Talk:Bootstrapping (statistics)

message: (see later for diuscussion of contents)
Yes this page should be merged.Gpeilon 15:01, 10 January 2007 (UTC)
thread_title:  Merging with Bagging 

message: == mediation ==
 
I would like to raise an isssue with the mention of "mediation" in the intro material. Should there be a minor subsection for this, explaining what "mediation" means, somw brief details of how boostrapping applies, and possibly with its own example being shown to show the contrast with an ordinary single sample case. Melcombe 13:21, 16 July 2007 (UTC)
replies: text: now added new section, but possibly there is a need for a much more technical description of bootstrapping overall in order to provide enough context/information. This need for a more formal specification would also benefit other parts perhaps. Melcombe (talk) 11:31, 12 February 2008 (UTC)

text: The lines are fine, they are somewhat complicated, but they mean what they intend to and what they need to. You could have looked up point estimation and point estimators before delving through this topic, but yes, math on wikipedia seems to be getting crowded with technical jargon. --128.2.48.150 (talk) 13:55, 23 October 2009 (UTC)
replies: text:  The lines are better than fine. They're a great definition for someone who understands the topic already. Utterly useless for anyone else.  —Preceding unsigned comment added by 194.171.7.39 (talk) 13:23, 6 April 2010 (UTC)
replies: text:  Um, bootstrapping is a fairly advanced topic; that is, it is not for complete novices.  You need to already know what an estimator is before coming here, and likely a distribution.  Ideally maybe those topics should be linked in the sentences at the top of this article, but we can't start every article from a point of absolute zero knowledge.  I am going to re-write the informal example to make it clearer.  Maybe someone can look at that and then comment here on how we could make the sentences at the top of the article clearer? Doctorambient (talk) 01:04, 30 September 2011 (UTC)
replies: text:  It's a questionable approach to think that some topics are only for some specific (educated) people.  Base on this notion the lemma of quantum mechanics would be next to unreadable for laymen (but it is not).  WP strives to explain even the most complex things in terms which a "novice" would understand.  It is okay not to dive too deeply and leave questions open in the details, but the mere definition should be clear even to a barely educated person, and also a kind of vague idea what is being done when using it. Alfe (talk) 08:50, 29 June 2017 (UTC)

text: I heart Python and all, but R is the de facto standard computing language for statistical research.  You could make a case for SAS code instead (bleah!) but what is the case for Python? I'll fix the R code.  Doctorambient (talk) 01:06, 30 September 2011 (UTC)

text: Wait!  What R code?  I guess it was deleted.  Doctorambient (talk) 01:08, 30 September 2011 (UTC)

text: Bootstrapping (machine learning) already contains a link to Bootstrap aggregating and that article would be a better candidate for merging to (or from). The topic here is rather different as indicated by the "(statistics)" in the title. Melcombe (talk) 11:15, 24 September 2009 (UTC)

text: The techniques are very different, one is for classifier improvement, the other for inference testing. There is a slim link between the two that the machine learning 'bootstrapping' was in principal derived from statistical bootstrapping technique, but the argument is very thin. I believe it would lead to a lot of confusion. So my vote is for keeping the topics separate while providing the link to either topics in the See also section, i.e. maintain the status quo. --128.2.48.150 (talk) 13:48, 23 October 2009 (UTC)
thread_title:  Discussion of contents 

message: I would like to raise an isssue with the mention of "mediation" in the intro material. Should there be a minor subsection for this, explaining what "mediation" means, somw brief details of how boostrapping applies, and possibly with its own example being shown to show the contrast with an ordinary single sample case. Melcombe 13:21, 16 July 2007 (UTC)
thread_title:  mediation 

message: This page needs to mention pivotal statistics, which are critial to bootstrapping.  —Preceding unsigned comment added by 129.2.18.171 (talk) 22:18, 11 February 2008 (UTC)
replies: text: now added new section, but possibly there is a need for a much more technical description of bootstrapping overall in order to provide enough context/information. This need for a more formal specification would also benefit other parts perhaps. Melcombe (talk) 11:31, 12 February 2008 (UTC)
thread_title:  pivots 

message: The definition of "wild bootstrap" is incomplete and does not describe the most commonly used method, see http://fmwww.bc.edu/RePEc/es2000/1413.pdf, page 7.
—Preceding unsigned comment added by Arnehe (talk • contribs) 10:07, 4 May 2010 (UTC)
thread_title:  Wild bootstrap 

