Asymmetric Laplace Distributions
Bibliographic record
Abstract
Chapter 3 is devoted to asymmetric Laplace distributions — a skewed family of distributions that in our opinion is the most appropriate skewed generalization of the classical Laplace law. In the last several decades, various forms of skewed Laplace distributions have sporadically appeared in the literature. One of the earliest is due to McGill (1962), who considers distributions with p.d.f.$$ f(x) = \left\{ {\begin{array}{*{20}c} {\frac{{\varphi _1 }} {2}e^{ - \varphi _1 |x - \theta |} , x \leqslant \theta ,} \\ {\frac{{\varphi _2 }} {2}e^{ - \varphi _2 |x - \theta |} , x > \theta ,} \\ \end{array} } \right. $$ (3.0.1) while Holla and Bhattacharya (1968) study the distribution with p.d.f. $$ f(x) = \left\{ {\begin{array}{*{20}c} {p\varphi e^{ - \varphi \left| {x - \theta } \right|} , x \leqslant \theta ,} \\ {(1 - p)\varphi e^{ - \varphi \left| {x - \theta } \right|} , \theta < x,} \\ \end{array} } \right. $$ (3.0.2) where 0 < p < 1. Lingappaiah (1988) derived some properties of (3.0.1), terming the distribution two-piece double exponential. Poiraud-Casanova and Thomas-Agnan (2000) exploited a skewed Laplace distribution with p.d.f. $$f\left( x \right) = \alpha \left( {1 - \alpha } \right)\left\{ {\begin{array}{*{20}{c}} {{{e}^{{ - \left( {1 - \alpha } \right)\left| {x - \theta } \right|}}}, for x < \theta ,} \hfill \\ {{{e}^{{ - \alpha \left| {x - \theta } \right|}}}, for x \geqslant \theta ,} \hfill \\ \end{array} } \right. $$ (3.0.3) and α∈(0,1), to show the equivalence of certain quantile estimators.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.033 | 0.009 |
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".