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ESTIMASI: Journal of Statistics and Its Application

ESTIMASI
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Published by Universitas Hasanuddin

ISSN : 2721379X EISSN : 27213803 DOI : http://dx.doi.org/10.20956/ejsa

Core Subject : Education,

Mathematics

ESTIMASI: Journal of Statistics and Its Application, is a journal published by the Department of Statistics, Faculty of Mathematics and Natural Sciences, Hasanuddin University. ESTIMASI is a peer – reviewed journal with the online submission system for the dissemination of statistics and its application. The material can be sourced from the results of research, theoretical, computational development and all fields of science development that are in one group.

Arjuna Subject : Matematika - Statistik dan Probabilitas

Articles 47 Documents

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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

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.

Kemampuan Estimator Spline Linear dalam Analisis Komponen Utama Samsul Arifin; Anna Islamiyati; Raupong Raupong
ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 1, Januari, 2020 : Estimasi
Publisher : Hasanuddin University

In the formation of a regression model there is a possibility of a relationship between one predictor variable with other predictor variables known as multicollinearity. In the parametric approach, multicollinearity can be overcome by the principal component analysis method. Principal component analysis (PCA) is a multivariate analysis that transforms the originating variables that are correlated into new variables that are not correlated by reducing a number of these variables so that they have smaller dimensions but can account for most of the diversity of the original variables. In some research data that do not form parametric patterns also allows the occurrence of multicollinearity on the predictor variables. This study examines the ability of spline estimators in the analysis of the main components. The data contained multicollinearity and was applied to diabetes mellitus data by taking cholesterol type factors as predictors. Based on the estimation results, one main component is obtained to explain the diversity of variables in diabetes data with the best linear spline model at one knot point.

Perbandingan Estimasi Metode Kuadrat Terkecil Terboboti dan Metode Transformasi Box-Cox Pada Data Heteroskedastisitas Risma Risma; Sitti Sahriman
ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 2, Juli, 2020 : Estimasi
Publisher : Hasanuddin University

Dalam mengestimasikan parameter regresi umumnya digunakan metode kuadrat terkecil. Metode ini memiliki beberapa asumsi yang perlu dipenuhi salah satunya yakni homoskedastisitas. Pelanggaran asumsi homoskedastisitas dapat menyebabkan model estimasi tidak efisien. Oleh karena itu jika terjadi pelanggaran homoskedastisitas maka metode kuadrat terkecil tidak dapat lagi digunakan,sehingga diperukan metode alternative. Metode untuk mengatasi pelanggaran homoskedatisitas dua diantaranya yakni metode kuadrat terkecil terboboti dan metode transformasi Box-Cox. Dalam penelitian ini akan dibandingkan metode kuadrat terkecil terboboti dan metode transformasi Box-Cox. Dari penerapan kedua metode tersebut didapatkan metode kuadrat terkecil terboboti memiliki RMSE (root mean square error) yang lebih kecil dan R2 yang lebih besar dibandingkan metode transformasi Box-Cox. Maka dapat disimpulkan metode kuadrat terkecil terboboti lebih bagus digunakan dalam menangani pelanggaran homoskedastisitas.

Penggunaan Regresi Kuantil Multivariat pada Perubahan Trombosit Pasien Demam Berdarah Dengue Widya Nauli Amalia Puteri; Anna Islamiyati; Anisa Anisa
ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 1, Januari, 2020 : Estimasi
Publisher : Hasanuddin University

Quantile regression is an extension of the regression model of conditional quantile where the distribution is derived from the response variable expressed as a co-variate function. Quantile regression can model data that contain outliers. Patterns of platelet change in DHF patients based on body temperature and white blood cells were analyzed by quantile regression using θ = 0,25; 0,50, and 0,75. Based on the parameter estimation results, the quantile θ = 0,25 and 0,75 obtained variables that affect the platelets of DHF patients are white blood cells. Significant differences from the variables in each quantile occur because of the possibility of other factors that influence the platelets of DHF patients that are not contained in the model. The difference in the influence of factors on each quantile requires an appropriate adjustment of medical measures so that efficiency can be obtained in handling DHF patients.

Estimasi Parameter Structural Equation Modeling Terhadap Kepuasan Pelanggan Layanan Telekomunikasi Menggunakan Metode Maximum Likelihood Dwicahyo Ramadhan Priyatna; Raupong Raupong; La Podje Talangko
ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 1, Januari, 2020 : Estimasi
Publisher : Hasanuddin University

Structural Equation Modeling is a statistical technique that is able to analyze the pattern of simultan linear relationships between indicator variables and latent variables. In this study using structural equation modeling to analyze the relationship between perceived quality, perceived value, perceived bestscore, and customer satisfaction. The purpose of this study is to obtain the result parameter model estimation of structural equation modeling using maximum likelihood method and to obtain the level of students satisfaction from faculty of Mathematics and Natural Science Hasanuddin University toward Tri operator. Data collected by distributing questionnaire. Collecting sample in this study using Proporsional Random Sampling technique. To measure the level of students satisfaction from faculty of Mathematics and Natural Science Hasanuddin University toward Tri operator, the model chosen is the model used to measure Indonesian Customer Satisfaction Indeks. From the result of this study obtained in the amount of 92,04% with very satisfied criteria level of students satisfaction from faculty of Mathematics and Natural Science Hasanuddin University toward Tri operator with very satisfied criteria.

