Dissertation/Thesis Abstract

Reduction of Confidence Interval Length for Small-Normal Data Sets Utilizing Bootstrap and Conformal Prediction Methods
by Chavarria, Pablo C., M.S., California State University, Long Beach, 2018, 45; 10840988
Abstract (Summary)

It is of common practice to evoke a t-confidence interval for estimating the mean of a small data set with an assumed Normal distribution. These t-intervals are known to be wide to account for the lack of information. This thesis will focus on exploring ways to reduce the length of the interval, while preserving the level of confidence. Simulated small normal data sets will be used to analyze a combination of Bootstrapping and Conformal Prediction methods, while investigating measures of spread, such as standard deviation, kurtosis, excess CS kurtosis, skewness, etc. to create a criterion for when this combination of methodologies will greatly reduce the interval length. The goal is to be able to use the insight simulated data have to offer in order to apply to real world data. If time permits, a further look into the theory behind the results will be explored.

Indexing (document details)
Advisor: Suaray, Kagba
Commitee: Korosteleva, Olga, Moon, Hojin
School: California State University, Long Beach
Department: Mathematics and Statistics
School Location: United States -- California
Source: MAI 58/02M(E), Masters Abstracts International
Subjects: Statistics
Keywords: Bootstrap, Confidence intervals, Conformal prediction, Semi parametric, Small data, Statistics
Publication Number: 10840988
ISBN: 9780438633735
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