scholarly journals Model Regresi Semiparametrik Spline untuk D ata Longitudinal pada Kasus Demam Berdarah Dengue di Kota Makassar

2021 ◽  
Vol 3 (1) ◽  
pp. 20
Author(s):  
Syafruddin Side ◽  
Wahidah Sanusi ◽  
Mustati'atul Waidah Maksum
Keyword(s):  
Regression Model ◽  
Mean Square Error ◽  
Cross Validation ◽  
Semiparametric Regression ◽  
Model Estimation ◽  
Mean Square ◽  
Semiparametric Regression Model ◽  
Regression Spline ◽  
Generalized Cross Validation ◽  
Selection Of

Abstrak. Regresi semiparametrik merupakan model regresi yang memuat komponen parametrik dan komponen nonparametrik dalam suatu model. Pada penelitian ini digunakan model regresi semiparametrik spline untuk data longitudinal dengan studi kasus penderita Demam Berdarah Dengue (DBD) di Rumah Sakit Universitas Hasanuddin Makassar periode bulan  Januari sampai bulan Maret 2018. Estimasi model regresi terbaik didapat dari pemilihan titik knot optimal dengan melihat nilai Generalized Cross Validation (GCV) dan Mean Square Error (MSE) yang minimum. Komponen parametrik pada penelitian ini adalah hemoglobin (g/dL) dan umur (tahun), suhu tubuh ( ), trombosit ( ) sebagai komponen nonparametrik dengan nilai GCV minimum sebesar 221,67745153 dicapai pada titik knot yaitu 14,552; 14,987; dan 15,096; nilai MSE sebesar 199,1032; dan nilai koefisien determinasi sebesar 75,3% yang diperoleh dari model regresi semiparametrik spline linear dengan tiga titik knot..Kata Kunci: regresi semiparametrik, spline, knot, Generalized Cross Validation, Demam Berdarah Dengue.Abstract. Semiparametric regression is a regression model that includes parametric and nonparametric components in it. The regression model in this research is spline semiparametric regression with case studies of patients with Dengue Hemorrahagic Fever (DHF) at University of Hasanuddin Makassar Hospital during the period of January to March 2018. The best regression model estimation is obtained from the selection of optimal knot which has minimum Generalized Cross Validation (GCV) and Mean Square Error (MSE). Parametric component in this research is hemoglobin (g/dL) and age (years), body temperature ( ), platelets ( ) as a nonparametric components. The minimum value of GCV is 221,67745153 achieved at the point 14,552; 14,987; and 15,096 knot; MSE value of 199,1032; and the value of coefficient determination is 75,3% obtained from semiparametric regression model linear spline with third point of knots.Keywords: semiparametric regression, spline, knot, Generalized Cross Validation, Dengue Hemorrahagic Fever.

scholarly journals APLIKASI MODEL REGRESI SEMIPARAMETRIK SPLINE TRUNCATED (Studi Kasus: Pasien Demam Berdarah Dengue (DBD) di Rumah Sakit Puri Raharja)

2017 ◽  
Vol 6 (1) ◽  
pp. 65
Author(s):  
NI WAYAN MERRY NIRMALA YANI ◽  
I GUSTI AYU MADE SRINADI ◽  
I WAYAN SUMARJAYA
Keyword(s):  
Body Temperature ◽  
Regression Model ◽  
Case Studies ◽  
Cross Validation ◽  
Single Point ◽  
Semiparametric Regression ◽  
Hemorrhagic Fever ◽  
Model Estimation ◽  
Semiparametric Regression Model ◽  
Selection Of

Semiparametric regression is a regression model that includes parametric components and nonparametric components in a model. The regression model in this research is truncated spline semiparametric regression with case studies of patients with Dengue Hemorrhagic Fever (DHF) at Puri Raharja Hospital during the period of January to March 2015. The best regression model estimation is obtained from the selection of optimal knots which has minimum Generalized Cross Validation (GCV) is. Parametric components in this research include age (years), body temperature (0C), platelets and hematocrit (%) as a nonparametric component. The minimum value of GCV is 0.03552045 achieved at the point of 39.6 knots, MSE value of 0.0296922; and the value of coefficient determination is 98.91%, obtained from semiparametric regression model truncated linear spline (order 2) with a single point of knots.

scholarly journals PEMILIHAN PARAMETER PENGHALUS DALAM REGRESI SPLINE LINIER

2011 ◽  
Vol 3 (1) ◽  
pp. 9
Author(s):  
Agustini Tripena Br. Sb.
Keyword(s):  
Mean Square Error ◽  
Cross Validation ◽  
Selection Method ◽  
Regression Estimation ◽  
Mean Square ◽  
Spline Regression ◽  
Selection Methods ◽  
The Mean ◽  
Smoothing Parameters ◽  
Selection Of

