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SUMMARY:Comparative Frameworks for Relative Multivariate Analysis - Ganesh
  Karapakula (Economics Faculty - University of Cambridge)
DTSTART:20231117T153000Z
DTEND:20231117T170000Z
UID:TALK208021@talks.cam.ac.uk
CONTACT:97804
DESCRIPTION:In this paper\, I develop new methods for comparative analysis
  of multivariate data\, such as multiple financial asset returns\, paired 
 biomedical data\, performance metrics across cross-validation folds or tes
 t sets for various machine learning algorithms or hyperparameter choices\,
  user ratings of various products\, voter ratings of political candidates 
 and their policy proposals\, or experimental data arranged in complete blo
 ck designs. My approaches involve transforming the original data into boun
 ded relative measures and are grounded in axiomatic frameworks\, which are
  inspired by the Ricardian notion of comparative advantage. The resulting 
 statistical procedures serve as general alternatives to some widely used n
 onparametric tests\, such as the sign test and the Friedman test. My metho
 dologies have applications in several areas. For example\, they are useful
  for conducting portfolio analysis and technical analysis in finance from 
 a relative perspective. Users of online marketplaces or streaming services
  or review platforms may find relative ratings of products and services he
 lpful. The general frameworks are also useful for building machine learnin
 g algorithms or statistical models that minimize loss functions based on i
 nterpretable relative prediction errors. In addition\, some of the propose
 d relative measures can themselves be practically treated as outcomes for 
 causal analysis in some settings.
LOCATION:Centre for Mathematical Sciences\, MR12
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