- Mar 2024
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Abstract
结论：预测结果，好于MOST（MO估计系统地低估了湍流通量的大小，改善了与观测值和减小与观测通量偏离的总幅度。）,不同地点的泛化能力 不足：不含物质通量，预测结果待提升，结果因稳定性而异常，不同季节的泛化能力，运用了不易获得的变量（找到最小观测集）
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Annotators
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- Feb 2024
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github.com github.com
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Regression in 3.13: custom matcher hash argument improperly converted to keyword args, results in ArgumentError
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- Oct 2023
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typeshare.co typeshare.co
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- Apr 2023
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ar5iv.labs.arxiv.org ar5iv.labs.arxiv.org
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While past work has characterized what kinds of functions ICL can learn (Garg et al., 2022; Laskin et al., 2022) and the distributional properties of pretraining that can elicit in-context learning (Xie et al., 2021; Chan et al., 2022), but how ICL learns these functions has remained unclear. What learning algorithms (if any) are implementable by deep network models? Which algorithms are actually discovered in the course of training? This paper takes first steps toward answering these questions, focusing on a widely used model architecture (the transformer) and an extremely well-understood class of learning problems (linear regression).
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inflecthealth.medium.com inflecthealth.medium.com
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This is the space where AI can thrive, tirelessly processing these countless features of every patient I’ve ever treated, and every other patient treated by every other physician, giving us deep, vast insights. AI can help do this eventually, but it will first need to ingest millions of patient data sets that include those many features, the things the patients did (like take a specific medication), and the outcome.
AI tools yes, not ChatGPT though. More contextualising and specialisation needed. And I'd add the notion that AI might be necessary as temporary fix, on our way to statistics. Its power is in weighing (literally) many more different factors then we could statistically figure out, also because of interdependencies between factors. Once that's done there may well be a path to less blackbox tooling like ML/DL towards logistic regression: https://pubmed.ncbi.nlm.nih.gov/33208887/ [[Machine learning niet beter dan Regressie 20201209145001]]
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- Jan 2023
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tedgioia.substack.com tedgioia.substack.com
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I've seen a bunch of people sharing this and repeating the conclusion: that the success is because the CEO loves books t/f you need passionate leaders and... while I think that's true, I don't think that's the conclusion to draw here. The winning strategy wasn't love, it was delegation and local, on the ground, knowledge.
This win comes from a leader who acknowledges people in the stores know their communities and can see and react faster to sales trends in store... <br /> —Aram Zucker-Scharff (@Chronotope@indieweb.social) https://indieweb.social/@Chronotope/109597430733908319 Dec 29, 2022, 06:27 · Mastodon for Android
Also heavily at play here in their decentralization of control is regression toward the mean (Galton, 1886) by spreading out buying decisions over a more diverse group which is more likely to reflect the buying population than one or two corporate buyers whose individual bad decisions can destroy a company.
How is one to balance these sorts of decisions at the center of a company? What role do examples of tastemakers and creatives have in spaces like fashion for this? How about the control exerted by Steve Jobs at Apple in shaping the purchasing decisions of the users vis-a-vis auteur theory? (Or more broadly, how does one retain the idea of a central vision or voice with the creative or business inputs of dozens, hundreds, or thousands of others?)
How can you balance the regression to the mean with potentially cutting edge internal ideas which may give the company a more competitive edge versus the mean?
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- Nov 2022
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cccrg.cochrane.org cccrg.cochrane.org
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PDF summary by Cochrane for planning a meta-analysis at the protocol stage. Gives guidance on how to anticipate & deal with various types of heterogeneity (clinical, methodological , & statistical). Link to paper
Covers - ways to assess heterogeneity - courses of action if substantial heterogeneity is found - methods to examine the influence of effect modifiers (either to explore heterogeneity or because there's good reason to suggest specific features of participants/interventions/study types will influence effects of the intervention. - methods include subgroup analyses & meta-regression
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- Aug 2022
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Mertens, G., Lodder, P., Smeets, T., & Duijndam, S. (2021). Fear of COVID-19 predicts vaccination willingness 14 months later. PsyArXiv. https://doi.org/10.31234/osf.io/rt7u4
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- Sep 2021
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www.reddit.com www.reddit.com
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If you want to find the bug, you can run a mozregression to find what broke it (using 70 as your last known good release and 71 as your bad release).
