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Non-Parametric Classification Of Time Series Using Permutation Ordinal Statistics, Aldo Duarte Vera Tudela
Non-Parametric Classification Of Time Series Using Permutation Ordinal Statistics, Aldo Duarte Vera Tudela
LSU Master's Theses
The present thesis explores some approaches to classify time series without prior statistical information using the concept of permutation entropy. Motivated by the results from a previous published and relevant work that set similarity relationships between EEG time series, a reproduction of the proposed approach was performed giving negative results. The failure to reproduce those results led to the conclusion that the approach of building statistics from permutation patterns have to be complemented with another metric in order to be used for classification purposes. The concept of Total Variation Distance (TVD) was then used to develop three algorithms to classify …
Information Analysis Of Dna Sequences, Riyazuddin Mohammed
Information Analysis Of Dna Sequences, Riyazuddin Mohammed
LSU Master's Theses
The problem of differentiating the informational content of coding (exons) and non-coding (introns) regions of a DNA sequence is one of the central problems of genomics. The introns are estimated to be nearly 95% of the DNA and since they do not seem to participate in the process of transcription of amino-acids, they have been termed “junk DNA.” Although it is believed that the non-coding regions in genomes have no role in cell growth and evolution, demonstration that these regions carry useful information would tend to falsify this belief. In this thesis, we consider entropy as a measure of information …