I have almost done with showing the other parts, but I am struggling with the last part of the third one to deduce the relationship between beta and beta hat.

I would be grateful if anyone can give me a piece of help to make me proceed on.

3 November 2013

(category : Exercices Théoriques)

I have almost done with showing the other parts, but I am struggling with the last part of the third one to deduce the relationship between beta and beta hat.

I would be grateful if anyone can give me a piece of help to make me proceed on.

Posted by Sungyeon Hong at 0:56

Comments (1)

29 October 2013

(category : Exercices Théoriques)

The lecture note remarks that they are different, but could anyone explain about their difference exactly?

Posted by Sungyeon Hong at 17:46

(category : Exercices Théoriques)

Despite the kind explanations in the answer sheet, I am not sure how to calculate the part (a) of the 4th exercise.

Could you let me know some more detail?

Posted by Sungyeon Hong at 16:35

28 October 2013

(category : Exercices Théoriques)

For the parts (a) and (b) in the 4th exercise of set 5, I have to describe the first p elements and the rest (n-p) elements of each fixed value \hat{\tilde{y}} and I have done computing it, but I am afraid if I do not have any idea of how to describe them.

And for the part (c), could anyone help a bit more for me to think further on?

Posted by Sungyeon Hong at 0:06

26 October 2013

(category : Théorie)

In the lecture slide of Week 1, there are seven properties of Gaussian vectors and the first one is about the MGF. It is written that M_Y(u) =exp{ μ^T u + 1/2 u^T Ω u}, but isn't it u^T μ for the first term of the exponent? Or it doesn't matter because they have the same value, I mean, μ^T u = u^T μ?

Posted by Sungyeon Hong at 23:37

(category : Informations Générales)

Is there any difference between a regression model and a fitted model?

Posted by Sungyeon Hong at 13:28

25 October 2013

(category : Informations Générales)

Some of you have asked me for recommended references regarding general statistics and regression models. My personal recommendation would be to consult the following books:

- For general introductory statistics up to and including linear regression: G. Casella, R.L. Berger, Statistical Inference, 2nd ed., Duxbury Press, 2002.

- For an accessible introduction to linear regression models (especially LASSO and Ridge regression): Chapter 3 of The Elements of Statistial Learning available as pdf at http://www-stat.stanford.edu/~tibs/ElemStatLearn/

- For a detailed treatment of essentially all important aspects of regression models: N.R. Draper, H.S. Smith. Applied regression analysis, 3rd ed., Wiley, 1998. (Note that the 3rd edition is a significant update of the earlier editions.)

- For a concise, yet detailed treatment of linear regression: Chapter 8 of A.C. Davison, Statistical models, Cambridge University Press, 2003.

Posted by Mikael Kuusela at 16:30

20 October 2013

(category : Exercices Théoriques)

For the second problem in Exercise set 4, is it right to use the table of "cement heat evolution" from the lecture slide?

I wonder why we have only three variables for x's: x1, x2, and x3.

To be more specific, what are x's in this case?

There might be something I misunderstand, but I hope someone would be able to help me.

Posted by Sungyeon Hong at 16:50

14 October 2013

(category : Exercices Théoriques)

Hi, I have been solving the homework problem 1 from Exercise Set 3, but I am not sure what I am going to use to prove the last part (d).

Is everyone doing well with this?

Posted by Sungyeon Hong at 21:53

30 September 2013

(category : Exercices avec R, Practicals, astuces R, astuces Latex)

You may have noticed that standard R only comes with a command line user's interface (on OS X there is also a simple GUI provided). I recently discovered a very nice and useful GUI for R called RStudio (http://www.rstudio.com/). It makes working with R very simple and pleasant and I would recommend you to give it a try when you are working on the practical exercises!

Posted by Mikael Kuusela at 14:02

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