SCS Reads 2019-2020

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== Links to past episodes of SCS Reads ==
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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.
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* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]
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* Add other suggestions here or send them to georges@yorku.ca.
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* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]
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* To get an account to edit this wiki send a message to georges@yorku.ca.
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* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]
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* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]
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* [[SCS Reads 2014-2015]]
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* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]
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* [[SCS Reads -- past seminars]]
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== New candidates topics for 2019-2020 ==
== New candidates topics for 2019-2020 ==
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* '''The Crisis in Statistics??''' Use 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, as a springboard to select articles from the special issue or elsewhere to discuss whether there is a crisis in statistics and what should we do.
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* '''Add other suggestions here or send them to georges@yorku.ca.'''
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* Add other suggestions here or send them to georges@yorku.ca.
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== Candidates from 2018-2019==
== Candidates from 2018-2019==
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* '''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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== Initial discussion of topics ==
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A view was expressed that we should consider this seminar series more broadly, with a view to:
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* wider, and more active participation by the seminar members
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* topics or book chapters that would:
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** enlist a volunteer as discussion leader, or organize the topic
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** perhaps involve some concrete, practical examples used to illustrate the topic
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Without prejudice to the choice of The ''Book of Why'' on causal inference, some suggested topics
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mentioned are listed below, though the general view was that, if we took this route, only 3-4
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should be considered for this year.  I'm just listing these, but they deserve to be fleshed out
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more for us to consider how & whether they would work.
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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. [MF: I should add some references here.]
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** 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
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** 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.
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== Links to past episodes of SCS Reads ==
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== September 21 ==
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* [[SCS Reads 2018-2019|SCS Reads 2018-2019 Causality mainly]]
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* [[SCS Reads 2017-2018|SCS Reads 2017-2018 Counterfactuals and Causal Inference]]
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Talk: Michael Friendly, ''100+ Years of Titanic Graphs''
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* [[SCS Reads 2016-2017|SCS Reads 2016-2017 Rethinking Statistics]]
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* [[SCS Reads 2015-2016|SCS Reads 2015-2016 Bayesian Statistics in the Social Sciences]]
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* Slides: [[File:SCS-TitanicGraphs-2x2.pdf]]
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* [[SCS Reads 2014-2015]]
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* [[SCS Reads Nominations|Past and current nominations for SCS Reads]]
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== October 5 ==
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* [[SCS Reads -- past seminars]]
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* Introduction and Chapter 1 of the Book of Why
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* [http://bayes.cs.ucla.edu/WHY/ Website for the book]
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* [http://bayes.cs.ucla.edu/WHY/why-ch1.pdf PDF of first chapter]
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== October 19 ==
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* Chapters 2 and 3 of the Book of Why
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== November 2 ==
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* Chapters 4 and 5 of the Book of Why
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....
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== January 4 ==
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* We plan to continue a discussion of Chapter 7 of the Book of Why focusing on details of the do-calculus.
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* An interesting reference might be this [https://www.ssc.wisc.edu/soc/faculty/pages/docs/elwert/Elwert%202013.pdf 2013 article by Felix Elwert on Graphical Causal Models].
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== February 1 ==
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* We continue with Chapter 8 of ''The Book of Why'', '''Counterfactuals: Mining Worlds That Could Have Been'''
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* MF offered some examples of papers describing R packages for learning/doing causal modeling
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** [http://dagity.net/primer Causal Inference in Statistics: A Companion for R Users]. This is designed to accompany the book [http://bayes.cs.ucla.edu/PRIMER/ Causal Inference in Statistics: A Primer] by Pearl, Glymour and Jewell.
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** [https://cran.r-project.org/web/packages/CausalImpact/vignettes/CausalImpact.html Vignette for the CausalImpact package]. This package implements a Bayesian approach to causal impact estimation in time series data.  It is designed for a specific situation, when you have time series pre- and post- some intervention. I mention it here only because it produces nice graphs of
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** [https://www.jstatsoft.org/article/view/v082i02 A Recipe for Interference: Start with Causal Inference. Add Interference. Mix Well with R.] by Bradley Saul & Michael Hudgens
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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]

Links to past episodes of SCS Reads

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