On New Invertible Skew and Symmetric Distributions
Bibliographic record
Abstract
The study of new distributions is necessary concerning data that often do not fit to already known distributions. The aim of this work is to present new skew symmetric and asymmetric distributions, their applications in economic, financial, and climate areas. There are three types of distributions proposed: an asymmetric distribution, a symmetric version on the real line, and a skew distribution generated from the symmetric version. Those are applied to analyze the following: three data sets and to produce graphical representations of the fit. The climate data refer to a set containing the annual rainfall in the city of Los Angeles, between 1878 and 1998 provided online at the National Weather Service (NWS) of the United States of America. The applications in the economic-financial area have two data sets; the first of which refers to the percentage returns calculated from daily exchange rates for the Real (Brazil) and Dollar (Canada) against the U.S. dollar between January 3, 2000 and May 20, 2011 released by the Federal Reserve; and the second deals with the energy consumption in kg of oil per capita by 136 countries in 1997 released by the World Bank.
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.
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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".