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- Jul 2021
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Antonoyiannakis, M. (2021). Does Publicity in the Science Press Drive Citations? ArXiv:2104.13939 [Physics]. http://arxiv.org/abs/2104.13939
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- Jun 2021
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docs.gitlab.com docs.gitlab.com
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Only test the happy path, but make sure to add a test case for any regression that couldn’t have been caught at lower levels with better tests (for example, if a regression is found, regression tests should be added at the lowest level possible).
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- May 2021
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www.postwachstum.de www.postwachstum.de
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Doch die stichhaltigere Erklärung für die Unersättlichkeit des Status- und Machtstrebens liegt in der Regression, d.h. der erlernten Unfähigkeit, im umfassenden Gebrauch der Gesamtheit der eigenen Anlagen Sinn und Erfüllung zu finden, und der daraus resultierenden Verführbarkeit durch die attraktiven Eigenschaften der Macht.“
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- Apr 2021
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psyarxiv.com psyarxiv.com
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Murat Baldwin, M., Fawns-Ritchie, C., Altschul, D., Campbell, A., Porteous, D., & Murray, A. L. (2021, April 25). Brief Report: Predictors of Adolescent Mental Health and Wellbeing During the COVID-19 Pandemic. https://doi.org/10.31234/osf.io/yra6v
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- Mar 2021
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www.frontiersin.org www.frontiersin.org
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Spiro, Neta, Rosie Perkins, Sasha Kaye, Urszula Tymoszuk, Adele Mason-Bertrand, Isabelle Cossette, Solange Glasser, and Aaron Williamon. ‘The Effects of COVID-19 Lockdown 1.0 on Working Patterns, Income, and Wellbeing Among Performing Arts Professionals in the United Kingdom (April–June 2020)’. Frontiers in Psychology 11 (2021). https://doi.org/10.3389/fpsyg.2020.594086.
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jedkolko.com jedkolko.com
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Jed. ‘The Geography of the COVID19 Third Wave | Jed Kolko’. Accessed 25 February 2021. http://jedkolko.com/2020/10/18/the-geography-of-the-covid19-third-wave/.
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tobeagile.com tobeagile.com
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Why separate out red tests from green tests? Because my green tests serve a fundamentally different purpose. They are there to act as a living specification, validating that the behaviors work as expected. Regardless of whether they are implemented in a unit testing framework or an acceptance testing framework, they are in essence acceptance tests because they’re based upon validating behaviors or acceptance criteria rather than implementation details.
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en.wikipedia.org en.wikipedia.org
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Sometimes a change impact analysis is performed to determine an appropriate subset of tests
Hey, I do that sometimes so I can run a smaller/faster subset of tests. Didn't know it had a fancy name though.
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non-regression testing
That would probably be a better name because you're actually testing/verifying that there hasn't been any regression.
You're testing for the absence of regression. But I guess testing for one also tests for the other, so it probably doesn't matter. (If something is not true you know it is false, etc.)
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Regression testing (rarely non-regression testing[1]) is re-running functional and non-functional tests to ensure that previously developed and tested software still performs after a change.[2] If not, that would be called a regression.
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- Feb 2021
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psyarxiv.com psyarxiv.com
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Długosz, Piotr. ‘PREDICTORS OF PSYCHOLOGICAL STRESS OCCURRING AFTER THE FIRST WAVE OF THE COVID-19 PANDEMIC IN POLAND’. PsyArXiv, 24 February 2021. https://doi.org/10.31234/osf.io/2k8px.
