and Temporally Weighted Regression (RGTWR) is a method used to model data by considering the heterogeneity of location and time and to overcome outliers in the data. The data used is the number of pneumonia sufferers aged under five and the factors that are thought to influence it, namely the number of health centers, population density, percentage of children under five with complete basic immunizations, percentage of children under five who are exclusively breastfed 0-6 months, and percentage of poor people. This study was conducted to model pneumonia sufferers under five and to find out the factors that significantly affect the number of sufferers in each observation. RGTWR produces an optimal model with an R2 value of 99.9997%, a Mean Absolute Deviation of 21.6852, and a Median Absolute Deviation of 6.9661 compared to the Geographically and Temporally Weighted Regression model. Variables number of puskesmas, percentage of infants with complete basic immunization, and percentage of poor population are factors that influence the number of pneumonia sufferers under five in most locations in 34 provinces and 5 years of observation.]]>
<![CDATA[Mengatasi Overdispersi Menggunakan Regresi Binomial Negatif dengan Penaksir Maksimum Likelihood pada Kasus Demam Berdarah di Kota Makassar ]]>
<![CDATA[Fadil, Muhammad]]>;
<![CDATA[Raupong, Raupong]]>;
<![CDATA[Ilyas, Nirwan]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 1, Januari, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i1.14552
<![CDATA[The basic assumption in Poisson regression is that the mean value is the same as the variance value, which is called equidispersion. However, in some cases, this assumption is not met. A variance value that is greater than the average is called overdispersion and is called underdispersion if the variance value is smaller than the average value. So the Poisson regression model is no longer suitable for modeling this type of data because it will produce biased parameter estimates, therefore a negative binomial regression model is used. The research results show that estimating the parameters of the negative binomial regression model uses the maximum likelihood estimation method and then continues with the Newton-Raphson iteration method. The results obtained show that the negative binomial regression model overcomes the overdispersion that occurs in data on the number of dengue fever cases in Makassar City with the model and an AIC value of 236.06647. The negative binomial regression model produces many models and then the best model with the smallest AIC criteria is selected.]]>
<![CDATA[Penerepan Analisis Diskriminan Kuadratik Robust Pada Klasifikasi Desa ]]>
<![CDATA[Asnidar, Asnidar]]>;
<![CDATA[Ilyas, Nirwan]]>;
<![CDATA[Raupong, Raupong]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 2, Juli, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i2.27002
<![CDATA[Discriminant analysis is a method used in separating objects into different groups and allocating objects into a predetermined group. Discriminant analysis is bound by the assumption that the mean vector for each group is different, the data is normally distributed multivariate and the covariance variance matrix between groups is the same. If there is a covariance variance matrix between different groups, then quadratic discriminant analysis is used for the classification process. However, sometimes it is found that data contains outliers, so a robust estimator is used, namely the Minimun Covariance Determinant with the fast-MCD algorithm. Therefore, robust quadratic discriminant analysis can be used to classify 128 villages and 48 sub-districts in Wajo district. It was found that 106 villages were correctly classified into village groups and 22 villages were misclassified into sub-district groups and 35 sub-districts were correctly classified as sub-district groups and 13 sub-districts were misclassified into village groups and produced an accuracy of classification results of 80.11%.]]>
<![CDATA[Perbandingan Analisis Komponen Utama Robust Minimum Covarian Determinant dengan Least Trimmed Square pada Data Produk Domestik Regional Bruto ]]>
<![CDATA[Amni, Wa Ode Sitti Amni]]>;
<![CDATA[Jaya, Andi Kresna]]>;
<![CDATA[Ilyas, Nirwan]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 5, No. 2, Juli, 2024 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v5i2.32283
<![CDATA[Regression analysis is a method to examine the relationship between variables and determine their influence. However, the problem of multicollinearity often arises in linear regression analysis and can cause interpretation problems. To handle multicollinearity, Principal Component Analysis (PCA) is used. However, this method has a weakness when the data contains outliers. Therefore, it was developed into robust PCA using the Minimum Covariance Determinant (MCD) method and the Least Trimmed Square (LTS) estimation method. This study uses Gross Regional Domestic Product data in Indonesia in 2020, which has problems with multicollinearity and outliers. This data is modeled using two robust PCA methods, namely MCD and LTS. The robust PCA model with MCD has an adjusted value of 87.87% and an MSE value of 0.0700. However, in the robust PCA regression model with LTS, the adjusted value is 98.93% and the MSE value is 0.0325. Thus, the effective method in handling multicollinearity and outliers is the LTS method because it shows better results.]]>
