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DTSTART;TZID=America/New_York:20260409T160000
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SUMMARY:Factor Analysis and Questions of Causation
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday April 9 at 4:00pm\nHSPH\, FXB 301 \nTyler VanderWeele\, PhD\, John L. Loeb And Frances Lehman Loeb\, Professor of Epidemiology\, Faculty Affiliate – Department of Biostatistics\, Harvard T.H. Chan School of Public Health \nFactor analysis is often employed to evaluate the extent to which a single factor suffices to explain the variation in individual indicators. \nHowever\, often the resulting factors are interpreted as corresponding to a structural univariate latent variable that is itself causally efficacious. This assumption is so strong that it has empirically testable implications\, even though the supposed latent variable is unobserved; statistical tests are proposed that can often reject this assumption. Factor analysis also suffers from the inability to distinguish between associations arising from causal versus conceptual relations; if two supposed factors were to causally affect one another then\, over time\, the process will converge to a factor model wherein only a single factor can be detected. When both positively and negatively worded items are used\, factor analysis can also suggest that two factors are present even if the data were in fact generated by one. Examples of these various phenomena are given. \nDespite these limitations\, factor analyses can nevertheless often be informative\, but requires an appropriate reinterpretation of results as reflecting a combination of causal\, conceptual\, and distributional relations. \n\nColloquium Seminar Series \n\n 
URL:https://ds.dfci.harvard.edu/event/factor-analysis-and-questions-of-causation/
CATEGORIES:Seminar
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DTSTART;TZID=America/New_York:20260416T160000
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SUMMARY:When Large p Is a Blessing
DESCRIPTION:HSPH Biostatistics and DFCI Data Science Colloquium\nThursday April 9 at 4:00pm\nHSPH\, FXB 301 \nZhijin Wu\, PhD\, Professor of Biostatistics\, Brown University \nBiomedical research has benefited tremendously from the breakthroughs in biotechnology in the last two decades that enabled simultaneous quantifications of a large number of biomolecules (DNA/RNA/proteins). Such data collected at the -omics scale often have a “small N large p” structure and the “large p” is often seen as a curse of Dimensionality. \nHowever\, sometimes the nature of high throughput data acquisition can be useful and provides information that is only accessible in “large p” settings. I will present several examples of our methodology development that takes advantage of the “large p” nature in genomic studies that lead to improved detection of molecular signals. \n\nColloquium Seminar Series \n\n 
URL:https://ds.dfci.harvard.edu/event/when-large-p-is-a-blessing/
CATEGORIES:Seminar
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