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by
Mitra, Ayan, author.
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novel fast, parallel, computationally efficient methods to perform multislice statistical reconstruction
by
Mickelson, Laura A., author.
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statistical methods were used to summarize the data. Multiple linear regression analysis was used to examine
by
Robinson, Amanda E. R., author.
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severity and the intent to seek help. There was insufficient follow-up data to support statistical analysis
by
Mahapatra, Suchismit, author. (orcid)0000-0002-1851-4195
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(NLSDR) methods have proved to be an indispensable tool. However, standard NLSDR methods, e.g. Isomap or
by
Holman, Bryan Paul, author.
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statistical methods have been developed to correct for these errors, the resulting post processed forecasts
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Kim, Jinseok, author.
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misidentified authors via first-initial and all-initials disambiguation methods. In addition, I simulate the
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Sambamurthy, Nikitha, author.
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in more authentic settings (i.e., scenarios that cannot be prepared for); however, the methods of
by
Wible, Emily Elizabeth, author.
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-months using a scanning-electron-microscope. Student t-tests and ANOVA statistical analyses were used at
by
Gomes, Luís Manuel dos Santos, author.
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extraction of new TEs, the creation of statistical machine translation systems, and the creation of bilingual
by
Jiao, Feiran, author.
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than quantitative differences. In this thesis, we develop new statistical methods for variable
by
Ding, Dong, author.
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develop even more powerful methods. In many statistical methods that focus on ordinal data, it is assumed
by
Knight, David Robert, author.
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statistical comparisons relevant or the previous identification of a representative number of similar genes

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