Pemodelan dan Peramalan Harga Penutupan Saham Perbankan dengan Metode ARIMA dan Family ARCH Devi Novanti; Hajrul Multazam; Novira Laily Husna; Ossy Sanityasa Rahajeng; Selfina L; Rani Nooraeni
ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 2, Juli, 2020 : Estimasi
Publisher : Hasanuddin University

Modelling the stock closing price stock is useful so that the investors are expected to be able to understand the situation of the stock, in order to make the right decision when they want to buy or sell their stocks. This study uses the ARIMA and Family ARCH methods in modelling the volatility of four banking stocks that are in high demand by the public, which are Bank BRI (BBRI), Bank BNI (BBNI), Bank Mandiri (BMRI), and Bank BCA (BBCA) from January 1st 2017 until January 31st 2020. Stock returns are modelled by using the ARIMA model, then proceeded with the heteroscedasticity testing. Based on the test, we obtained the results of BBRI, BMRI, and BBCA are heteroscedastic. While BBNI are homoscedastic. The volatility models obtained from the test are BBNI has ARIMA models ([6,13], 1, [6,13]), BBRI has ARI models ([2,24,28), 1,0) -ARCH (1), BMRI has an ARIMA (2,1,4) -GARCH (1,1) model, and BBCA has ARI ([1,2], 1,0) -GARCH (1,1) model. Based on the rising value of the stock price, we suggest the best stock for the investors is BBRI because it has the largest increase of 10% followed by BBCA and BMRI

Penggunaan Analisis Korespondensi Sederhana dalam Pemetaan Wilayah Potensi Bencana di Provinsi Sulawesi Tengah Iis Cendrah Kasih; Georgina Maria Tinungki; Nasrah Sirajang
ESTIMASI: Journal of Statistics and Its Application Vol. 2, No. 1, Januari, 2021 : Estimasi
Publisher : Hasanuddin University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20956/ejsa.v2i1.10234

Disaster cases need to be analyzed considering that when a disaster occurs it will have an extraordinary impact. The statistical method that can be used to study disaster cases is a simple correspondence analysis. This study aims to map areas with the potential for natural disasters in the province of Central Sulawesi. So, in the analysis, regions are grouped according to row profile values that are greater than the average. The result of simple correspondence analysis obtained flood disaster has the potential to occur in Banggai, Morowali, Donggala, Buol, Parigi Moutong, Tojo Una-una, Sigi, and North Morowali. While the dominant tornado disaster occurred in Banggai Kepulauan, Banggai, Poso, Toli-toli, Parigi Moutong and Sigi. For regional landslides with potential Banggai Islands, Donggala, Toli-toli, Parigi Moutong, and Sigi. Then Banggai Islands and the City of Palu are the dominant regions for earthquake disasters. The results of the grouping can be the basis of government and community focus in tackling the dominant disasters occurring in their respective regions so as to minimize the impact when natural disasters occur.

Regresi Model Data Panel Efek Tetap dengan Metode Within Group pada Data Indeks Pembangunan Manusia Provinsi Sulawesi Selatan Andi Sitti Fahmi Riyanti Hufaini; Raupong Raupong; Nirwan Ilyas
ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 1, Januari, 2020 : Estimasi
Publisher : Hasanuddin University

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.

Estimasi Model Regresi Kuantil Spline Kuadratik pada Data Trombosit dan Hematokrit Pasien DBD Bunga Aprilia; Anna Islamiyati; Anisa Anisa; Nirwan Ilyas
ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 2, Juli, 2020 : Estimasi
Publisher : Hasanuddin University

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.

Regresi Data Panel dengan Pendekatan Common Effect Model (CEM), Fixed Effect model (FEM) dan Random Effect Model (REM) (Studi Kasus: Persentase Penduduk Miskin Menurut Kabupaten/Kota di Kalimantan Timur Tahun 2015-2018) Eka Nur Amaliah; Darnah Darnah; Sifriyani Sifriyani
ESTIMASI: Journal of Statistics and Its Application Vol. 1, No. 2, Juli, 2020 : Estimasi
Publisher : Hasanuddin University

Panel data regression is a regression that combines cross section data and time series data. Panel data regression estimation can be done through 3 estimates namely CEM, FEM and REM. This research will make a modeling of the percentage of poor people according to regencies / cities in East Kalimantan using panel data regression analysis. Poverty occurs due to lack of income and assets to meet basic needs. For this reason, variables that are assumed to affect the percentage of the poor are used, including the Population Growth Rate (LPP), Human Development Index (HDI), and Adjustable Per capita Expenditure (PPD). By using 3 CEM, FEM and REM approaches based on testing, the best FEM model is obtained. Based on the FEM model the factors that significantly influence are the HDI and PPD. A value of 0.7755 means that the HDI and PPD can explain the percentage of poor people according to the Regency / City in East Kalimantan of 77.55% while the remaining 22.45% is influenced by other variables not yet included in the model.

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Anna Islamiyati

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Home Page

OAI Link

Editorial Team

Contact

Reviewer

Google Scholar

Contact Name Anna Islamiyati

Contact Email jurnalestimasi@unhas.ac.id

Phone -

Journal Mail Official jurnalestimasi@unhas.ac.id

Editorial Address Jl. Perintis Kemerdekaan Km. 10 Tamalanrea Makassar - Indonesia, 90245

Location Kota makassar, Sulawesi selatan INDONESIA

Abstract

Abstract

Abstract

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Abstract

Abstract

Abstract

Abstract

Abstract

Abstract

Contact Name
Anna Islamiyati

Contact Email
jurnalestimasi@unhas.ac.id

Phone
-

Journal Mail Official
jurnalestimasi@unhas.ac.id

Editorial Address
Jl. Perintis Kemerdekaan Km. 10 Tamalanrea Makassar - Indonesia, 90245

Location
Kota makassar,

Sulawesi selatan

INDONESIA