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STATISTICAL INFERENCE FOR HIGH DIMENSIONAL MODELS IN GENOMICS AND
MICROBIOME
Piauí Lu
A DISSERTATION
in
Epidemiology and Biostatistics
Presented to the Faculties of the University of Pennsylvania
in
Partial
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What is problems in high-dimensional statistics?
High-dimensional statistics deals with situations where the number of variables is much larger than the number of observations. This leads to various challenges, including overfitting, computational difficulties, and the risk of spurious correlations.
Who is required to file problems in high-dimensional statistics?
Researchers, statisticians, and data scientists working in fields that utilize high-dimensional data, such as genomics, finance, and machine learning, may need to address these problems.
How to fill out problems in high-dimensional statistics?
Addressing problems in high-dimensional statistics often involves choosing appropriate models, regularization techniques, and validating results through cross-validation or similar methods.
What is the purpose of problems in high-dimensional statistics?
The purpose is to understand how to analyze and interpret data with a high number of variables and to derive valid statistical inferences despite the challenges that arise.
What information must be reported on problems in high-dimensional statistics?
Researchers must report the methodologies used, assumptions made, results obtained, and the implications of their findings, while acknowledging the limitations of their analysis.
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