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by Polter
August 8th, 2009, 11:15 pm
Forum: Programming and Software Forum
Topic: Software that can solve SDEs analytically?
Replies: 8
Views: 37846

Software that can solve SDEs analytically?

<r>Haven't really had time to play with those, but perhaps you might want to take a look:<URL url="http://www-stat.wharton.upenn.edu/~steele/Publications/PDF/AoMtSC.pdfhttp://www.warwick.ac.uk/statsdept/staff/WSK/ca.htmlhttp://www2.warwick.ac.uk/fac/sci/statistics/staff/academic/kendall/personal/ca/...
by Polter
June 21st, 2008, 2:55 pm
Forum: General Forum
Topic: control sample for logistic regression forecasting
Replies: 10
Views: 54486

control sample for logistic regression forecasting

<r>In case of v.small / v.large asymmetric probabilities it might also be worth looking at complementary log log (cloglog) regression -- just like a logistic one, but with another link function:<URL url="http://www.gseis.ucla.edu/courses/ed231c/notes2/clog.htmlhttp://planetmath.org/encyclopedia/Comp...
by Polter
June 2nd, 2008, 4:53 pm
Forum: Numerical Methods Forum
Topic: Specral Decomposition of a large matrix
Replies: 10
Views: 56490

Specral Decomposition of a large matrix

The first one (rmvnorm from the mvtnorm package) works rather fine for me (could be faster).Here's the output for my case (I also use genPositiveDefMat from clusterGeneration) -- let me know if that helps:
by Polter
May 26th, 2008, 7:47 pm
Forum: Numerical Methods Forum
Topic: Specral Decomposition of a large matrix
Replies: 10
Views: 56490

Specral Decomposition of a large matrix

<r>I think it's simple to just use Cholesky decomposition for this. Check here for example in R:<URL url="http://astrostatistics.psu.edu/su07/R/mv.htmlStill">http://astrostatistics.psu.edu/su07/R/mv.htmlStill</URL>, R has plenty of packages which have this functionality (generating multivariate Gaus...
by Polter
May 26th, 2008, 12:42 pm
Forum: Numerical Methods Forum
Topic: Specral Decomposition of a large matrix
Replies: 10
Views: 56490

Specral Decomposition of a large matrix

<r>I assume you're using eigen(X), where X is your matrix. Does it help when you set symmetric (also works for Hermitian if the matrix is complex) to true, i.e. eigen(X, symmetric = TRUE)? What is your ultimate goal, by the way? If you need this for the PCA, for example, then perhaps there's a faste...