This paper discusses aselection of smoothing parameters for the linier spline regression estimation on the data of electrical voltage differences in the wastewater. The selection methods are based on the mean square errorr (MSE) and generalized cross validation (GCV). The results show that in selection of smooting paranceus the mean square error (MSE) method gives smaller value , than that of the generalized cross validatio (GCV) method. It means that for our data case the errorr mean square (MSE) is the best selection method of smoothing parameter for the linear spline regression estimation.

scholarly journals The extension of generalized cross-validation to a multi-parameter class of estimators

1981 ◽  
Vol 22 (4) ◽  
pp. 501-514 ◽  
Author(s):  
D. M. O'Brien ◽  
J. N. Holt
Keyword(s):  
Mean Square Error ◽  
Cross Validation ◽  
Regularization Parameter ◽  
Simple Algorithm ◽  
Mean Square ◽  
Generalized Cross Validation ◽  
New Class ◽  
Class Of Estimators ◽  
Expected Values ◽  
The One

AbstractThe method of generalized cross-validation (GCV) provides a good value for the “ridge” regularization parameter for an ill-conditioned linear system, such as the system produced by discretization of a Fredholm integral equation of the first kind. In this note we apply GCV to a wider class of estimators than the one parameter ridge estimators. We observe that the expected values of the parameter mean-square error, the predictive mean-square error, and the GCV function are simultaneously minimized over this new class, so we accept the minimizer of the GCV function as the best computable estimator. We present a simple algorithm for computing this estimator from the data, so that a numerical search is not needed.

scholarly journals Model Regresi Semiparametrik Spline Hasil Produksi Padi di Kabupaten Lombok Timur

2019 ◽  
Vol 1 (1) ◽  
pp. 11
Author(s):  
Bidayani Bidayani ◽  
Mustika Hadijati ◽  
Nurul Fitriyani
Keyword(s):  
Regression Model ◽  
Sea Level ◽  
Cross Validation ◽  
Rice Production ◽  
Spline Regression ◽  
Determination Coefficient ◽  
Generalized Cross Validation ◽  
Selection Of

This study was conducted with the aim of determining the semiparametric spline regression model in the analysis of factors that influence rice production in East Lombok District in 2014 and finding out what factors influence the rice production results. The method used was semiparametric spline regression, with the selection of the optimum knot points using Generalized Cross Validation. The results obtained indicate that the variable that significantly affects rice production was the height of the area above sea level, with the determination coefficient value of 99.71% and the RMSEP value of 41.65.

The Zero-Inflated Negative Binomial Semiparametric Regression Model: Application to Number of Failing Grades Data

2021 ◽  
Author(s):  
Elton G. Aráujo ◽  
Julio C. S. Vasconcelos ◽  
Denize P. dos Santos ◽  
Edwin M. M. Ortega ◽  
Dalton de Souza ◽  
...  
Keyword(s):  
Regression Model ◽  
Negative Binomial ◽  
Semiparametric Regression ◽  
Model Application ◽  
Semiparametric Regression Model

Quantitative Analysis of Forsythiae fructus by Near Infrared Spectroscopy

2013 ◽  
Vol 807-809 ◽  
pp. 1967-1971
Author(s):  
Yan Bai ◽  
Xiao Yan Duan ◽  
Hai Yan Gong ◽  
Cai Xia Xie ◽  
Zhi Hong Chen ◽  
...  
Keyword(s):  
Infrared Spectroscopy ◽  
Root Mean Square Error ◽  
Mean Square Error ◽  
Root Mean Square ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Cross Validation ◽  
Ethanol Extract ◽  
Mean Square ◽  
Validation Set

In this paper, the content of forsythoside A and ethanol-extract were rapidly determinated by near-infrared reflectance spectroscopy (NIRS). 85 samples of Forsythiae Fructus harvested in Luoyang from July to September in 2012 were divided into a calibration set (75 samples) and a validation set (10 samples). In combination with the partical least square (PLS), the quantitative calibration models of forsythoside A and ethanol-extract were established. The correlation coefficient of cross-validation (R2) was 0.98247 and 0.97214 for forsythoside A and ethanol-extract, the root-mean-square error of calibration (RMSEC) was 0.184 and 0.570, the root-mean-square error of cross-validation (RMSECV) was 0.81736 and 0.36656. The validation set were used to evaluate the performance of the models, the root-mean-square error of prediction (RMSEP) was 0.221 and 0.518. The results indicated that it was feasible to determine the content of forsythoside A and ethanol-extract in Forsythiae Fructus by near-infrared spectroscopy.

scholarly journals Estimation of Heteroskedasticity Semiparametric Regression Curve Using Fourier Series Approach

2020 ◽  
Vol 2 (1) ◽  
pp. 14-20
Author(s):  
Rahmawati Pane ◽  
Sutarman
Keyword(s):  
Fourier Series ◽  
Regression Model ◽  
Variable Parameter ◽  
Predictor Variable ◽  
Semiparametric Regression ◽  
Least Square ◽  
Parameter Vector ◽  
Regression Curve ◽  
Semiparametric Regression Model ◽  
Main Components

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

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