To analyze the factor affecting poverty during several periods by considering some geographical factors, we can use a geographically weighted panel regression (GWPR) method. GWPR is a combination of the geographically weighted regression (GWR) model and the panel regression model. The research conducts to identify the factors affecting the percentage of poor people in 34 provinces in Indonesia during 2015-2019. The results show that a suitable GWPR model is a fixed-effect model (FEM) with an exponential adaptive kernel function. Referring to the model, the province is divided into four groups based on variables having a significant effect on the percentage of poor people. That factors causing the poor people percentage in Indonesia are the poor people percentage aged above 15 years old and unemployment, the people percentage aged above 15 years old and employed in the agricultural sector, the literacy rate of the poor aged between 15 to 55 years old, and the life expectancy rate.
Keywords: fixed effect model, exponential adaptive kernel.
The generalized linear process accomplishes stationarity and invertibility properties. The invertibility property must be having a series of convergence conditions of the process parameter. The generalized Space-Time Autoregressive (GSTAR) model is one of the stationary linear models therefore it is necessary to reveal the invertibility through the convergence of the parameter series. This article studies the invertibility of model GSTAR(1;1) with kernel random weight. The result shows that the model GSTAR(1;1) under kernel random weight fulfills the invertibility property and obtains a finite order of Generalized Space-Time Moving Average (GSTMA) process. The other result obtained is the time order of the finite orde . On the Triangular kernel resulted in the relatively great value n, so that it does not apply to the kernel with a finite value n.
ABSTRACT. This paper elaborates a research of the cancer patients after receiving a treatment in cencored data using Bayesian estimation under Linex Loss function for Survival Model which is assumed as an exponential distribution. By giving Gamma distribution as prior and likelihood function produces a gamma distribution as posterior distribution. The posterior distribution is used to find estimatior ̂ by using Linex approximation. After getting ̂, the estimators of hazard function ĥ and survival function ̂ can be found. Finally, we compare the result of Maximum Likelihood Estimation (MLE) and Linex approximation to find the best method for this observation by finding smaller MSE. The result shows that MSE of hazard and survival under MLE are 2.91728E-07 and 0.000309004 and by using Bayesian Linex worths 2.8727E-07 and 0.000304131, respectively. It concludes that the Bayesian Linex is better than MLE.
Model Vector Autoregressive Exogenous (VARX) dapat digunakan untuk memprediksi data deret waktu lebih dari satu variabel yang menggunakan variabel eksogen dalam sistem persamaannya. Model VARX menggunakan variabel endogen yang saling berhubungan dipengaruhi waktu sebelumnya dan terdapat variabel eksogen yang mempengaruhi variabel endogen tersebut. Penelitian ini menggunakan data sekunder, yaitu data bulanan deret waktu hasil produksi karet kering dan karet basah dalam periode tahun 2016 sampai tahun 2018 pada kebun Sintang di Kalimantan Barat serta data deret waktu curah hujan pada kebun . Tujuan penelitian ini adalah melakukan pemodelan dan memprediksi hasil produksi karet kering dan karet basah PTPN XIII pada periode Juli sampai Desember 2018 dengan model VARX. Hasil analisis menunjukkan bahwa model VARX yang dapat diterapkan terhadap data hasil produksi karet kering dan karet basah adalah VARX(1,1). Nilai MAPE untuk model VARX(1,1) pada masing-masing variabel yaitu 14,73% dan 16,06% sehingga ketepatan hasil prediksi model dapat dikatakan baik. Kata Kunci: karet basah, karet kering, curah hujan, MAPE
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