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www.thelancet.com www.thelancet.com
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Li, You, Harry Campbell, Durga Kulkarni, Alice Harpur, Madhurima Nundy, Xin Wang, and Harish Nair. ‘The Temporal Association of Introducing and Lifting Non-Pharmaceutical Interventions with the Time-Varying Reproduction Number (R) of SARS-CoV-2: A Modelling Study across 131 Countries’. The Lancet Infectious Diseases 21, no. 2 (1 February 2021): 193–202. https://doi.org/10.1016/S1473-3099(20)30785-4.
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- Oct 2020
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seeing-theory.brown.edu seeing-theory.brown.edu
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Kunin, D. (n.d.). Seeing Theory. Retrieved October 27, 2020, from http://seeingtheory.io
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covid-19.iza.org covid-19.iza.org
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Dang, H. H., & Trinh, T. (2020). The Beneficial Impacts of COVID-19 Lockdowns on Air Pollution: Evidence from Vietnam. IZA Discussion Paper, 13651.
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- Sep 2020
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www-sciencedirect-com.ezproxy.neu.edu www-sciencedirect-com.ezproxy.neu.edu
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exposure limits are determined by the following equations (NIOSH, 2016):(4)ÂRAL[°C-WBGT]=59.9-14.1log10M<math><mrow is="true"><mi mathvariant="normal" is="true">R</mi><mi mathvariant="normal" is="true">A</mi><mi mathvariant="normal" is="true">L</mi><mo stretchy="false" is="true">[</mo><mi is="true">Â</mi><mi is="true">°</mi><mi mathvariant="normal" is="true">C</mi><mo is="true">-</mo><mi mathvariant="normal" is="true">W</mi><mi mathvariant="normal" is="true">B</mi><mi mathvariant="normal" is="true">G</mi><mi mathvariant="normal" is="true">T</mi><mo stretchy="false" is="true">]</mo><mo is="true">=</mo><mn is="true">59.9</mn><mo is="true">-</mo><mn is="true">14.1</mn><mi mathvariant="normal" is="true">l</mi><mi mathvariant="normal" is="true">o</mi><mi mathvariant="normal" is="true">g</mi><mn is="true">10</mn><mi mathvariant="normal" is="true">M</mi></mrow></math>(5)Â
regressional analysis of exposure limits
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www.sciencedirect.com www.sciencedirect.com
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Gignac, G. E., & Zajenkowski, M. (2020). The Dunning-Kruger effect is (mostly) a statistical artefact: Valid approaches to testing the hypothesis with individual differences data. Intelligence, 80, 101449. https://doi.org/10.1016/j.intell.2020.101449
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www.ajpmonline.org www.ajpmonline.org
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Liu, Y., Finch, B. K., Brenneke, S. G., Thomas, K., & Le, P. D. (2020). Perceived Discrimination and Mental Distress Amid the COVID-19 Pandemic: Evidence From the Understanding America Study. American Journal of Preventive Medicine, 0(0). https://doi.org/10.1016/j.amepre.2020.06.007
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www.medrxiv.org www.medrxiv.org
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Ip, A., Ahn, J., Zhou, Y., Goy, A. H., Hansen, E., Pecora, A. L., Sinclaire, B. A., Bednarz, U., Marafelias, M., Mathura, S., Sawczuk, I. S., Underwood, J. P., Walker, D. M., Prasad, R., Sweeney, R. L., Ponce, M. G., LaCapra, S., Cunningham, F. J., Calise, A. G., … Goldberg, S. L. (2020). Hydroxychloroquine in the treatment of outpatients with mildly symptomatic COVID-19: A multi-center observational study. MedRxiv, 2020.08.20.20178772. https://doi.org/10.1101/2020.08.20.20178772
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- Aug 2020
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papers.ssrn.com papers.ssrn.com
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Grossman, G., Kim, S., Rexer, J., & Thirumurthy, H. (2020). Political Partisanship Influences Behavioral Responses to Governors’ Recommendations for COVID-19 Prevention in the United States (SSRN Scholarly Paper ID 3578695). Social Science Research Network. https://doi.org/10.2139/ssrn.3578695
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osf.io osf.io
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Alipio, M. (2020). Do socio-economic indicators associate with COVID-2019 cases? Findings from a Philippine study [Preprint]. SocArXiv. https://doi.org/10.31235/osf.io/e2hfa
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osf.io osf.io
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Mak, H. W., & Fancourt, D. (2020). Predictors of engaging in voluntary work during the Covid-19 pandemic: Analyses of data from 31,890 adults in the UK. https://doi.org/10.31235/osf.io/er8xd
