citation_context stringlengths 123 1.05k | content_license stringclasses 1
value | context_method stringclasses 1
value | correctness_verified bool 1
class | doi stringclasses 68
values | external_url stringlengths 22 849 ⌀ | kind stringclasses 3
values | post_id int64 45.1k 677k | post_url stringlengths 40 131 | product stringclasses 1
value | record_id stringlengths 50 50 | split stringclasses 1
value | thread dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
ght)
\end{aligned}$$
(a Beta function, arising e.g. from normalizing a Beta prime distribution (https://en.wikipedia.org/wiki/Beta_prime_distribution#Generalization)).
Applying this in the case $\alpha = \nu + n$ and $k = k_n$ says
$$\begin{aligned}
2\int_ | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Beta_prime_distribution#Generalization | external_url | 676,851 | https://stats.stackexchange.com/a/676851 | citations | Scientific-Citation-Graph:56ca72be4ec598dcbd102e90 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<p><strong>I offer an elementary solution.</strong> It requires one substitution in an ordinary integral which, when repeated for each variable, yields a product of Beta function values.</p>\n<p>There are other solutions that might provide simp... |
ntercept in your case) instead. More general random-effects models (e.g., with the R coxme package (https://cran.r-project.org/package=coxme)) would allow modeling of nested correlation structures (e.g., individuals within working groups).
| CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://cran.r-project.org/package=coxme | external_url | 676,835 | https://stats.stackexchange.com/a/676835 | citations | Scientific-Citation-Graph:65433d2cde6d96c4609de978 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<blockquote>\n<p>I am considering using Kaplan–Meier curves and the log-rank test to compare the time-to-next-PR distributions between abandoned and non-abandoned focal PRs.</p>\n</blockquote>\n<p>That could be a start, but it won't allow you to... |
ested correlation structures (e.g., individuals within working groups).
The R survival package (https://cran.r-project.org/package=survival) has many vignettes illustrating aspects of survival modeling.
*The Kaplan-Meier survival es | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://cran.r-project.org/package=survival | external_url | 676,835 | https://stats.stackexchange.com/a/676835 | citations | Scientific-Citation-Graph:6cbbe4d28c395b9cb450cf53 | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<blockquote>\n<p>I am considering using Kaplan–Meier curves and the log-rank test to compare the time-to-next-PR distributions between abandoned and non-abandoned focal PRs.</p>\n</blockquote>\n<p>That could be a start, but it won't allow you to... |
urvival model be more appropriate?
Yes.
The title of Chapter 8 of Therneau and Grambsch (https://www.springer.com/us/book/9780387987842) is "Multiple Events per Subject." Quoting:
A major issue in extending proportional hazards | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://www.springer.com/us/book/9780387987842 | external_url | 676,835 | https://stats.stackexchange.com/a/676835 | citations | Scientific-Citation-Graph:453b5ea997c363b1fcfe624b | train | {
"accepted_answer_id": null,
"answers": [
{
"answer_html": "<blockquote>\n<p>I am considering using Kaplan–Meier curves and the log-rank test to compare the time-to-next-PR distributions between abandoned and non-abandoned focal PRs.</p>\n</blockquote>\n<p>That could be a start, but it won't allow you to... |
, does $-2\log\lambda$ has a general asymptotic distribution in such case?
[image: ; source: https://i.sstatic.net/cWv54DMg.png] (https://i.sstatic.net/cWv54DMg.png)
# R code for simulation
nsim=1000 # number of simulation | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 676,842 | https://stats.stackexchange.com/questions/676842/the-asymptotic-distribution-of-likelihood-ratio-statistic-when-the-null-hypothes | citations | Scientific-Citation-Graph:78d9c973fd18190708db1051 | train | {
"accepted_answer_id": 676849,
"answers": [
{
"answer_html": "<p>It is still true that the asymptotic null distribution of the LRT is the null distribution of a local Gaussian shift alternative. In your <span class=\"math-container\">$N(\\mu,\\sigma^2)$</span> example that means the asymptotic null dist... | |
^2_1$s. The red line shows this CDF on your plot.
