Spline Polynomial Truncated dalam Regresi Nonparametrik

Islamiyati, Anna

doi:10.20956/jmsk.v14i1.3538

Cited by 11 publications

(6 citation statements)

References 3 publications

(5 reference statements)

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“…Model regresi logistik biner termasuk dalam sebaran keluarga Eksponensial. Sebaran keluarga eksponensial yang dimaksud adalah sebaran Bernoulli, yaitu sebaran dari peubah acak yang hanya mempunyai 2 kategori yaitu 0 dan 1 [7]. Variabel respon pada regresi logistik biner terdiri 2 kategori, misalkan 𝑦 = 1 menyatakan hasil yang diperoleh 'sukses' dan 𝑦 = 0 menyatakan hasil yang diperoleh 'gagal'.…”

Section: Regresi Logistik Binerunclassified

Pendugaan Koefisien Regresi Logistik Biner Menggunakan Algoritma Least Angle Regression

Utami,

Islamiyati,

Thamrin

2024

estimasi

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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.

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Section: Regresi Logistik Binerunclassified

Pendugaan Koefisien Regresi Logistik Biner Menggunakan Algoritma Least Angle Regression

Utami,

Islamiyati,

Thamrin

2024

estimasi

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“…Titik knot adalah perpaduan dari dua interval yang terjadi perubahan garis perilaku pola. Cross Validation (CV), Generalized Cross Validation (GCV), dan Generalized Maximum Likelihood (GML), merupakan metode yang dapat digunakan dalam penentuan titik knot dengan pendekatan model spline (Islamiyati, 2017). Metode GCV merupakan metode penentuan yang paling sering digunakan dalam penentuan titik knot terbaik atau optimal, metode GCV adalah hasil modifikasi dari metode CV (Nurhuda et al, 2022).…”

Section: Pendekatan Splineunclassified

Pemodelan Regresi Nonparametrik Dengan Estimator Spline Polynomial Truncated Pada Data Jumlah Wisatawan Nusantara

Arman,

Lemido,

Siswanto

et al. 2024

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The nonparametric regression approach is a statistical method used to determine the relationship between predictor variables and the dependent variable when the assumed pattern is unknown. Truncated spline is an estimator used in nonparametric regression to handle data with varying behaviors. Nonparametric regression modeling with truncated polynomial spline was applied to local Indonesian tourist visitation data obtained from BPS for the years 2017-2019, for each month. The optimal knot points were selected based on the smallest Gross Cross Validation values. Based on the analysis, the optimal model is a second-order spline with the smallest Gross Cross Validation value of 17,95 and the optimal knot points are in the 2nd, 6th, and 7th months. The goodness of the model is evident from an value of 81,88% and an MSE of 12,46. The best model obtained shows a fairly accurate ability to explain the estimated number of domestic tourists so that it can be a basis for stakeholders to make key decisions in planning and managing the tourism industry as an effort to increase domestic tourism interest.

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“…Oleh sebab itu, model regresi dikembangkan ke dalam regresi nonparametrik yang digunakan untuk data dengan bentuk fungsi regresinya belum diketahui 1 . Beberapa estimator yang dikembangkan untuk mengestimasi fungsi regresi adalah estimator polinomial lokal 2 , estimator kernel 3 , deret fourier 4 , dan estimator spline 5 .…”

Section: Pendahuluanunclassified

Estimasi Model Regresi Spline Kubik Tersegmen dengan Metode Penalized Least Square

Islamiyati

Anisa

Raupong

et al. 2022

JPMIPA

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Abstract:Nonparametric regression is used for data whose data pattern is non-parametric. One of the estimators that can be developed is a segmented cubic spline which is able to show several segmentation changes in the data. This article examines the estimation of segmented cubic spline nonparametric regression models using the Penalized Least Square estimation criteria. The method involves knot points and smoothing parameters simultaneously. In addition, the model is used to analyze data on BPJS claims based on patient age. The results show that the optimal model is at two-knot points, namely 26 and 52 with a smoothing parameter of 0.89. There are three segmentation changes from the cubic data, which consist of young people up to 26 years old, 26-52 years old, and 52 years and over. Abstrak:Regresi nonparametrik digunakan untuk data yang pola datanya bentuk non parametrik. Salah satu estimator yang dapat dikembangkan adalah spline kubik tersegmen yang mampu menunjukkan beberapa segmentasi perubahan pada data. Artikel ini mengkaji estimasi model regresi nonparametrik spline kubik tersegmen melalui kriteria estimasi menggunakan Penalized Least Square. Metode tersebut melibatkan titik knot dan parameter penghalus secara bersamaan. Selain itu, model digunakan untuk menganalisis data klaim BPJS berdasarkan usia pasien. Hasil menunjukkan bahwa model optimal pada dua titik knot yaitu 26 dan 52 dengan parameter penghalus sebesar 0,89. Terdapat tiga segmentasi perubahan data secara kubik, yaitu usia muda hingga 26 tahun, usia 26-52 tahun, dan usia 52 tahun ke atas.

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Spline Polynomial Truncated dalam Regresi Nonparametrik

Cited by 11 publications

References 3 publications

Pendugaan Koefisien Regresi Logistik Biner Menggunakan Algoritma Least Angle Regression

Pendugaan Koefisien Regresi Logistik Biner Menggunakan Algoritma Least Angle Regression

Pemodelan Regresi Nonparametrik Dengan Estimator Spline Polynomial Truncated Pada Data Jumlah Wisatawan Nusantara

Estimasi Model Regresi Spline Kubik Tersegmen dengan Metode Penalized Least Square

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