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<![CDATA[Penggunaan Regresi Kuantil Multivariat pada Perubahan Trombosit Pasien Demam Berdarah Dengue ]]>
<![CDATA[Widya Nauli Amalia Puteri]]>;
<![CDATA[Anna Islamiyati]]>;
<![CDATA[Anisa Anisa]]>
<![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.v1i1.9224
<![CDATA[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.]]>
<![CDATA[Hubungan Faktor Kolestrol Terhadap Gula Darah Diabetes dengan Spline Kubik Terbobot ]]>
<![CDATA[Zhazha Alifkhamulki Ramdhani]]>;
<![CDATA[Anna Islamiyati]]>;
<![CDATA[Raupong Raupong]]>
<![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.v1i1.9252
<![CDATA[Diabetes Mellitus (DM) is often recognized through an increase in a person's blood sugar level. Factors that can affect the increase in blood sugar levels of DM patients one of which is cholesterol. It usually contains the bookkeeping of several types of cholesterol, including LDL and total cholesterol. DM data are assumed to experience heterokedasticity so that in this study analyzed using regression of weighted cubic spline nonparametric. The estimation method used is weighted least square (WLS). This study aims to obtain a weighted cubic spline model on cholesterol based DM data. The selection of the best model can be seen based on the criteria for the value of generalized cross validation (GCV) minimum. Based on the analysis obtained weighted cubic spline models for cholesterol factors for blood sugar as follows:]]>
<![CDATA[Kemampuan Estimator Spline Linear dalam Analisis Komponen Utama ]]>
<![CDATA[Samsul Arifin]]>;
<![CDATA[Anna Islamiyati]]>;
<![CDATA[Raupong Raupong]]>
<![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.v1i1.9262
<![CDATA[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.]]>
<![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[Pemodelan Regresi Nonparametrik Spline Poisson Pada Tingkat Kematian Bayi di Sulawesi Selatan ]]>
<![CDATA[Novilia Jao]]>;
<![CDATA[Anna Islamiyati]]>;
<![CDATA[Nurtiti Sunusi]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 1, Januari, 2022 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.vi.11997
<![CDATA[Poisson regression analysis is a method used to analyze the relationship between predictor variables and response variables with a Poisson distribution. However, not all data have an orderly pattern, so the Poisson regression is not appropriate to use. To solve this problem, a multivariable Poisson nonparametric regression with a spline truncated estimator was used. In this research, the estimation parameters of multivariable Poisson nonparametric regression was applied to data of infant mortality rates in South Sulawesi in 2017. The infant mortality rate can be measured from the number of infant deaths under one year. The method of selecting the optimal knot point uses the Generalized Cross Validation (GCV) method. The best model is formed on a linear spline model with one knot point. Based on the estimation of the parameters formed, it shows that the variable number of babies with low birth weight (x1) and the number of infants who are exclusively breastfed (x3) significantly affect the number of infant deaths. Keywords: GCV, Multivariable Nonparametric Regression, Poisson, Spline Truncated, Total Infant Mortality.]]>
<![CDATA[Analisis Perubahan Berat Badan Balita dengan Estimator Penalized Spline Kuadratik ]]>
<![CDATA[Muhammad Jayzul Usrah]]>;
<![CDATA[Anna Islamiyati]]>;
<![CDATA[Anisa]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 3, No. 2, Juli, 2022 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.vi.11459
<![CDATA[Nonparametric regression is a regression approach that is used when one of the parametric assumptions are not fulfilled. One of the estimators in nonparametric regression is penalized spline. The growth pattern of toddler that varied each month of observation make the suitable regression approach is nonparametric penalized spline regression because of its high flexibility. This study aims to obtain an estimate of the growth model for toddler in South Sulawesi. The optimal model obtained with a minimum GCV value of 4.87E-05 using two point knots that is 14 and 56 with lamda 100. The estimation results show that there are 3 intervals of change patterns in the growth of toddler in South Sulawesi]]>
<![CDATA[Pemodelan Regresi Logistik Ordinal dengan Dispersi Efek Lokasi ]]>
<![CDATA[Ainun Utari Budistiharah]]>;
<![CDATA[Anna Islamiyati]]>;
<![CDATA[Sri Astuti Thamrin]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 4, No. 2, Juli, 2023 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v4i2.12355