[image: enter image description here; source: https://i.sstatic.net/Qsmelzjn.png] (https://i.sstatic.net/Qsmelzjn.png)
For the more general case you have a limiting bivariate N | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | external_url | 676,849 | https://stats.stackexchange.com/a/676849 | citations | Scientific-Citation-Graph:f5fd0d7ebd2f6fa40e5746dd | train | {
"accepted_answer_id": 676849,
"answers": [
{
"answer_html": "<p>It is still true that the asymptotic null distribution of the LRT is the null distribution of a local Gaussian shift alternative. In your <span class=\"math-container\">$N(\\mu,\\sigma^2)$</span> example that means the asymptotic null dist... | |
bull shape) as a function of covariates, but it's possible for example with the R flexsurv package (https://cran.r-project.org/package=flexsurv).
To summarize:
There can be interest in whether (and in what direction) the Weibull shape | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://cran.r-project.org/package=flexsurv | external_url | 676,861 | https://stats.stackexchange.com/a/676861 | citations | Scientific-Citation-Graph:932e5001b4696abd6c4ba91e | train | {
"accepted_answer_id": 676856,
"answers": [
{
"answer_html": "<p>The scale parameter is routinely analysed in survival models, because the scale parameter is a location parameter for <span class=\"math-container\">$\\log T$</span>. Accelerated-failure models are of the form\n<span class=\"math-container... |
ion (https://stats.stackexchange.com/a/468333/28500). Yours is the first optional parameterization (https://en.wikipedia.org/wiki/Weibull_distribution#First_option) in Wikipedia. The Rodríguez notes linked above use a form similar to Wikipedia's "Standard paramet | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Weibull_distribution#First_option | external_url | 676,861 | https://stats.stackexchange.com/a/676861 | citations | Scientific-Citation-Graph:8be87220e13ecc497a4d7785 | train | {
"accepted_answer_id": 676856,
"answers": [
{
"answer_html": "<p>The scale parameter is routinely analysed in survival models, because the scale parameter is a location parameter for <span class=\"math-container\">$\\log T$</span>. Accelerated-failure models are of the form\n<span class=\"math-container... |
"a. The Rodríguez notes linked above use a form similar to Wikipedia's \"Standard parameterization,(...TRUNCATED) | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://en.wikipedia.org/wiki/Weibull_distribution#Standard_parameterization | external_url | 676,861 | https://stats.stackexchange.com/a/676861 | citations | Scientific-Citation-Graph:ce1746f1e9497680afa3d5b3 | train | {"accepted_answer_id":676856,"answers":[{"answer_html":"<p>The scale parameter is routinely analysed(...TRUNCATED) |
"To see more about the basis for Thomas Lumley's answer, look at these course notes (https://grodri.(...TRUNCATED) | CC BY-SA 4.0 | literal text window; null if identifier only in original HTML | false | null | https://grodri.github.io/survival/ParametricSurvival.pdf | external_url | 676,861 | https://stats.stackexchange.com/a/676861 | citations | Scientific-Citation-Graph:b186a17c60b081391e3d9a7f | train | {"accepted_answer_id":676856,"answers":[{"answer_html":"<p>The scale parameter is routinely analysed(...TRUNCATED) |
RegalFire Scientific reference graph
RegalFire — AI Data Foundry
Literal external URLs and DOI-like references with mechanically extracted text context and complete forum attribution.
Verified scope
Records: 888; distinct source threads: 221; unique answers represented: 320. Domain thread counts: {"statistics": 61, "computational_science": 71, "biology": 89}. Actual record splits: {"train": 543, "test": 109, "validation": 109, "holdout": 127}.
Each record includes a canonical thread, all source answers, author attribution, original HTML, mechanically converted text, revisions and raw hash/API provenance. record_id is the primary key; thread_id is site-qualified, while original numeric question/answer IDs are retained. Citation record counts count edges; answer/thread totals are deduplicated within the product.
Sources and licensing
Official Stack Exchange API 2.3 source snapshots from Cross Validated (stats), Computational Science (scicomp) and Biology (biology), as applicable to the recorded domain distribution. Native new capture windows used the 100 most recently created answered questions for Stats and Scicomp. Biology reuses a deterministic 100-thread sample from the previously validated local capture; the original public Biology repo is unchanged. After source and additional cross-post sensitivity exclusions, the family source corpus contains 288 threads.
