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    Multiple imputation of missing multivariate atmospheric chemistry time series data from Denali National Park

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    Charoonsophonsak_C_2020.pdf
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    Author
    Charoonsophonsak, Chanachai
    Chair
    Goddard, Scott
    Committee
    Barry, Ronald
    McIntyre, Julie
    Short, Margaret
    Metadata
    Show full item record
    URI
    http://hdl.handle.net/11122/11859
    Abstract
    This paper explores a technique where we impute missing values for an incomplete dataset via multiple imputation. Incomplete data is one of the most common issues in data analysis and often occurs when measuring chemical and environmental data. The dataset that we used in the model consists of 26 atmospheric particulates or elements that were measured semiweekly in Denali National Park from 1988 to 2015. The collection days were alternating between three and four days apart from 3/2/88 - 9/30/00 and being consistently collected every three days apart from 10/3/00 - 12/29/15. For this reason, the data were initially partitioned into two in case the separation between collection days would have an impact. With further analysis, we concluded that the misalignments between the two datasets had very little or no impact on our analysis and therefore combined the two. After running five Markov chains of 1000 iterations we concluded that the model stayed consistent between the five chains. We found out that in order to get a better understanding of how well the imputed values did, more exploratory analysis on the imputed datasets would be required.
    Description
    Master's Project (M.S.) University of Alaska Fairbanks, 2020
    Date
    2020-05
    Type
    Master's Project
    Collections
    Mathematics and Statistics

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