The Research Development and Technical Framework of Functional Data Analysis
Bibliographic record
Abstract
Functional Data Analysis(FDA) has been developed into a Multivariate Statistical Analysis(MSA) method based on thoughts of converting discrete data into functional ones since 1980s,which portrayed more generalized and more profound statistical relationship through the functional analysis.The basic idea of FDA is brought up by James O.Ramsay,a professor of Canada McGill University and Bernard W.Silverman,from Oxford.Many other world-famous scholars have contributed to the idea.The method is now widely used in economics,biology,meteorology,psychology,industry and other fields.Functional Data Analysis regards observed data as a whole,but not just the order of the individual observations.Functions essentially refer to the inner structure of data,but not their intuitive form.This paper briefly reviews the development history of FDA and tracks domestic and international research trends.It introduces the FDA research technical framework and the differences between FDA research technical framework and the traditional method of multivariate statistical analysis.Attention focus on the application of FDA in economics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.024 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".