Rank: 1685 based on 57 downloads (last 30 days) and 3 files submitted

Statovic

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Professional Interests:: statistics, machine learning

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Files Posted by Statovic				View all 1 - 3 of 3
Updated	File	Tags	Downloads (last 30 days)	Comments	Rating
28 Oct 2008	Generate sorted vectors of uniformly distributed variates Efficiently generate sorted p-vectors of variates drawn uniformly from [0,1] Author: Statovic	mathematics, probability, statistics, random number generat..., simulation	22	3	3.0 3.0 \| 1 rating
24 Jul 2008	Exact Negative Log-likelihood of ARMA models via Kalman Filtering Computation of the exact negative log-likelihood of ARMA models using the Kalman Filter Author: Statovic	arma, loglikelihood, kalman filter, probability, statistics	24	1	3.0 3.0 \| 1 rating
22 Jul 2008	Non-negative Garotte Implementation of Leo Breiman's non-negative garotte for linear regression Author: Statovic	leo breiman, regression, stai, statistics, linear regression, probability	11	2

Comments and Ratings by Statovic		1 - 1 of 1
Updated	File	Comments	Rating
28 Oct 2008	Generate sorted vectors of uniformly distributed variates Efficiently generate sorted p-vectors of variates drawn uniformly from [0,1] Author: Statovic	The important difference between sort(rand(...)) and this algorithm is that the sort(.) procedure is at best O(n log(n)) time complexity, while this algorithm is linear time, i.e. O(n). For short length vectors the sort(.) approach will be quicker (due to a more efficient implementation of the sort(.) function), but for large n the differences can be significant. As an example: >> tic;sort(rand(1,10^7),2);toc Elapsed time is 1.757046 seconds. vs. >> tic;genrandsorted(1,10^7);toc Elapsed time is 0.531082 seconds. I am guessing that if one implemented the Bentley & Saxe algorithm in C it would probably be comparable to sort(.) for smaller 'n' (and of course, faster again for large 'n').

Comments and Ratings on Statovic's Files			View all 1 - 5 of 7
Updated	File	Comment by	Comments	Rating
16 Sep 2009	Exact Negative Log-likelihood of ARMA models via Kalman Filtering Computation of the exact negative log-likelihood of ARMA models using the Kalman Filter Author: Statovic	Alawieh, Fraunhofer IIS	the program is well written, but i believe the state space representation is wrong. Though the implemented part is as mentioned in the refernce paper still , I argue about the 'R' in arma_ConvertToSS Taking the MA parameters directly is not the proper representation. instead R=[ c1-a1;c2- a2-( c1-a1);.....]; ' SPECTRAL ESTIMATION FOR NOISY SIGNALS OBSERVED THROUGH A LINEAR SYSTEM' Check this paper
15 Sep 2009	Exact Negative Log-likelihood of ARMA models via Kalman Filtering Computation of the exact negative log-likelihood of ARMA models using the Kalman Filter Author: Statovic	Alawieh, Fraunhofer IIS
28 Oct 2008	Generate sorted vectors of uniformly distributed variates Efficiently generate sorted p-vectors of variates drawn uniformly from [0,1] Author: Statovic	Statovic	The important difference between sort(rand(...)) and this algorithm is that the sort(.) procedure is at best O(n log(n)) time complexity, while this algorithm is linear time, i.e. O(n). For short length vectors the sort(.) approach will be quicker (due to a more efficient implementation of the sort(.) function), but for large n the differences can be significant. As an example: >> tic;sort(rand(1,10^7),2);toc Elapsed time is 1.757046 seconds. vs. >> tic;genrandsorted(1,10^7);toc Elapsed time is 0.531082 seconds. I am guessing that if one implemented the Bentley & Saxe algorithm in C it would probably be comparable to sort(.) for smaller 'n' (and of course, faster again for large 'n').
28 Oct 2008	Generate sorted vectors of uniformly distributed variates Efficiently generate sorted p-vectors of variates drawn uniformly from [0,1] Author: Statovic	Jos	I acknowledge that this is indeed a clever algorithm but in matlab sort(rand(..)), as already suggested by Wolgang Schwanghart, is much to be prefered. Therefore this submission has only some educational value, for which the internal comments should be expanded.
28 Oct 2008	Generate sorted vectors of uniformly distributed variates Efficiently generate sorted p-vectors of variates drawn uniformly from [0,1] Author: Statovic	Schwanghart, Wolfgang	What makes the function different to the command x = sort(rand(n,p),2); ? I don't have the cited paper at hand, so it might be good to explain the difference, if there are any. Best regards, Wolfgang

Top Tags Applied by Statovic	1 - 5 of 13
statistics, probability, arma, kalman filter, leo breiman

Files Tagged by Statovic				View all 1 - 3 of 3
Updated	File	Tags	Downloads (last 30 days)	Comments	Rating
28 Oct 2008	Generate sorted vectors of uniformly distributed variates Efficiently generate sorted p-vectors of variates drawn uniformly from [0,1] Author: Statovic	mathematics, probability, statistics, random number generat..., simulation	22	3	3.0 3.0 \| 1 rating
24 Jul 2008	Exact Negative Log-likelihood of ARMA models via Kalman Filtering Computation of the exact negative log-likelihood of ARMA models using the Kalman Filter Author: Statovic	arma, loglikelihood, kalman filter, probability, statistics	24	1	3.0 3.0 \| 1 rating
22 Jul 2008	Non-negative Garotte Implementation of Leo Breiman's non-negative garotte for linear regression Author: Statovic	leo breiman, regression, stai, statistics, linear regression, probability	11	2

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