## generating from a failure rate function [X’ed]

Posted in Books, Kids, Statistics, University life with tags , , , , , , , , on July 4, 2015 by xi'an

While I now try to abstain from participating to the Cross Validated forum, as it proves too much of a time-consuming activity with little added value (in the sense that answers are much too often treated as disposable napkins by users who cannot be bothered to open a textbook and who usually do not exhibit any long-term impact of the provided answer, while clogging the forum with so many questions that the individual entries seem to get so little traffic, when compared say with the stackoverflow forum, to the point of making the analogy with disposable wipes more appropriate!), I came across a truly interesting question the other night. Truly interesting for me in that I had never considered the issue before.

The question is essentially wondering at how to simulate from a distribution defined by its failure rate function, which is connected with the density f of the distribution by $\eta(t)=\frac{f(t)}{\int_t^\infty f(x)\,\text{d}x}=-\frac{\text{d}}{\text{d}t}\,\log \int_t^\infty f(x)\,\text{d}x$

From a purely probabilistic perspective, defining the distribution through f or through η is equivalent, as shown by the relation $F(t)=1-\exp\left\{-\int_0^t\eta(x)\,\text{d}x\right\}$

but, from a simulation point of view, it may provide a different entry. Indeed, all that is needed is the ability to solve (in X) the equation $\int_0^X\eta(x)\,\text{d}x=-\log(U)$

when U is a Uniform (0,1) variable. Which may help in that it does not require a derivation of f. Obviously, this also begs the question as to why would a distribution be defined by its failure rate function.

## simulating correlated Binomials [another Bernoulli factory]

Posted in Books, Kids, pictures, R, Running, Statistics, University life with tags , , , , , , , on April 21, 2015 by xi'an This early morning, just before going out for my daily run around The Parc, I checked X validated for new questions and came upon that one. Namely, how to simulate X a Bin(8,2/3) variate and Y a Bin(18,2/3) such that corr(X,Y)=0.5. (No reason or motivation provided for this constraint.) And I thought the following (presumably well-known) resolution, namely to break the two binomials as sums of 8 and 18 Bernoulli variates, respectively, and to use some of those Bernoulli variates as being common to both sums. For this specific set of values (8,18,0.5), since 8×18=12², the solution is 0.5×12=6 common variates. (The probability of success does not matter.) While running, I first thought this was a very artificial problem because of this occurrence of 8×18 being a perfect square, 12², and cor(X,Y)x12 an integer. A wee bit later I realised that all positive values of cor(X,Y) could be achieved by randomisation, i.e., by deciding the identity of a Bernoulli variate in X with a Bernoulli variate in Y with a certain probability ϖ. For negative correlations, one can use the (U,1-U) trick, namely to write both Bernoulli variates as $X_1=\mathbb{I}(U\le p)\quad Y_1=\mathbb{I}(U\ge 1-p)$

in order to minimise the probability they coincide.

I also checked this result with an R simulation

> z=rbinom(10^8,6,.66)
> y=z+rbinom(10^8,12,.66)
> x=z+rbinom(10^8,2,.66)
cor(x,y)
> cor(x,y)
 0.5000539


Searching on Google gave me immediately a link to Stack Overflow with an earlier solution with the same idea. And a smarter R code.

## another R new trick [new for me!]

Posted in Books, Kids, R, Statistics, University life with tags , , , , , , , on July 16, 2014 by xi'an While working with Andrew and a student from Dauphine on importance sampling, we wanted to assess the distribution of the resulting sample via the Kolmogorov-Smirnov measure $\max_x |\hat{F_n}(x)-F(x)|$

where F is the target.  This distance (times √n) has an asymptotic distribution that does not depend on n, called the Kolmogorov distribution. After searching for a little while, we could not figure where this distribution was available in R. It had to, since ks.test was returning a p-value. Hopefully correct! So I looked into the ks.test function, which happens not to be entirely programmed in C, and found the line

PVAL <- 1 - if (alternative == "two.sided")
.Call(C_pKolmogorov2x, STATISTIC, n)


which means that the Kolmogorov distribution is coded as a C function C_pKolmogorov2x in R. However, I could not call the function myself.

> .Call(C_pKolmogorov2x,.3,4)


Hence, as I did not want to recode this distribution cdf, I posted the question on stackoverflow (long time no see!) and got a reply almost immediately as to use the package kolmim. Followed by the extra comment from the same person that calling the C code only required to add the path to its name, as in

> .Call(stats:::C_pKolmogorov2x,STAT=.3,n=4)
 0.2292


## Le Monde sans puzzle [& sans penguins]

Posted in Books, Kids, R, University life with tags , , , , , on April 12, 2014 by xi'an As the Le Monde mathematical puzzle of this week was a geometric one (the quadrangle ABCD is divided into two parts with the same area, &tc…) , with no clear R resolution, I chose to bypass it. In this April 3 issue, several items of interest: first, a report by Etienne Ghys on Yakov Sinaï’s Abel Prize for his work “between determinism and randomness”, centred on ergodic theory for dynamic systems, which sounded like the ultimate paradox the first time I heard my former colleague Denis Bosq give a talk about it in Paris 6. Then a frightening fact: the summer conditions have been so unusually harsh in Antarctica (or at least near the Dumont d’Urville French austral station) that none of the 15,000 Adélie penguin couples studied there managed to keep their chick alive. This was due to an ice shelf that did not melt at all over the summer, forcing the penguins to walk an extra 40k to reach the sea… Another entry on the legal obligation for all French universities to offer a second chance exam, no matter how students are evaluated in the first round. (Too bad, I always find writing a second round exam a nuisance.)

## Le Monde puzzle [#843]

Posted in Books, Kids, R with tags , , , , , on December 7, 2013 by xi'an A Le Monde mathematical puzzle of moderate difficulty:

How many binary quintuplets (a,b,c,d,e) can be found such that any pair of quintuplets differs by at least two digits?

I solved it by the following R code that iteratively eliminates quintuplets that are not different enough from the first ones, for a random order of the 2⁵ quintuplets because the order matters in the resulting number (the intToBits trick was provided by an answer on StackExchange/stackoverflow):

sol=0
for (t in 1:10^5){ #random permutations
as.integer(intToBits(x))})[1:5,sample(1:32)]
V=32;inin=rep(TRUE,V);J=1
while (J<V){
for (i in (J+1):V)
inin[i]=FALSE
J=J+1}
if (sol<V){
}


which returns solutions like

> sol
 16
> levote
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11]
[1,]  0    0    0    0    1    1    1    1    0     1     0
[2,]  0    1    0    1    0    1    0    1    0     1     1
[3,]  0    1    1    0    1    0    1    1    1     0     0
[4,]  0    1    1    1    0    0    0    0    0     1     0
[5,]  0    0    0    0    0    0    1    0    0     0     0
[,12] [,13] [,14] [,15] [,16]
[1,]    0    1     1     0     1
[2,]    0    1     1     0     1
[3,]    1    0     0     1     1
[4,]    0    0     1     1     0
[5,]    1    0     1     0     1