All retained current question and answer contributions explicitly returned CC BY-SA 4.0. This is verified per post, not assumed for historical posts. Bionic raw contained 2.5/3.0 contributions but lacked sufficient independently captured provenance/revisions; those inputs are excluded. Historical revision license fields, when supplied, are retained. See SOURCE_LICENSE_AUDIT.json and official source licensing.
Keep question titles, Biology/Computational Science/Cross Validated source identity, contributor names and available profile links, original post URLs, license links and transformation notices when redistributing. Each thread includes revision contributors and their source identifiers. Null profiles mean unavailable/deleted information; no profile is invented. Share adaptations under the applicable same or compatible license and impose no additional restrictions. Packaging does not replace source ownership or licensing. Third-party quotations and referenced images/papers may have separate rights; external assets are not downloaded or repackaged.
Deterministic product rules
QA chooses an accepted answer when present, otherwise maximum community score >=1 with lowest answer ID as tie breaker. accepted_answer remains null for a score-based fallback. Ranking requires at least two answers. RAG signals describe counts, acceptance, scores, references and time spans: they are not semantic disagreement annotations, verified difficulty or correct-answer labels. Code selection uses preformatted blocks of at least 20 characters with a documented code-syntax screen. Language is explicitly unspecified; snippets may be incomplete/pseudocode and are not executed. Citation extraction uses literal external URLs/DOI-like strings; context is a literal ±100-character text window or null when only HTML contains the reference. No DOI resolution, paper download or citation correctness claim.
Transformations and privacy
No LLM rewriting. Original API HTML remains intact. Text mechanically decodes HTML entities, preserves source text, code whitespace and TeX strings, adds literal link destinations and image-reference markers, and flattens superscript/subscript formatting. Code blocks retain exact decoded preformatted contents with SHA-256. Treat HTML/links/code as untrusted; sanitize rendered HTML and never execute snippets without a sandbox.
Patient-specific/personal medical histories, direct treatment requests and email-like identifiers are screened across both questions and answers and excluded by an English heuristic. General scientific medical discussions can remain with sensitivity flags. This is not comprehensive PII detection or clinical review. No medical advice. Rejections contain IDs/URLs/reasons without the excluded bodies. SENSITIVITY_REPORT.json describes the family screen.
Splits and overlap
One shared source-thread grouping is used by all five family products: normalized title/body/exact answer hashes and exhaustive question word-trigram Jaccard >=0.80 unite obvious duplicates. SHA-256 buckets assign 70/10/10/10 train/validation/test/holdout; observed proportions vary. Every occurrence of a thread has the same split across products. Products overlap intentionally; do not add their volumes as unique source content or concatenate one product’s held-out records into another’s training. These are publicly available forum posts: holdout is not a contamination-resistant private benchmark and lexical checks do not guarantee absence of semantic paraphrases.
Validation and reproducibility
Independent source verifiers check schema, UTF-8, author/revision attribution, actual license fields, HTTP/raw hashes, HTML/text preservation, unique IDs, complete answers and accepted consistency. A separate product engine checks eligibility, code/citation extraction, source equality, duplicates, exact split files and leakage. Five product-specific validator entry points use this engine; no builder is imported. The family passed 15 controlled corruption tests and byte-identical regeneration. validation_report.json records this product’s results; ROBUSTNESS_REPORT.json records family tests.
Run python validate_dataset.py --source-root <scientific_product_factory> with the local capture archive and source pipelines. Full raw/source validation requires that archive. Public release files omit raw bodies containing excluded data; SOURCE_MANIFEST.json records their hashes, API URLs and collection times. Fresh API collection can differ from the original snapshot. MANIFEST.json hashes every release file except itself.
Intended use and limitations
Citation/provenance tooling and scientific RAG engineers. No expert scientific correctness labels, model accuracy claims, validated code execution, clinical review or resolved-citation guarantees. Community votes are not scientific truth. This is a bounded starter evaluation corpus; expansion depends on source completeness, licensing and available API quota. Statistical QA/code subsets are small and recent; do not treat them as representative of a whole research discipline.
Custom / Private Dataset Work
RegalFire builds custom AI datasets, evaluation sets and reproducible data pipelines for research and production systems.
Client-specific reference schemas, proprietary document integration, DOI resolution as a separate checked stage, continuous licensed ingestion.
Contact: ootiris@gmail.com Hugging Face: RegalFire
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