On Kernel-Based Mode Estimation Using Different Stratified Sampling Designs
Document Type
Article
Publication Date
6-2019
Publication Title
Journal of Statistical Theory and Practice
DOI
10.1007/s42519-018-0034-3
ISSN
1559-8616
Abstract
In the literature, the properties and the application of mode estimation is considered under simple random sampling and ranked set sampling (RSS). We investigate some of the asymptotic properties of kernel density-based mode estimation using stratified simple random sampling (SSRS) and stratified ranked set sampling designs (SRSS). We demonstrate that kernel density-based mode estimation using SRSS and SSRS is consistent, asymptotically normally distributed and using SRSS has smaller variance than that under SSRS. Improved performance of the mode estimation using SRSS compared to SSRS is supported through a simulation study. We will illustrate the method by using biomarker data collected in China Health and Nutrition Survey data.
Recommended Citation
Samawi, Hani, Haresh Rochani, Jingjing Yin, Robert L. Vogel.
2019.
"On Kernel-Based Mode Estimation Using Different Stratified Sampling Designs."
Journal of Statistical Theory and Practice, 13 (2): Taylor & Francis Online.
doi: 10.1007/s42519-018-0034-3 source: https://www.researchgate.net/publication/331106876_On_Kernel-Based_Mode_Estimation_Using_Different_Stratified_Sampling_Designs
https://digitalcommons.georgiasouthern.edu/bee-facpubs/126
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