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Sun, K., Wang, W., Gao, L., Wang, Y., Luo, K., Ren, L., Zhan, Z., Chen, X., Zhao, S., Huang, Y., Sun, Q., Liu, Z., Litvinova, M., Vespignani, A., Ajelli, M., Viboud, C., & Yu, H. (2020). Transmission heterogeneities, kinetics, and controllability of SARS-CoV-2. MedRxiv, 2020.08.09.20171132. https://doi.org/10.1101/2020.08.09.20171132
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www.jahonline.org www.jahonline.org
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Gaiha, S. M., Cheng, J., & Halpern-Felsher, B. (2020). Association Between Youth Smoking, Electronic Cigarette Use, and Coronavirus Disease 2019. Journal of Adolescent Health, 0(0). https://doi.org/10.1016/j.jadohealth.2020.07.002
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covid-19.iza.org covid-19.iza.org
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The Effect of Business Cycle Expectations on the German Apprenticeship Market: Estimating the Impact of COVID-19. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved August 5, 2020, from https://covid-19.iza.org/publications/dp13368/
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covid-19.iza.org covid-19.iza.org
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Pandemic Meets Pollution: Poor Air Quality Increases Deaths by COVID-19. COVID-19 and the Labor Market. (n.d.). IZA – Institute of Labor Economics. Retrieved July 31, 2020, from https://covid-19.iza.org/publications/dp13418/
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psyarxiv.com psyarxiv.com
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Webster, G. D., Howell, J. L., Losee, J. E., Mahar, E., & Wongsomboon, V. (2020). Culture, COVID-19, and Collectivism: A Paradox of American Exceptionalism? [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/hqcs6
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jasp-stats.org jasp-stats.org
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Introducing JASP 0.11: The Machine Learning Module. (2019, September 24). JASP - Free and User-Friendly Statistical Software. https://jasp-stats.org/2019/09/24/introducing-jasp-0-11-the-machine-learning-module/
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- Jul 2020
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Knittel, C. R., & Ozaltun, B. (2020). What Does and Does Not Correlate with COVID-19 Death Rates (Working Paper No. 27391; Working Paper Series). National Bureau of Economic Research. https://doi.org/10.3386/w27391
Tags
- USA
- is:article
- temperature
- Iowa
- socio-economic
- energy
- African American
- obesity
- Indiana
- health economics
- lang:en
- California
- public transport
- COVID-19
- health care
- linear regression
- pollution
- economy
- Michigan
- Colorado
- employment
- binomial
- climate
- correlate
- county
- commute
- Louisiana
- environment
- transport
- elderly
- poverty
- ICU
- telecommuting
Annotators
URL
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psyarxiv.com psyarxiv.com
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Ahn, M. H., Shin, Y. W., Kim, J. H., Kim, H. J., Lee, K.-U., & Chung, S. (2020). High Work-related Stress and Anxiety Response to COVID-19 among Healthcare Workers in South Korea: SAVE study [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/9nxth
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- Jun 2020
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twitter.com twitter.com
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Twitter. (n.d.). Twitter. Retrieved June 22, 2020, from https://twitter.com/JASPStats/status/1274764017752592384
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psyarxiv.com psyarxiv.com
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Antonakis, J., Bastardoz, N., & Jacquart, P. (2020). In praise of the impact factor [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/h4p9e
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psyarxiv.com psyarxiv.com
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Abdelrahman, M. K. (2020, April 14). Personality Traits, Risk Perception and Social Distancing During COVID-19. https://doi.org/10.31234/osf.io/6g7kh
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psyarxiv.com psyarxiv.com
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Gibson Miller, J., Hartman, T. K., Levita, L., Martinez, A. P., Mason, L., McBride, O., … Bentall, R. (2020, April 20). Capability, opportunity and motivation to enact hygienic practices in the early stages of the COVID-19 outbreak in the UK. https://doi.org/10.31234/osf.io/typqv
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roadtolarissa.com roadtolarissa.com
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You Regress It: Have Masks Prevented 66,000 Infections in New York City? (n.d.). Retrieved June 17, 2020, from https://roadtolarissa.com/regression-discontinuity
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journals.sagepub.com journals.sagepub.com
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Maltby, J., Hunt, S. A., Ohinata, A., Palmer, E., & Conroy, S. (2020). Frailty and Social Isolation: Comparing the Relationship between Frailty and Unidimensional and Multifactorial Models of Social Isolation: Journal of Aging and Health. https://doi.org/10.1177/0898264320923245
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www.tandfonline.com www.tandfonline.com
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Efron, B. (2020). Prediction, Estimation, and Attribution. Journal of the American Statistical Association, 115(530), 636–655. https://doi.org/10.1080/01621459.2020.1762613
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- May 2020
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www.preprints.org www.preprints.org
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Samuel, J.; Ali, G.G.M.N.; Rahman, M.M.; Esawi, E.; Samuel, Y. COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification. Preprints 2020, 2020050015 (doi: 10.20944/preprints202005.0015.v1)
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psyarxiv.com psyarxiv.com
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Martin, R., & Ruby, M. (2020). What does food retail research tell us about the implications of COVID-19 for grocery purchasing habits? [Preprint]. PsyArXiv. https://doi.org/10.31234/osf.io/z2kup
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psyarxiv.com psyarxiv.com
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Tomohiro, I. (2020, May 8). Consensus among group members’ shared leadership ratings polarizes group performance. https://doi.org/10.31234/osf.io/psjeu
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onlinelibrary.wiley.com onlinelibrary.wiley.com
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Roland, L. T., Gurrola, J. G., Loftus, P. A., Cheung, S. W., & Chang, J. L. (2020). Smell and taste symptom‐based predictive model for COVID‐19 diagnosis. International Forum of Allergy & Rhinology, alr.22602. https://doi.org/10.1002/alr.22602
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psyarxiv.com psyarxiv.com
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Zinn, S., & Gnambs, T. (2020, April 18). Analyzing nonresponse in longitudinal surveys using Bayesian additive regression trees: A nonparametric event history analysis. https://doi.org/10.31234/osf.io/82c3w
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psyarxiv.com psyarxiv.com
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Rotella, A. M., & Mishra, S. (2020, April 24). Personal relative deprivation negatively predicts engagement in group decision-making. https://doi.org/10.31234/osf.io/6d35w
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- Jan 2020
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www.sthda.com www.sthda.com
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Logistic regression assumptions
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www.sthda.com www.sthda.com
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Make sure that the predictor variables are normally distributed. If not, you can use log, root, Box-Cox transformation.
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online.stat.psu.edu online.stat.psu.edu
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An outlier is a data point whose response y does not follow the general trend of the rest of the data. A data point has high leverage if it has "extreme" predictor x values. With a single predictor, an extreme x value is simply one that is particularly high or low. With multiple predictors, extreme x values may be particularly high or low for one or more predictors, or may be "unusual" combinations of predictor values (e.g., with two predictors that are positively correlated, an unusual combination of predictor values might be a high value of one predictor paired with a low value of the other predictor).
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jbhender.github.io jbhender.github.io
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As shown in the Residuals vs Fitted plot, there is a megaphone shape, which indicates that non-constant variance is likely to be an issue.
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- Jul 2019
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lifelines.readthedocs.io lifelines.readthedocs.io
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Non-proportional hazards is a case of model misspecification.
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The idea behind the model is that the log-hazard of an individual is a linear function of their static covariates and a population-level baseline hazard that changes over time.
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- Jan 2019
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assets.publishing.service.gov.uk assets.publishing.service.gov.uk
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There are some environmental elements of the Withdrawal Agreement which our current proposals do not cover, namely those concerning the independent body’s scope to enforce implementation of the “non-regression” clause. We will consider these provisions of the Withdrawal Agreement ahead of publishing the final Bill
hmmmmm....
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The text sets out that, if the protocol is required, the UK and EU will not reduce their respective levels of environmental protection below those in place at the end of the implementation period
note the 'if' attached to N-R
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- Sep 2018
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192.168.199.102:5000 192.168.199.102:5000
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生成模型 vs. 判别模型
总体来看，如果样本足够多，判别模型的正确率高于生成模型的正确率。
生成模型和判别模型最大的区别在于，生成模型预先假设了很多东西，比如预先假设数据来自高斯，伯努利，符合朴素贝叶斯等等，相当于预先假设了 Hypothesis 函数集，只有在此基础上才有可能求出这个概率分布的参数。
生成模型，进行了大量脑补。脑补听起来并不是一件好事，但是当你的数据量太小的时候，则必须要求你的模型具备一定的脑补能力。
判别模型非常依赖样本，他就是很传统，死板，而生成模型比较有想象力，可以“想象”出不存在于当前样本集中的样本，所以他不那么依赖样本。
关于 想象出不能存在于当前样本集的样本 ，见本课程 40:00 老师举例。
生成模型在如下情形比判别模型好：
- 数据量较小时。
- 数据是noisy，标签存在noisy。
- 先验概率和类别相关的概率可以统计自不同的来源。
释疑第三条优点：老师举例，在语音辨识问题中，语音辨识部分虽然是 DNN --- 一个判别模型，但其整体确实一个生成模型，DNN 只是其中一块而已。为什么会这样呢？因为你还是要去算一个先验概率 --- 某一句话被说出来的概率，而获得这个概率并不需要样本一定是声音，只要去网络上爬很多文字对话，就可以估算出这个概率。只有 类别相关的概率 才需要声音和文字pair，才需要判别模型 --- DNN 出马。
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Annotators
URL
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stats.stackexchange.com stats.stackexchange.com
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Relationship between ridge regression and PCA regression
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- Aug 2018
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assets.publishing.service.gov.uk assets.publishing.service.gov.uk
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committing to high regulatory environmental standards through a non-regression requirement;
non regression and high standards commitment
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- Oct 2017
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www.restore.ac.uk www.restore.ac.uk
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An Introduction to Odds, Odds Ratios and Exponents
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- Jun 2016
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www.ats.ucla.edu www.ats.ucla.edu
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The parameter estimate for the first contrast compares the mean of the dependent variable, write, for levels 1 and 2 yielding 11.5417 and is statistically significant (p<.000). The t-value associated with this test is 3.5122. The results of the second contrast, comparing the mean of write for levels 1 and 3. The expected difference in variable write between group 1 and 3 is 1.7417 and is not statistically significant (t = 0.6374, p = .5246), while the third contrast is statistically significant. Notice that the intercept corresponds to the cell mean for race = Hispanic group.
Interpreting the reference group in dummy coding.
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- Nov 2015
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Visual regression testing tool that may be worth investigating.
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- Aug 2015
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Quality (DRG) 1.2675 0.61
It seems there is no statistically significant differences in the Quality measured between the two aggregation levels.
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total offive variables in the frontierestimate: three input types in dollars, output in number of patient-days, andquality in estimated HSMR values.
So this is the model translated as:
number of patient-days(by hospital*) = HSMR value(ratio) + capital prices($) + labor($) + materials($)
- all the co-variants are also accounted by Hospital
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bootstrap-adjustedTobit regression as specified by Simar and Wilson (2007
*interesting reference! on "bootstrap-adjusted Tobit regression!
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- Jan 2015
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www.ats.ucla.edu www.ats.ucla.edu
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Logistic regression, also called a logit model, is used to model dichotomous outcome variables. In the logit model the log odds of the outcome is modeled as a linear combination of the predictor variables.
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Annotators
URL
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