Article Per Year (5 Year)
p-Index From 2021 - 2026
1.518
P-Index
This Author published in this journals
All Journal Journal of Mathematics Education and Application (JMEA) MES: Journal of Mathematics Education and Science Journal of Research in Mathematics Trends and Technology Journal of Mathematics Technology and Education Innovative: Journal Of Social Science Research Journal of Mathematics, Computation and Statistics (JMATHCOS)
Pane, Rahmawati
Unknown Affiliation
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Environmental Science Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

Published : 11 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 11 Documents
Search

 1   2 
Estimation of Heteroskedasticity Semiparametric Regression Curve Using Fourier Series Approach Pane, Rahmawati; Sutarman; Andi Tenri Ampa
Journal of Research in Mathematics Trends and Technology Vol. 2 No. 1 (2020): Journal of Research in Mathematics Trends and Technology (JoRMTT)
Publisher : Talenta Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32734/jormtt.v2i1.3744

Abstract

A heteroskedastic semiparametric regression model consists of two main components, i.e. parametric component and nonparametric component. The model assumes that any data (x̰ i′ , t i , y i ) follows y i = x̰ i′ β̰+ f(t i ) + σ i ε i , where i = 1,2, … , n , x̰ i′ = (1, x i1 , x i2 , … , x ir ) and t i is the predictor variable. Parameter vector β̰ = (β 1 , β 2 , … , β r ) ′ ∈ ℜ r is unknown and f(t i ) is also unknown and is assumed to be in interval of C[0,π] . Random error ε i is independent on zero mean and varianceσ 2 . Estimation of the heteroskedastic semiparametric regression model was conducted to evaluate the parametric and nonparametric components. The nonparametric component f(t i ) regression was approximated by Fourier series F(t) = bt + 12 α 0 + ∑ α k 𝑐 𝑜𝑠 kt Kk=1 . The estimation was obtained by means of Weighted Penalized Least Square (WPLS): min f∈C(0,π) {n −1 (y̰− Xβ̰−f̰) ′ W −1 (y̰− Xβ̰− f̰) + λ ∫ 2π [f ′′ (t)] 2 dt π0 } . The WPLS solution provided nonparametric component f̰̂ λ (t) = M(λ)y̰ ∗ for a matrix M(λ) and parametric component β̰̂ = [X ′ T(λ)X] −1 X ′ T(λ)y̰
Co-Authors Andi Tenri Ampa Asima Manurung Br Tarigan, Enita Dewi br Tarigan, Ernita Dewi Harahap, Sherly Vitara Nababan, Yosua Pernando Nasution, Elsa Fadillah Nasution, Putri K Pasukat Sembiring Silalahi, Putri Mayang Syafira Simangungsong, Monalisa Sutarman Sutarman Sutarman Suyanto Suyanto Tambunan, Ruth A Tarigan, Enita Dewi br. Yastri, Yastri