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Your search keyword '"Zain, Ismaini"' showing total 2 results
Start Over Author "Zain, Ismaini" Topic sample size (statistics) Publication Year Range Last 3 years
2 results on '"Zain, Ismaini"'

1. The estimation of mixed truncated spline and Fourier series estimator in bi-response nonparametric regression.

Author

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

Subjects
LEAST squares, REGRESSION analysis, INDEPENDENT variables, SPLINES, NONPARAMETRIC estimation, SAMPLE size (Statistics), FOURIER series
Abstract

Due to the lack of prior knowledge regarding the type of relationship between the response and the predictor variable, not all patterns of the regression curve are identifiable in regression analysis. Hence, nonparametric regression becomes a reasonable solution since no prespecified functional form is assumed. In nonparametric regression, curve estimation using a mixed estimator is rather complex, notably when there are two or more correlated response variables. In this study, we developed the curve estimation of bi-response nonparametric regression with a mixed truncated spline and Fourier series estimator model. The main objective was to estimate the regression curve using penalized weighted least square and weighted least square optimization. Based on the estimation results, numerical simulations with various sample sizes and correlations were implemented with generalized cross validation as the criterion. Thus, the model with a large sample size and high correlation was performed with the best outcome. [ABSTRACT FROM AUTHOR]

Published
2023
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2. 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

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