message: I was looking for a definition of the bootstrap method, and couldn't understand the definition given here, in the 2nd sentence: 
"Bootstrapping is the practice of estimating properties of an estimator (such as its variance) by measuring those properties when sampling from an approximating distribution. One standard choice for an approximating distribution is the empirical distribution of the observed data."
Since I do not know whar bootsrapping is, I cannot change it myself, so I wrote it here instead.  Setreset (talk) 15:27, 8 April 2008 (UTC)
replies: text: The lines are fine, they are somewhat complicated, but they mean what they intend to and what they need to. You could have looked up point estimation and point estimators before delving through this topic, but yes, math on wikipedia seems to be getting crowded with technical jargon. --128.2.48.150 (talk) 13:55, 23 October 2009 (UTC)
replies: text:  The lines are better than fine. They're a great definition for someone who understands the topic already. Utterly useless for anyone else.  —Preceding unsigned comment added by 194.171.7.39 (talk) 13:23, 6 April 2010 (UTC)
replies: text:  Um, bootstrapping is a fairly advanced topic; that is, it is not for complete novices.  You need to already know what an estimator is before coming here, and likely a distribution.  Ideally maybe those topics should be linked in the sentences at the top of this article, but we can't start every article from a point of absolute zero knowledge.  I am going to re-write the informal example to make it clearer.  Maybe someone can look at that and then comment here on how we could make the sentences at the top of the article clearer? Doctorambient (talk) 01:04, 30 September 2011 (UTC)
replies: text:  It's a questionable approach to think that some topics are only for some specific (educated) people.  Base on this notion the lemma of quantum mechanics would be next to unreadable for laymen (but it is not).  WP strives to explain even the most complex things in terms which a "novice" would understand.  It is okay not to dive too deeply and leave questions open in the details, but the mere definition should be clear even to a barely educated person, and also a kind of vague idea what is being done when using it. Alfe (talk) 08:50, 29 June 2017 (UTC)
thread_title:  unclear 

message: The formatting in the R code is ugly. Beside fixing its formatting, another possible solution is to replace that code with Python code.  —Preceding unsigned comment added by 79.43.56.205 (talk) 15:56, 19 July 2009 (UTC)
replies: text: I heart Python and all, but R is the de facto standard computing language for statistical research.  You could make a case for SAS code instead (bleah!) but what is the case for Python? I'll fix the R code.  Doctorambient (talk) 01:06, 30 September 2011 (UTC)

text: Wait!  What R code?  I guess it was deleted.  Doctorambient (talk) 01:08, 30 September 2011 (UTC)
thread_title:  R code 

message: Bootstrapping (machine learning) seems to cover basically the same topic. I think the few content of that article should be merged here. Calimo (talk) 11:33, 8 September 2009 (UTC)
replies: text: Bootstrapping (machine learning) already contains a link to Bootstrap aggregating and that article would be a better candidate for merging to (or from). The topic here is rather different as indicated by the "(statistics)" in the title. Melcombe (talk) 11:15, 24 September 2009 (UTC)

text: The techniques are very different, one is for classifier improvement, the other for inference testing. There is a slim link between the two that the machine learning 'bootstrapping' was in principal derived from statistical bootstrapping technique, but the argument is very thin. I believe it would lead to a lot of confusion. So my vote is for keeping the topics separate while providing the link to either topics in the See also section, i.e. maintain the status quo. --128.2.48.150 (talk) 13:48, 23 October 2009 (UTC)
thread_title:  Merger proposal 

message: Congratulations on this accesible but reasonably thorough article. Especially the section with the informal description and practical example should be part of any mathematical page, and possibly every wikipedia page.  — Preceding unsigned comment added by 145.18.30.3 (talk) 15:44, 20 March 2012 (UTC)
thread_title:  Congrats 

message: The example section contains figures for Newcomb's speed of light measurements.  Going to the referenced URL, one finds the following dataset:
 Simon Newcomb's measurements of the speed of light, from Stigler
 (1977).  The data are recorded as deviations from $24,\!800$
 nanoseconds.  Table 3.1 of Bayesian Data Analysis.
 
 28 26 33 24 34 -44 27 16 40 -2
 29 22 24 21 25 30 23 29 31 19
 24 20 36 32 36 28 25 21 28 29
 37 25 28 26 30 32 36 26 30 22
 36 23 27 27 28 27 31 27 26 33
 26 32 32 24 39 28 24 25 32 25
 29 27 28 29 16 23 
The two outliers are clearly -44 and -2  but the rest of the data ranges from 16 to 40.  The bar charts accompanying the example don't reflect the above data at all -- the x-axis labels don't match this range. So WTF!?  I slapped a disputed sticker onto the mess. linas (talk) 04:20, 11 April 2012 (UTC)
replies: text: Since there is no source for the example in the article it really sould be deleted as WP:OR. Melcombe (talk) 12:49, 11 June 2012 (UTC)

text: (2\theta -\theta^{*}_{(1-\alpha/2)}, 2\theta -\theta^{*}_{(\alpha/2)})
--Quantum7 08:45, 24 September 2014 (UTC)

