Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal Media Statistika MATEMATIKA Journal of the Indonesian Mathematical Society Indonesian Journal of Statistics and Its Applications
Herni Utami
Department of Mathematics Gadjah Mada University, Indonesia
Computer Science & IT Decision Sciences, Operations Research & Management Mathematics Other

Published : 4 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 4 Documents
Search

 1 
SECOND ORDER LEAST SQUARE ESTIMATION ON ARCH(1) MODEL WITH BOX-COX TRANSFORMED DEPENDENT VARIABLE Utami, Herni; -, Subanar
Journal of the Indonesian Mathematical Society Volume 19 Number 2 (October 2013)
Publisher : IndoMS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22342/jims.19.2.166.99-110

Abstract

Box-Cox transformation is often used to reduce heterogeneity and to achieve a symmetric distribution of response variable. In this paper, we estimate the parameters of Box-Cox transformed ARCH(1) model using second-order leastsquare method and then we study the consistency and asymptotic normality for second-order least square (SLS) estimators. The SLS estimation was introduced byWang (2003, 2004) to estimate the parameters of nonlinear regression models with independent and identically distributed errors.DOI : http://dx.doi.org/10.22342/jims.19.2.166.99-110
PENGARUH SUATU DATA OBSERVASI DALAM MENGESTIMASI PARAMETER MODEL REGRESI Utami, Herni; I, Ruri; abdurrakhman, abdurrakhman
MATEMATIKA Vol 5, No 3 (2002): Jurnal Matematika
Publisher : MATEMATIKA

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (54.254 KB)

Abstract

Observasi yang mempengaruhi model regresi sedemikian hingga elipsoid konfidensi untuk estimasi parameter regresinya menjadi kecil apabila observasi tersebut “dihilangkan” adalah observasi penting.  Sehingga observasi penting tersebut bisa merupakan observasi berpengaruh sesungguhnya atau bisa juga sebagai outlier. Salah satu cara menentukan observasi ke-i penting atau tidak, melihat  elipsoid konfidensi parameter model regresi linear dengan “menghilangkan”  observasi tersebut.
SELECTION OF INPUT VARIABLES OF NONLINEAR AUTOREGRESSIVE NEURAL NETWORK MODEL FOR TIME SERIES DATA FORECASTING Hermansah, Hermansah; Rosadi, Dedi; Abdurakhman, Abdurakhman; Utami, Herni
MEDIA STATISTIKA Vol 13, No 2 (2020): Media Statistika
Publisher : Department of Statistics, Faculty of Science and Mathematics, Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/medstat.13.2.116-124

Abstract

NARNN is a type of ANN model consisting of a limited number of parameters and widely used for various applications. This study aims to determine the appropriate NARNN model, for the selection of input variables of nonlinear autoregressive neural network model for time series data forecasting, using the stepwise method. Furthermore, the study determines the optimal number of neurons in the hidden layer, using a trial and error method for some architecture. The NARNN model is combined in three parts, namely the learning method, the activation function, and the ensemble operator, to get the best single model. Its application in this study was conducted on real data, such as the interest rate of Bank Indonesia. The comparison results of MASE, RMSE, and MAPE values with ARIMA and Exponential Smoothing models shows that the NARNN is the best model used to effectively improve forecasting accuracy.
The Model of Per-Capita Expenditure Figures in Sumatera Selatan uses a Geographically Weighted Panel Regression: Model Angka Pengeluaran Per-Kapita di Sumatera Selatan menggunakan Geographically Weighted Panel Regression Wati, Dia Cahya; Azka, Dea Alvionita; Utami, Herni
Indonesian Journal of Statistics and Applications Vol 5 No 1 (2021)
Publisher : Statistics and Data Science Program Study, SSMI, IPB University, in collaboration with the Forum Pendidikan Tinggi Statistika Indonesia (FORSTAT) and the Ikatan Statistisi Indonesia (ISI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29244/ijsa.v5i1p61-74

Abstract

The Geographically Weighted Panel Regression (GWPR) is a development of a global regression model where the basic idea is taken from a combination of panel data and GWR. The GWPR model is built from the point approach method, which is based on the position of the coordinates of latitude and longitude. The parameters for the regression model at each location will produce different values. GWPR can accommodate spatial effects, so that it can better explain the relationship between response variables and predictors. The purpose of this study is to compare the GWPR model with the Fixed Gaussian and Adaptive Bisquare weighting functions based on the AIC value. The data used in this study is secondary data taken from the website of the Central Statistics Agency (BPS) in the form of Per-Capita Expenditure Figures in South Sumatra in 2013-2019. This research results that in the case of the Per-Capita Expenditure Rate (AP), it is better to use the GWPR method with a fixed gaussian weighting function in the modeling, where the resulting coefficient of determination is 95.81% rather than adaptive bisquare with a determination coefficient of 93.3%. The factors that influence the Per-Capita Expenditure Rate (AP) in South Sumatra on the fixed gaussian weighting are divided into 6 groups, while the adaptive bisquare is divided into 2 groups.
Co-Authors Abdurakhman Abdurakhman ABDURRAKHMAN ABDURRAKHMAN, ABDURRAKHMAN Azka, Dea Alvionita Dedi Rosadi Dia Cahya Wati Hermansah Hermansah, Hermansah I, Ruri Subanar -