Bootstrap Residual Ensemble Methods for Estimation of Standard Error of Parameter Logistic Regression To Hypercolesterolemia Patient Data In Health Laboratory Yogyakarta

Fransiska Grace S.W., Sri Sulistijowati Handajani, Titin Sri Martini

Abstract

Logistic regression is one of regression analysis to determine the relationship between response variable that have two possible values and some predictor variables. The method used to estimate logistic regression parameters is the maximum likelihood estimation (MLE) method. This method will produce a good estimate of the parameters if the estimation results have a small standard error.
In a research, the characteristics of good data must be representative of the population. If the samples taken in small size they will cause a large standard error value. Bootstrap is a resampling method that can be used to obtain a good estimate based on small data samples. Small data will be resampling so it can represent the population to obtain minimum standard error. Previous studies have discussed resampling bootstrap on residuals as much as b times. In this research we will be analyzed resampling bootstrap on the error added to the dependent variable and take the average parameter estimation ensemble logistic regression model resampling result. Next we calculate the standard value error logistic regression parameters bootstrap results.
This method is applied to the hypercholesterolemic patient status data in Health Laboratory Yogyakarta and after bootstrapping, the standard error produced is smaller than before the bootstrap resampling.
Keywords : logistic regression, standard error, bootstrap resampling, parameter estimation ensemble

Full Text:

PDF

References

A.Hossain and H.T.A. Khan, Nonparametric Bootstrapping for Multiple Logistic Regression Model Using R, BRAC University Journal, Vol. I(2004), No.2, 109-113, Bangladesh.

A. N.Putri, D.R.S.Saputro and P.Widyaningsih, “Informasi Fisher pada Algoritme Fisher Scoring untuk Estimasi Parameter Model Regresi Logistik Ordinal Terboboti Geografis (RLOTG)”, Seminar Nasional, Jurusan Matematika, FMIPA UNS, Surakarta, 2016.

B. Efron and R.J. Tibshirani, An Introduction to the Bootstrap, (Chapman & Hall, Inc, New York, 1993).

D.W. Hosmer and S. Lemeshow, Applied Logistic Regression Second Edition, (John Wiley & Sons, Inc, New York, 2000).

D.Prabandari, “Analisis Regresi Logistik Ganda dalam Memodelkan Faktor-Faktor Terindikasinya Penyakit Hiperkolesterol di Balai Laboratorium Kesehatan Yogyakarta”, Laporan Kerja Praktek, Fakultas MIPA UGM, Yogyakarta, 2009.

I. Pardoe and S.Weisberg. An Introduction to Bootstrap Methods using Arc, (University of Minnesota St.Paul, New York, 2001).

J.Sungkono, Resampling Bootstrap pada R, FKIP UNWIDHA, Vol.XXV(2013), No.84, Klaten.

K.Teknomo, Bootstrap Sampling Tutorial, Ateneo de Manila University, Manila, 2006.

DOI: https://doi.org/10.13057/ijas.v1i1.24086

Refbacks

  • There are currently no refbacks.