Penaksiran Parameter dan Pengujian Hipotesis Model Multivariate Adaptive Bivariate Generalized Poisson Regression Spline (Studi Tentang: Jumlah Kasus dan Jumlah Kematian Covid-19 di Pulau Jawa)

Ramadany, Rizqiyanti (2021) Penaksiran Parameter dan Pengujian Hipotesis Model Multivariate Adaptive Bivariate Generalized Poisson Regression Spline (Studi Tentang: Jumlah Kasus dan Jumlah Kematian Covid-19 di Pulau Jawa). Masters thesis, Institut Teknologi Sepuluh Nopember.

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Abstract

Regresi Poisson merupakan regresi yang digunakan untuk memodelkan data count. Variabel respon regresi Poisson mengikuti distribusi Poisson. Regresi Poisson memiliki asumsi khusus yaitu nilai rata-rata variabel respon sama dengan nilai ragam variabel respon atau yang dikenal dengan asumsi ekuidispersi. Apabila asumsi ekuidispersi tidak bisa terpenuhi dapat menggunakan metode Generalized Poisson Regression (GPR). Multivariate Adaptive Regression Spline (MARS) adalah suatu metode nonparametrik dengan keunggulan mampu mengatasi permasalahan data dengan banyak variabel prediktor 3 ≤ p ≤ 20 Apabila terdapat dua variabel respon yang saling berkorelasi, maka model yang digunakan adalah pengembangan gabungan model MARS dan BGPR menjadi suatu model Multivariate Adaptive Bivariate Generalized Poisson Regression Spline (MABGPRS). Hasil penelitian menunjukkan bahwa penaksiran parameter model MABGPRS menggunakan metode Weighted Least Square (WLS) dan Maximum Likelihood Estimation (MLE). Statistik uji untuk pengujian serentak menggunakan Maximum Likelihood Ratio Test (MLRT). Model MABGPRS diterapkan pada jumlah kasus dan jumlah kematian Covid-19 di Pulau Jawa tahun 2020. Unit penelitiannya adalah 119 Kabupaten/Kota di Pulau Jawa. Variabel prediktor yang digunakan sebanyak tujuh variabel. Seluruh variabel prediktor berpengaruh terhadap jumlah kematian Covid-19 di Pulau Jawa. Variabel prediktor yang berpengaruh terhadap jumlah kasus Covid-19 di Pulau Jawa adalah sebanyak enam variabel prediktor. Variabel angka kesakitan/morbiditas tidak berkonstribusi terhadap pembentukan model MABGPRS jumlah kasus Covid-19 di Pulau Jawa. Berdasarkan nilai kepentingan variabel diketahui bahwa kepadatan penduduk (X1) dan persentase rumah tangga dengan sumber air minum layak (X4) adalah variabel terpenting pada model MABGPRS jumlah kasus Covid-19 dengan nilai tingkat kepentingan variabel 100. Variabel terpenting pada model MABGPRS jumlah kematian Covid-19 adalah persentase rumah tangga dengan akses sanitasi layak (X3) dan kelembaban udara (X7) dengan nilai tingkat kepentingan variabel 100. ===================================================================================================== Poisson regression is a regression used to model count data. The Poisson regression response variable follows the Poisson distribution. Poisson regression has a special assumption, namely the average value of the response variable is equal to the value of the variance of the response variable, which is known as the assumption of equidispersion. If the assumption of equidispersion cannot be met, the Generalized Poisson Regression (GPR) method can be used. Multivariate Adaptive Regression Spline (MARS) is a nonparametric method with the advantage of being able to overcome data problems with many predictor variables 3 ≤ 20. If there are two response variables that are correlated with each other, the model used is the combined development of the MARS and BGPR models into a model. Multivariate Adaptive Bivariate Generalized Poisson Regression Spline (MABGPRS). The results showed that the parameter estimation of the MABGPRS model used the Weighted Least Square (WLS) and Maximum Likelihood Estimation (MLE) methods. Test statistics for simultaneous testing using the Maximum Likelihood Ratio Test (MLRT). The MABGPRS model is applied to the number of cases and the number of deaths of Covid-19 on the island of Java in 2020. The research unit is 119 districts/cities on the island of Java. The predictor variables used were seven variables. All predictor variables affect the number of Covid-19 deaths in Java. The predictor variables that affect the number of Covid�19 cases in Java are six predictor variables. The variable percentage of the population who has health complaints/morbidities has no contribution on the MABGPRS model of thwnumber of Covid-19 cases in Java. Based on the value of the variable importance, it is known that population density (X1) and the percentage of households with adequate drinking water sources (X4) are the most important variables in the MABGPRS model the number of Covid-19 cases with a variable importance value of 100. The most important variable in the MABGPRS model is the number of Covid-19 deaths. 19 is the percentage of households with access to proper sanitation (X3) and humidity (X7) with a variable importance value of 100.

Item Type: Thesis (Masters)
Uncontrolled Keywords: BGPR, MABGPRS, Jumlah Kasus Covid-19, Jumlah Kematian Covid-19, MARS, MLE, MLRT, WLS, BGPR, MABGPRS, MARS, MLE, MLRT, Number of Covid-19 Cases, Number of Covid-19 Deaths, WLS
Subjects: Q Science > QA Mathematics > QA371 Differential equations--Numerical solutions
Q Science > QA Mathematics > QA278 Cluster Analysis. Multivariate analysis. Correspondence analysis (Statistics)
Divisions: Faculty of Science and Data Analytics (SCIENTICS) > Statistics > 49101-(S2) Master Thesis
Depositing User: Rizqiyanti Ramadany
Date Deposited: 10 Sep 2021 09:24
Last Modified: 10 Sep 2021 09:34
URI: https://repository.its.ac.id/id/eprint/91965

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