A blog by Rob J Hyndman 

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Posts Tagged ‘teaching’:


Interpreting noise

Published on 6 April 2014

When watch­ing the TV news, or read­ing news­pa­per com­men­tary, I am fre­quently amazed at the attempts peo­ple make to inter­pret ran­dom noise. For exam­ple, the lat­est tiny fluc­tu­a­tion in the share price of a major com­pany is attrib­uted to the CEO being ill. When the exchange rate goes up, the TV finance com­men­ta­tor con­fi­dently announces that it is a reac­tion to Chi­nese build­ing con­tracts. No one ever says “The unem­ploy­ment rate has dropped by 0.1% for no appar­ent rea­son.” What is going on here is that the com­men­ta­tors are assum­ing we live in a noise-​​​​free world. They imag­ine that every­thing is explic­a­ble, you just have to find the expla­na­tion. How­ever, the world is noisy — real data are sub­ject to ran­dom fluc­tu­a­tions, and are often also mea­sured inac­cu­rately. So to inter­pret every lit­tle fluc­tu­a­tion is silly and misleading.

 
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Fast computation of cross-​​validation in linear models

Published on 17 March 2014

The leave-​​​​one-​​​​out cross-​​​​validation sta­tis­tic is given by     where , are the obser­va­tions, and is the pre­dicted value obtained when the model is esti­mated with the th case deleted. This is also some­times known as the PRESS (Pre­dic­tion Resid­ual Sum of Squares) sta­tis­tic. It turns out that for lin­ear mod­els, we do not actu­ally have to esti­mate the model times, once for each omit­ted case. Instead, CV can be com­puted after esti­mat­ing the model once on the com­plete data set.

 
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Highlighting the web

Published on 6 March 2014

Users of my new online fore­cast­ing book have asked about hav­ing a facil­ity for per­sonal high­light­ing of selected sec­tions, as stu­dents often do with print books. We have plans to make this a built-​​​​in part of the plat­form, but for now it is pos­si­ble to do it using a sim­ple browser exten­sion. This approach allows any web­site to be high­lighted, so is even more use­ful than if we only had the facil­ity on OTexts​.org. There are sev­eral pos­si­ble tools avail­able. One of the sim­plest tools that allows both high­light­ing and anno­ta­tions is Diigo.

 
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Interview for the Capital of Statistics

Published on 5 February 2014

Earo Wang recently inter­viewed me for the Chi­nese web­site Cap­i­tal of Sta­tis­tics. The Eng­lish tran­script of the inter­vew is on Earo’s per­sonal web­site. This is the third inter­view I’ve done in the last 18 months. The oth­ers were for: Data Min­ing Research. Repub­lished in Amstat News. DecisionStats.  

 
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Free books on statistical learning

Published on 30 January 2014

Hastie, Tib­shi­rani and Friedman’s Ele­ments of Sta­tis­ti­cal Learn­ing first appeared in 2001 and is already a clas­sic. It is my go-​​​​to book when I need a quick refresher on a machine learn­ing algo­rithm. I like it because it is writ­ten using the lan­guage and per­spec­tive of sta­tis­tics, and pro­vides a very use­ful entry point into the lit­er­a­ture of machine learn­ing which has its own ter­mi­nol­ogy for sta­tis­ti­cal con­cepts. A free down­load­able pdf ver­sion is avail­able on the web­site. Recently, a sim­pler related book appeared enti­tled Intro­duc­tion to Sta­tis­ti­cal Learn­ing with appli­ca­tions in R by James, Wit­ten, Hastie and Tib­shi­rani. It “is aimed for upper level under­grad­u­ate stu­dents, mas­ters stu­dents and Ph.D. stu­dents in the non-​​​​mathematical sci­ences”. This would be a great text­book for our new 3rd year sub­ject on Busi­ness Ana­lyt­ics. The R code is a wel­come addi­tion in show­ing how to imple­ment the meth­ods. Again, a free down­load­able pdf ver­sion is avail­able on the web­site. There is also a new, free book on Sta­tis­ti­cal foun­da­tions of machine learn­ing by Bön­tempi and Ben Taieb avail­able on the OTexts plat­form. This is more of a hand­book and is writ­ten by two authors com­ing from a machine learn­ing back­ground. R code is also pro­vided. Being an OTexts book, it is con­tin­u­ally updated and revised, and is freely avail­able

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Slides from my online forecasting course

Published on 23 January 2014

Last year I taught an online course on fore­cast­ing using R. The slides and exer­cise sheets are now avail­able at www​.otexts​.org/​f​p​p​/​r​e​s​o​u​rces/

 
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Questions on my online forecasting course

Published on 4 October 2013

I’ve been get­ting emails ask­ing ques­tions about my upcom­ing course on Fore­cast­ing using R. Here are some answers.

 
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OTexts​.org is launched

Published on 27 September 2013

The pub­lish­ing plat­form I set up for my fore­cast­ing book has now been extended to cover more books and greater func­tion­al­ity. Check it out at www​.otexts​.org.

 
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Forecasting with R

Published on 26 September 2013

The fol­low­ing video has been pro­duced to adver­tise my upcom­ing course on Fore­cast­ing with R, run in part­ner­ship with Rev­o­lu­tion Analytics.

 
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Online course on forecasting using R

Published on 11 September 2013

I am team­ing up with Rev­o­lu­tion Ana­lyt­ics to teach an online course on fore­cast­ing with R. Top­ics to be cov­ered include sea­son­al­ity and trends, expo­nen­tial smooth­ing, ARIMA mod­el­ling, dynamic regres­sion and state space mod­els, as well as fore­cast accu­racy meth­ods and fore­cast eval­u­a­tion tech­niques such as cross-​​​​validation. I will talk about some of my con­sult­ing expe­ri­ences, and explain the tools in the fore­cast pack­age for R. The course will run from 21 Octo­ber to 4 Decem­ber, for two hours each week. Par­tic­i­pants can net­work and inter­act with other prac­ti­tion­ers through an online community.

 
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