You can get a look of Matloff’s text here (= pdf for ya)

Manos Parzakonis  -  12:31 μ.μ.  -  Δημόσιο

Plz don’t make me type

data(BostonHousing)

ever again. I hate this dataset ;x
#rstats#rant

PS: Of course you can add me to your Google+ circles…

Fact checked…;(

• OkTrends http://blog.okcupid.com/
• Data Evolution (Dataspora) http://dataspora.com
• FiveThirtyEight http://www.fivethirtyeight.com/
• Brendan O’Connor’s Blog http://anyall.org
• Undirected Grad (Alex Smola) http://undirectedgrad.blogspot.com/
• Pete Warden http://petewarden.typepad.com
• Jeffry Heer http://hci.stanford.edu/jheer/
• Measuring Measures (Bradford Cross) http://measuringmeasures.com
• Info Vegan (Clay Johnson) http://www.infovegan.com

[source]

PS : (Confession) I didn’t like the Bayesian Data Analysis book.

A one-way anova shouted at a two-way anova: “stop! Turn around – you are going the wrong way!”
The two-way anova yelled back: “sorry! I will turn when i see an interaction!”

[source]

IPSUR stands for Introduction to Probability and Statistics Using R,
ISBN: 978-0-557-24979-4, which is a textbook written for an
undergraduate course in probability and statistics. The approximate
prerequisites are two or three semesters of calculus and some linear
algebra in a few places. Attendees of the class include mathematics,
engineering, and computer science majors.

Now, there is a new way to read R books (anyway, new to me!)

1
2
3

install.packages("IPSUR", repos="http://cran.r-project.org")
library(IPSUR)
read(IPSUR)

As I left consulting to go back to the university,these were the perceptions I had about working with data to find answers to problems:

(a) Focus on finding a good solution—that’s what consultants get paid for.
(b) Live with the data before you plunge into modeling.
(c) Search for a model that gives a good solution, either algorithmic or data.
(d) Predictive accuracy on test sets is the criterion for how good the model is.
(e) Computers are an indispensable partner.

Read more

Leo Breiman (2001), Statistical modeling: The two cultures, Statistical Science, 16:199-231 [pdf]

At that time I had some time to search more about it. One of the most common things to search for a test is a robust version of it (at least that’s what I search for!). A little search in the 3rd page of google results leads to the following :

One-sample and two-sample robust Hotelling tests with fast and robust bootstrap

The classical Hotelling test for testing if the mean equals a certain value or if two means are equal is modified into a robust one through substitution of the empirical estimates by the MM-estimates of location and scatter. The MM-estimator, using Tukey’s biweight function, is tuned by default to have a breakdown point of 50% and 95% location efficiency. This could be changed through the control argument if desired.

Robust Hotelling T2 test

Performs one and two sample Hotelling T2 tests as well as robust one-sample Hotelling T2 test.

The first uses MM and S estimators while the latter a Minimum Covariance Determinant one. You can get info on those on the links in the end of the post. What might be crucial to you is that MM/S estimators would be more time comsuming compared to MCD. A little demonstation is the following..

library(rrcov)

data(delivery)

delivery.x <- delivery[,1:2]

T2.test(delivery.x)

# 

#     One-sample Hotelling test

# 

# data:  delivery.x 

# T^2 = 21.0494, df1 = 2, df2 = 23, p-value = 6.365e-06

# alternative hypothesis: true mean vector is not equal to (0, 0)' 

#  

# sample estimates:

#               n.prod distance

# mean x-vector   8.76   409.28

t0<-Sys.time()

T2.test(delivery.x, method="mcd")

# 

#     One-sample Hotelling test (Reweighted MCD Location)

# 

# data:  delivery.x 

# T^2 = 37.701, df1 = 2.000, df2 = 9.146, p-value = 3.829e-05

# alternative hypothesis: true mean vector is not equal to (0, 0)' 

#  

# sample estimates:

#                n.prod distance

# MCD x-vector 6.190476 309.7143
Sys.time()-t0
# Time difference of 0.04200006 secs

library(FRB)

t0<-Sys.time()
FRBhotellingMM(delivery.x)

# One sample Hotelling test based on multivariate MM-estimates
# (bdp = 0.5, eff = 0.95) 

# data:  delivery.x 

# T^2_R =  84.59 

# p-value =  0.0022 

# Alternative hypothesis : true mean vector is not equal to ( 0 0 ) 
Sys.time()-t0
# Time difference of 4.859 secs

Time consuming as it may is I would stick with the Bootstrap method. What would you do?

Read more

Roelant, E., Van Aelst, S., and Willems, G. (2008), “Fast Bootstrap for Robust Hotelling Tests,” COMPSTAT 2008: Proceedings in Computational Statistics (P. Brito, Ed.) Heidelberg: Physika-Verlag, to appear.

Willems G., Pison G., Rousseeuw P. and Van Aelst S. (2002), A robust hotelling test, Metrika, 55, 125–138.

In case you’re curious too this is a good study material from R-Bloggers :

maps ; geographical ; spatial

Ok. This could be a tweet rather than a post…