1. Lucio Baggio - Lucio Baggio - False discovery rate: setting the probability of false claim of detection 1 False discovery rate: setting the probability of false claim of detection Lucio Baggio Italy, INFN and University of Trento

2. Lucio Baggio - Lucio Baggio - False discovery rate: setting the probability of false claim of detection 2  ” We made many different (almost independent) background and coincidence counts using different values for the target signal amplitude (thresholds)” Where does this FDR comes from?  I met prof. James T. Linnemann (MSU) at PHYSTAT2003, I explained him the problem we had in assessing frequentist probability for IGEC results…  ” At last, one of the 90% confidence intervals was not including the null hypothesis… but when one accounts for many trials, it is possible to compute that with 30% probability we had a chance that at least one of the tests falsely rejected the null hypothesis. So, no true discovery, after all.”  ” Perhaps next time we should use 99.99% confidence intervals, in order to make the probability of false claim still low after tens of trials…But I’m afraid that signals cannot any more emerge with such stringent a requirement.”  He pointed out that maybe what I’m looking for are false discovery rate methods… Thanks, Jim!!

3. Lucio Baggio - Lucio Baggio - False discovery rate: setting the probability of false claim of detection 3 Why FDR? When should I care of multiple test procedures?. All sky surveys: many source directions and polarizations are tried Template banks Wide-open-eyes searches: many analysis pipelines are tried altogether, with different amplitude thresholds, signal durations, and so on Periodic updates of results: every new science run is a chance for a “discovery”. “Maybe next one is the good one”. Many graphical representations or aggregations of the data: “If I change the binning, maybe the signal shows up better…

4. Lucio Baggio - Lucio Baggio - False discovery rate: setting the probability of false claim of detection 4 Preliminary (1) : hypothesis testing False discoveries (false positives) Detected signals (true positives) Reported signal candidates inefficiency Null Retained (can’t reject) Reject= Reject Null = Accept Accept Alternative Total Null (H o ) True Background (noise) U B Type I Error α = ε b momo Alternative True signal Type II Error β = 1- ε s T Sm1m1 m-R R = S+B m

5. Lucio Baggio - Lucio Baggio - False discovery rate: setting the probability of false claim of detection 5 Preliminary (2): p-level Assume you have a model for the noise that affects the measure x. However, for our purposes it is sufficient assuming that the signal can be distinguished from the noise, i.e. dP/dp  1. Typically, the measured values of p are biased toward 0. signal You derive a test statistics t(x) from x. F(t) is the distribution of t when x is sampled from noise only (off-signal). The p-level associated with t(x) is the value of the distribution of t in t(x): p = F(t) = P(t>t(x)) Example:  2 test  p is the “one-tail”  2 probability associated with n counts (assuming d degrees of freedom) Usually, the alternative hypothesis is not known. p-level 1 background pdf The distribution of p is always linearly raising in case of agreement of the noise with the model P(p)=p  dP/dp = 1

6. Lucio Baggio - Lucio Baggio - False discovery rate: setting the probability of false claim of detection 6 Usual multiple testing procedures For each hypothesis test, the condition {p<   reject null} leads to false positives with a probability  In case of multiple tests (need not to be the same test statistics, nor the same tested null hypothesis), let p={p 1, p 2, … p m } be the set of p-levels. m is the trial factor. We select “discoveries” using a threshold T(p): {p j <T(p)  reject null}. Uncorrected testing: T(p)=  –The probability that at least one rejection is wrong is P(B>0) = 1 – (1-  ) m ~ m  hence false discovery is guaranteed for m large enough Fixed total 1 st type errors (Bonferroni): T(p)=  /m –Controls familywise error rate in the most stringent manner: P(B>0) =  –This makes mistakes rare… –… but in the end efficiency (2 nd type errors) becomes negligible!!

7. Lucio Baggio - Lucio Baggio - False discovery rate: setting the probability of false claim of detection 7 p S pdf m0m0 Let us make a simple case when signals are easily separable (e.g. high SNR) Controlling false discovery fraction We desire to control (=bound) the ratio of false discoveries over the total number of claims: B/R = B/(B+S)  q. The level T(p) is then chosen accordingly. B m q p B S cumulative counts R

8. Lucio Baggio - Lucio Baggio - False discovery rate: setting the probability of false claim of detection 8 Benjamini & Hochberg FDR control procedure Among the procedures that accomplish this task, one simple recipe was proposed by Benjamini & Hochberg (JRSS-B (1995) 57:289-300 ) choose your desired FDR q (don’t ask too much!); define c(m)=1 if p-values are independent or positively correlated; otherwise c(m)=Sum j (1/j) compute p-values {p 1, p 2, … p m } for a set of tests, and sort them in creasing order; p m determine the threshold T(p)= p k by finding the index k such that p j k; reject H 0 q/c(m)

9. Lucio Baggio - Lucio Baggio - False discovery rate: setting the probability of false claim of detection 9 Summary In case of multiple tests one wants to control the false claim probability, but it is advisable to mitigate the strict requirement that we want NO false claim, which could end up in burying the signals also. Controlling FDR seems to be a wise suggestion. This talk was based mainly on Miller et. al. ApJ 122: 3492-3505 Dec 2001 http://arxiv.org/abs/astro-ph/0107034 and Jim Linnemann’s talk http://user.pa.msu.edu/linnemann/public/FDR/ However, there is a fairly wide literature aboute this, if one looks for references in biology, imaging, HEP, and recently also astrophysics, at last!