The Research Development and Technical Framework of Functional Data Analysis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it