Nuisance parameter, systematic uncertain

Nuisance parameters and systematic uncertainties: 

Nuisance parameters and systematic uncertainties Glen Cowan Glen Cowan Royal Holloway, University of London g.cowan@rhul.ac.uk www.pp.rhul.ac.uk/~cowan IoP Half Day Meeting on Statistics in High Energy Physics University of Manchester 16 November, 2005 Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester

Vague outline: 

Vague outline Glen Cowan I. Nuisance parameters and systematic uncertainty II. Parameter measurement Frequentist Bayesian III. Estimating intervals (setting limits) Frequentist Bayesian IV. Conclusions Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester

Statistical vs. systematic errors : 

Statistical vs. systematic errors Glen Cowan Statistical errors: How much would the result fluctuate upon repetition of the measurement? Implies some set of assumptions to define probability of outcome of the measurement. Systematic errors: What is the uncertainty in my result due to uncertainty in my assumptions, e.g., model (theoretical) uncertainty; modeling of measurement apparatus. The sources of error do not vary upon repetition of the measurement. Often result from uncertain value of, e.g., calibration constants, efficiencies, etc. Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester

Nuisance parameters : 

Nuisance parameters Glen Cowan Suppose the outcome of the experiment is some set of data values x (here shorthand for e.g. x1, ..., xn). We want to determine a parameter q, (could be a vector of parameters q1, ..., q n). The probability law for the data x depends on q : L(x| q) (the likelihood function) E.g. maximize L to find estimator Now suppose, however, that the vector of parameters: contains some that are of interest, and others that are not of interest: Symbolically: The are called nuisance parameters. Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester

Example #1: fitting a straight line: 

Example #1: fitting a straight line Glen Cowan Data: Model: measured yi independent, Gaussian: assume xi and si known. Goal: estimate q0 (don’t care about q1). Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester

Case #1: q1 known a priori: 

Case #1: q1 known a priori Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester For Gaussian yi, ML same as LS Minimize c2 → estimator Come up one unit from to find

Slide7: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester Correlation between causes errors to increase. Standard deviations from tangent lines to contour Case #2: both q0 and q1 unknown

Slide8: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester The information on q1 improves accuracy of Case #3: we have a measurement t1 of q1

Slide9: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester The ‘tangent plane’ method is a special case of using the profile likelihood: The profile likelihood is found by maximizing L (q0, q1) for each q0. Equivalently use The interval obtained from is the same as what is obtained from the tangents to Well known in HEP as the ‘MINOS’ method in MINUIT. Profile likelihood is one of several ‘pseudo-likelihoods’ used in problems with nuisance parameters. See e.g. talk by Rolke at PHYSTAT05.

Slide10: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester The Bayesian approach In Bayesian statistics we can associate a probability with a hypothesis, e.g., a parameter value q. Interpret probability of q as ‘degree of belief’ (subjective). Need to start with ‘prior pdf’ p(q), this reflects degree of belief about q before doing the experiment. Our experiment has data x, → likelihood function L(x|q). Bayes’ theorem tells how our beliefs should be updated in light of the data x: Posterior pdf p(q|x) contains all our knowledge about q.

Slide11: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester Case #4: Bayesian method We need to associate prior probabilities with q0 and q1, e.g., Putting this into Bayes’ theorem gives: posterior Q likelihood  prior ← based on previous measurement reflects ‘prior ignorance’, in any case much broader than

Slide12: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester Bayesian method (continued) Ability to marginalize over nuisance parameters is an important feature of Bayesian statistics. We then integrate (marginalize) p(q0, q1 | x) to find p(q0 | x): In this example we can do the integral (rare). We find

Slide13: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester Digression: marginalization with MCMC Bayesian computations involve integrals like often high dimensionality and impossible in closed form, also impossible with ‘normal’ acceptance-rejection Monte Carlo. Markov Chain Monte Carlo (MCMC) has revolutionized Bayesian computation. Google for ‘MCMC’, ‘Metropolis’, ‘Bayesian computation’, ... MCMC generates correlated sequence of random numbers: cannot use for many applications, e.g., detector MC; effective stat. error greater than √n . Basic idea: sample multidimensional look, e.g., only at distribution of parameters of interest.

Slide14: 

Although numerical values of answer here same as in frequentist case, interpretation is different (sometimes unimportant?) Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester Example: posterior pdf from MCMC Sample the posterior pdf from previous example with MCMC: Summarize pdf of parameter of interest with, e.g., mean, median, standard deviation, etc.

Slide15: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester Case #5: Bayesian method with vague prior Suppose we don’t have a previous measurement of q1 but rather some vague information, e.g., a theorist tells us: q1 ≥ 0 (essentially certain); q1 should have order of magnitude less than 0.1 ‘or so’. Under pressure, the theorist sketches the following prior: From this we will obtain posterior probabilities for q0 (next slide). We do not need to get the theorist to ‘commit’ to this prior; final result has ‘if-then’ character.

Slide16: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester Sensitivity to prior Vary () to explore how extreme your prior beliefs would have to be to justify various conclusions (sensitivity analysis). Try exponential with different mean values... Try different functional forms...

Slide17: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester Example #2: Poisson data with background Count n events, e.g., in fixed time or integrated luminosity. s = expected number of signal events b = expected number of background events n ~ Poisson(s+b): Sometimes b known, other times it is in some way uncertain. Goal: measure or place limits on s, taking into consideration the uncertainty in b. Widely discussed in HEP community, see e.g. proceedings of PHYSTAT meetings, Durham, Fermilab, CERN workshops...

Slide18: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester Setting limits Frequentist intervals (limits) for a parameter s can be found by defining a test of the hypothesized value s (do this for all s): Specify values of the data n that are ‘disfavoured’ by s (critical region) such that P(n in critical region) ≤ g for a prespecified g, e.g., 0.05 or 0.1. (Because of discrete data, need inequality here.) If n is observed in the critical region, reject the value s. Now invert the test to define a confidence interval as: set of s values that would not be rejected in a test of size g (confidence level is 1 - g ). The interval will cover the true value of s with probability ≥ 1 - g. Equivalent to Neyman confidence belt construction.

Slide19: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester Setting limits: ