SCS Reads 2019-2020
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- | + | * Note that a topic need not occupy the entire academic year and we could plan to consider more than one topic. | |
- | * | + | * Add other suggestions here or send them to georges@yorku.ca. |
- | + | * To get an account to edit this wiki send a message to georges@yorku.ca. | |
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== New candidates topics for 2019-2020 == | == New candidates topics for 2019-2020 == | ||
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== Candidates from 2018-2019== | == Candidates from 2018-2019== | ||
- | + | * '''Reproducibility of research: a crisis in Statistics??''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. | |
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- | * '''Reproducibility of research''': How should statistical practice be informed by current controversies about the ''replication crisis'' and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference. | + | |
** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226] | ** Roger Peng, Reproducible Research in Computational Science, ''Science'', Vol. 334, Issue 6060, pp. 1226-1227 [http://science.sciencemag.org/content/334/6060/1226] | ||
**RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter | **RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter | ||
- | + | ** We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-value and the concept of statistical significance. | |
* '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective] | * '''Big Data problems''': Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective] | ||
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* Some statistical methods topics, not yet clearly articulated: | * Some statistical methods topics, not yet clearly articulated: | ||
** Clustering methods [RC: Could this fall under the Big Data label?] | ** Clustering methods [RC: Could this fall under the Big Data label?] | ||
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** Mediation | ** Mediation | ||
** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research] | ** Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research] | ||
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* '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss] | * '''Consulting issues''': Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss] | ||
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* '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. | * '''Survey Sampling''': Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada. | ||
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* '''Evidence-based medicine''': Ideas and implications | * '''Evidence-based medicine''': Ideas and implications | ||
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* '''Machine Learning, AI, Deep Learning''': An Overview | * '''Machine Learning, AI, Deep Learning''': An Overview | ||
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* '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?] | * '''Disseminating technical information to non-technical audiences''': in consulting and in teaching. [Could this be put together with 'consulting issues'?] | ||
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* '''Missing Data''' | * '''Missing Data''' | ||
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* '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful] | * '''Statistical Paradoxes and Fallacies''' [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful] | ||
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** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment. | ** Initial attempt at a creating a list: [[Paradoxes, Fallacies and Other Surprises]]. Please add, modify or comment. | ||
- | + | == Links to past episodes of SCS Reads == | |
- | == | + | * [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]] |
- | + | * [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]] | |
- | + | * [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]] | |
- | + | * [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]] | |
- | * | + | * [[SCS Reads 2014-2015]] |
- | + | * [[SCS Reads Nominations|Past and current nominations for SCS Reads]] | |
- | + | * [[SCS Reads -- past seminars]] | |
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Revision as of 12:36, 1 September 2019
- Note that a topic need not occupy the entire academic year and we could plan to consider more than one topic.
- Add other suggestions here or send them to georges@yorku.ca.
- To get an account to edit this wiki send a message to georges@yorku.ca.
New candidates topics for 2019-2020
- Add other suggestions here or send them to georges@yorku.ca.
Candidates from 2018-2019
- Reproducibility of research: a crisis in Statistics??: How should statistical practice be informed by current controversies about the replication crisis and countervailing moves toward open science and reproducibility? This was a big topic at the recent JSM 2018 conference.
- Roger Peng, Reproducible Research in Computational Science, Science, Vol. 334, Issue 6060, pp. 1226-1227 [1]
- RC: This is timely in a couple ways. Chris Green is teaching a course on the topic, so he or one of the students might be able to come to a meeting to discuss their views. We also have a QM Forum speaker on topic in the Winter
- We can use articles in the special issue of the American Statistician devoted to current problems in statistical inference, particularly the use and interpretation of p-value and the concept of statistical significance.
- Big Data problems: Another hot topic, but perhaps too broad. [MF: I don't know enough to specify it more clearly as a useful seminar topic.] [This would be interesting, but I would hope we could find materials that address the issue from a social science perspective]
- Some statistical methods topics, not yet clearly articulated:
- Clustering methods [RC: Could this fall under the Big Data label?]
- Robustness
- Mediation
- Meta analysis in medicine: how can you tell whether a lit review is complete with logistic regression?! [RC: I think Meta-Analysis could be a good topic, including M-L/J-A discussing their research]
- Consulting issues: Practical aspects of statistical consulting [MF: Perhaps this would be a better topic for the SCS staff meetings ??] [RC: That would make a good topic for a business/staff meeting, if there were no consulting cases to discuss]
- Survey Sampling: Elucidating the mystery of bootstrap weights and how to use them when analyzing survey data, e.g. from Statistics Canada.
- Evidence-based medicine: Ideas and implications
- Machine Learning, AI, Deep Learning: An Overview
- Disseminating technical information to non-technical audiences: in consulting and in teaching. [Could this be put together with 'consulting issues'?]
- Missing Data
- Statistical Paradoxes and Fallacies [RC: There are some good "summary" articles related to statistical paradoxes and Georges' examples are always helpful]
- Initial attempt at a creating a list: Paradoxes, Fallacies and Other Surprises. Please add, modify or comment.