Coverage Properties of a Neural Network Estimator of Finite Population Total in High-Dimensional Space
The problem in nonparametric estimation of finite population total particularly when dealing with high-dimensional datasets is addressed in this paper. The coverage properties of a robust finite population total estimator based on a feedforward backpropagation neural network developed with the help...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
|
| Series: | Journal of Mathematics |
| Online Access: | http://dx.doi.org/10.1155/2022/2431308 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | The problem in nonparametric estimation of finite population total particularly when dealing with high-dimensional datasets is addressed in this paper. The coverage properties of a robust finite population total estimator based on a feedforward backpropagation neural network developed with the help of a superpopulation model are computed, and a comparison with existing model-based estimators that can handle high-dimensional datasets is conducted to evaluate the estimator’s performance using simulated datasets. The results presented in this paper show good performance in terms of bias, MSE, and mean absolute error for the feedforward backpropagation neural network estimator as compared to other identified existing estimators of finite population total in high-dimensional datasets. In this regard, the paper recommends the use of the proposed estimator in estimating population parameters such as population total in the presence of high-dimensional datasets. |
|---|---|
| ISSN: | 2314-4785 |