Search

Your search keyword '"Zain, Ismaini"' showing total 14 results
Start Over Author "Zain, Ismaini" Search Limiters Full Text
14 results on '"Zain, Ismaini"'

13. The New Estimation of Bi-response Nonparametric Regression Curve with Combined Estimators.

Author

Nurcahayani, Helida, Budiantara, I. Nyoman, and Zain, Ismaini

Subjects
*FOURIER series, *HUMAN Development Index, *NONPARAMETRIC estimation, *LEAST squares, *REGRESSION analysis, *SAMPLE size (Statistics)
Abstract

To date, nonparametric regression studies using a combined estimator model have recently begun to develop significantly. Yet previous research employing this approach is still restricted to models with a single response variable. Hence, this paper offers a novel method to estimate bi-response nonparametric regression using a model that combines Fourier series and truncated spline estimators. This research aims to estimate the regression curve of the proposed model using twostage estimation. The first stage is completed by the penalized weighted least square optimization followed by utilized the weighted least square optimization. We conduct numerical simulations with various sample sizes and correlations to assess the performance of the proposed model. Using generalized cross-validation as a criterion, the best model was obtained from the scenario model with big sample size and strong correlation. Furthermore, compared to uncombined estimators, the proposed model outperformed when applied to a real dataset of the human development index (HDI) education indicator in the East Java Province, Indonesia. [ABSTRACT FROM AUTHOR]

Published
2023

14. Classification method at acceptance of new student at public university on the national written test

Author

W, Ika S and Zain, Ismaini

Abstract

Acceptance of new students at public universities through the national written test is based on the total score and the capacity of the study program. This causes the study program accepts several students who have low scores on the main subject of the study program. The purpose of this study is to find the best method in predicting the probability of being accepted on the national written test and find the minimum score for each subject that must be achieved by participants to be accepted at a public university. There are two classification methods in statistics that are studied to overcome this problem, i.e. logistic regression and random forest. The results showed that the best logistic regression model had an accuracy of 97.11 percent, whereas the random forest method had an accuracy of 96.59 percent. Furthermore, the minimum score for each subject was developed based on the univariate logistic regression model.

Published
2019

Catalog

Books, media, physical & digital resources
See catalog results