text: Not if you are sampling with replacement. This means that each time you sample 1 of your 100 values, you put it back into the 'bag', so there's a chance it could be selected again. In fact, if you have 100 unique values in your original sample, from which your perform sampling with replacement, the odds of not resampling at least one value is incredibly small; thus so is computing the same statistic (e.g. mean) on independent trials. Niubrad (talk) 05:39, 4 August 2015 (UTC)

text: The equation is correct, ie.  y^*_i = \hat{y_i} + \hat{\epsilon}_j. I'll change the yi to  \hat{y_i} in the text.Lucleon (talk) 00:02, 7 November 2018 (UTC)
thread_title:  Newcomb's speed of light 

message: "very simple methods"
I have to disagree with this statement. See discussion here: http://stats.stackexchange.com/questions/26088/explaining-to-laypeople-why-bootstrapping-works
Compared to deriving asymptotic sampling distribution it is not difficult to apply bootstrapping (http://kurt.schmidheiny.name/teaching/bootstrap2up.pdf). However, to a non-statistician it could be fairly difficult.
The main reason I disagree with the statement is that Wikipedia departs from an expert-driven approach to encyclopaedias and the citation refers to a publication that targets experts.
81.226.179.21 (talk) 14:59, 23 September 2012 (UTC)
thread_title:  Not so Simple 

message: There's one history section at the top and one history section at the bottom (before "see also"). The bottom contains similar information to the top history section. Weaktofu (talk) 02:32, 7 January 2013 (UTC)
thread_title:  Two History Sections? 

message: Is the (a;b) notation used in the section Methods for bootstrap confidence intervals standard? I've only seen semicolons used in statistics for parameter values (e.g. Pr[x;\theta]), so this confused me. Wouldn't a comma be more standard? E.g.
:(2\theta -\theta^{*}_{(1-\alpha/2)}, 2\theta -\theta^{*}_{(\alpha/2)})
--Quantum7 08:45, 24 September 2014 (UTC)
thread_title:  Semicolons in confidence intervals 

message: Sentence: "...involves taking the original data set of N heights, and, using a computer, sampling from it to form a new sample (called a 'resample' or bootstrap sample) that is also of size N." is unclear.
Someone else mentioned it too. If population is 10,000 and you take sample of size N=100, then take samples also of size N (=100), you keep ending up with the original 100 from the original sample...
71.139.163.158 (talk) 05:41, 12 March 2015 (UTC)
replies: text: Not if you are sampling with replacement. This means that each time you sample 1 of your 100 values, you put it back into the 'bag', so there's a chance it could be selected again. In fact, if you have 100 unique values in your original sample, from which your perform sampling with replacement, the odds of not resampling at least one value is incredibly small; thus so is computing the same statistic (e.g. mean) on independent trials. Niubrad (talk) 05:39, 4 August 2015 (UTC)
thread_title:  also of size N. 

message: Dr. MacKinnon has reviewed this Wikipedia page, and provided us with the following comments to improve its quality:
We hope Wikipedians on this talk page can take advantage of these comments and improve the quality of the article accordingly.
We believe Dr. MacKinnon has expertise on the topic of this article, since he has published relevant scholarly research:
*Reference : James G. MacKinnon, 2014. "Wild cluster bootstrap confidence intervals," Working Papers 1329, Queen's University, Department of Economics.
ExpertIdeasBot (talk) 19:00, 30 August 2016 (UTC)
thread_title:  Dr. MacKinnon's comment on this article 

message: In section (2) of Resampling residuals, we have: 
... add a randomly resampled residual, \hat{\epsilon}_j, to the response variable yi. In other words, create synthetic response variables y^*_i = \hat{y_i} + \hat{\epsilon}_j where ...
It looks to me like yi switches to \hat{y_i}. I'm not sure which is right. I can kind of guess, but I won't because I don't know it well enough. (Or I just can't read and it's right as it is...) 203.14.82.101 (talk) 23:53, 6 November 2018 (UTC)
replies: text: The equation is correct, ie.  y^*_i = \hat{y_i} + \hat{\epsilon}_j. I'll change the yi to  \hat{y_i} in the text.Lucleon (talk) 00:02, 7 November 2018 (UTC)
thread_title:  Small notation error, I think, in Resampling residuals 

message: I'm not going to do it now, but think it's worth flagging. Tal Galili (talk) 13:27, 18 November 2020 (UTC)
thread_title:  The external links section should probably be cleaned up 

message: could you please add this https://towardsdatascience.com/the-bayesian-bootstrap-6ca4a1d45148 2A02:3100:55C4:4900:31F3:8EA7:5768:BBDD (talk) 13:21, 2 April 2023 (UTC)
thread_title:  Bayesian Bootstrap 

message: Not discussed https://www.jstor.org/stable/2337152 . This is linked to empirical likelihood. Bootstrap likelihood uses a nested bootstrap and kernel density estimation. Biggerj1 (talk) 21:50, 18 July 2023 (UTC)
thread_title:  Bootstrap likelihood