🔗 Who cites «Statsmodels: econometric and statistical modeling with Python»

75 papers of our library cite it (of the 149,439 whose bibliographies we read) · 11 with a page here, the most cited first · ← back to «Statsmodels: econometric and statistical modeling with Python»

1. SciPy 1.0 — Fundamental Algorithms for Scientific Computing in PythonarXiv:1907.10121 (Nature Methods 17, 261 (2020))Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, et al.2019 · 38,334 citations
2. Diachronic Word Embeddings Reveal Statistical Laws of Semantic ChangeAnnual Meeting of the Association for Computational Linguistics (ACL)William L. Hamilton, Jure Leskovec, Dan Jurafsky2016 · 1,278 citations
3. Behavior Analysis of NLI Models: Uncovering the Influence of Three Factors on RobustnessConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Ivan Sanchez, Jeff Mitchell, Sebastian Riedel2018 · 56 citations
4. Predicting In-Game Actions from Interviews of NBA PlayersComputational LinguisticsNadav Oved, Amir Feder, Roi Reichart2020 · 24 citations
5. From Insanely Jealous to Insanely Delicious: Computational Models for the Semantic Bleaching of English IntensifiersProceedings for the International Workshop on Computational Approaches to Language ChangeYiwei Luo, Dan Jurafsky, Beth Levin2019 · 15 citations
6. From Calculation to Adjudication: Examining LLM judges on Mathematical Reasoning TasksarXiv:2409.04168Andreas Stephan, Dawei Zhu, Matthias Aßenmacher, et al.2024 · 13 citations
7. An investigation of numeral quantifiers in EnglishGlossa a journal of general linguisticsCole Ira Brendel2019 · 6 citations
8. Controlling Reading Ease with Gaze-Guided Text GenerationConference of the European Chapter of the Association for Computational Linguistics (EACL)Andreas Säuberli, Darja Jepifanova, Diego Frassinelli, et al.2026 · 3 citations
9. Child-directed speech facilitates production, not comprehension, in BabyLMsConference on Computational Natural Language Learning (CoNLL)Bastian Bunzeck, Sina Zarrieß2026
10. The Effects of Structured LLM-Generated Feedback on Programming Assignment PerformanceWorkshop on Innovative Use of NLP for Building Educational Applications (BEA)Tsvetomila Mihaylova, Evanfiya Logacheva, Arto Hellas, et al.2026
11. What Makes a Good Query? Measuring the Impact of Human-Confusing Linguistic Features on LLM PerformanceFindings of the Association for Computational LinguisticsWilliam Watson, Nicole Cho, Sumitra Ganesh, et al.2026

64 more papers of our library cite it; they have no page here yet.

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