<![CDATA[Logistic regression ordinal is a regression model that can explain the relationship between predictor variables in the form of categorical data or continuous data with response variable is more than two categories with a scale of measurement that is level or sequence. In ordinal logistic regression, the frequency of occurrence in each response category is often very different, so it will affect the model's accuracy. Therefore, this study will model ordinal logistic regression with a dispersion of location effects, then applied to the nutritional status data of toddler in 2019 at the Pekkae Puskesmas, Barru Regency. The results obtained show that the ordinal logistic regression model with the dispersion of location effects is better than the usual ordinal logistic regression model for predicting the nutritional status data for toddlers in 2019 at Pekkae Puskesmas, Barru Regency based on deviance values. The factors that influence the nutritional status of toddler based on TB/U are gender, age, and height.]]>
<![CDATA[Pendugaan Koefisien Regresi Logistik Biner Menggunakan Algoritma Least Angle Regression ]]>
<![CDATA[Utami, Mamik]]>;
<![CDATA[Islamiyati, Anna]]>;
<![CDATA[Thamrin, Sri Astuti]]>
<![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.v5i1.12489
<![CDATA[Binary logistic regression is a statistical analysis method that aims to determine the relationship between variable which has two categories with the predictor variable that have categorical or continuous scale. The method that used to estimate logistic regression parameters is Maximum Likelihood Estimation (MLE) method. In estimating parameters, Least Angle Regression (LAR) algorithm is used to select the significant variables in order to get the best model from the estimation results of binary logistic regression coefficients. This LAR algorithm is applied to the risko of stunting data in two-year-old-babies at Buntu Batu Health Center working area, Enrekang Regency, South Sulawesi in 2019. This results obtained in the estimation of binary logistic regression prediction model using LAR algorithm, the standard error value is 0.018 smaller than the standard error value of binary logistic regression, which is 0.025. This shows that the binary logistic regression model using LAR algorithm is better than the usual binary logistic regression model on the risk of stunting data. Based on the results obtained, the variables that significantly affect the risk of stunting in two-year-old-babies on 2019 are father’s height, body length of birth, exclusive breastfeeding, history of infectious diseases, and history of immunization.]]>
<![CDATA[Estimasi Parameter Model Regresi Logistik Biner dengan Conditional Maximum Likelihood Estimation pada Data Panel ]]>
<![CDATA[Fitri, Fitri]]>;
<![CDATA[Islamiyati, Anna]]>;
<![CDATA[Kalondeng, Anisa]]>
<![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.v5i2.13998
<![CDATA[Binary logistic regression models can be used on panel data with categorical responses that experience repeated measurements based on time. This study aims to determine the factors that influence the Human Development Index in South Sulawesi Province in 2015-2019. Data were analyzed through binary logistic regression with fixed effect model approach through Conditional Maximum Likelihood Estimation (CMLE) for panel data. The results of this study indicate that the variables that have a significant effect are life expectancy (X1), school length expectancy (X2) and the average length of schooling (X3). Obtained the probability value of districts/cities that have a medium low and medium high human development index with a classification accuracy of 56.25%.]]>
<![CDATA[Estimasi Model Perubahan Indeks Harga Saham Gabungan melalui Regresi Kuantil Spline Smoothing ]]>
<![CDATA[Ashwad K, Hajratul]]>;
<![CDATA[Islamiyati, Anna]]>;
<![CDATA[Siswanto, Siswanto]]>
<![CDATA[ ESTIMASI: Journal of Statistics and Its Application Vol. 6, No. 1, Januari, 2025 : Estimasi ]]>
Publisher : <![CDATA[Hasanuddin University ]]>
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DOI: 10.20956/ejsa.v6i1.25198
<![CDATA[Regression of nonparametric quantile is conducted on purpose to help estimating the function of regression when the assumptions about the regression curve shape are not known involving quantile values. Spline is claimed as one of the estimators commonly applied in nonparametric regression. Patterns of platelet change in Jacarta Composite Indeks (JCI) based on Dow Jones Index (IDJ) were analyszed by quantile spline smoothing using τ 0.25, 0.50, and 0.75. The analysis results show two patterns of change in the relationship of JCI and the IDJ. It can be seen from the optimal knot point for each quantile, namely 28500, 35000 and 29600, which shows that before and after the IDJ value reaches the point from the knot point, there is a tendency to decrease and then increase in the JCI data. The optimal model with the one-knot point. According to the minimum GCV value, the optimal model with the smallest GCV vaue, which is 5243.45 on quantile 0.75.]]>
