Date of Award:
5-1996
Document Type:
Thesis
Degree Name:
Master of Science (MS)
Department:
Mathematics and Statistics
Committee Chair(s)
Michael Minnotte
Committee
Michael Minnotte
Committee
Daniel Coster
Committee
Ronald Canfield
Abstract
The kernel persists as the most useful tool for density estimation. Although, in general, fixed kernel estimates have proven superior to results of available variable kernel estimators, Minnotte's mode tree and mode existence test give us newfound hope of producing a useful adaptive kernel estimator that triumphs when the fixed kernel methods fail. It improves on the fixed kernel in multimodal distributions where the size of modes is unequal, and where the degree of separation of modes varies. When these latter conditions exist, they present a serious challenge to the best of fixed kernel density estimators. Capitalizing on the work of Minnotte in detecting multimodality adaptively, we found it possible to determine the bandwidth h adaptively in a most original fashion and to estimate the mixture normals adaptively, using the normal kernel with encouraging results.
Checksum
781dcfe7428205c20e821fcc4c418432
Recommended Citation
Jawhar, Nizar Sami, "Adaptive Density Estimation Based on the Mode Existence Test" (1996). All Graduate Theses and Dissertations, Spring 1920 to Summer 2023. 7129.
https://digitalcommons.usu.edu/etd/7129
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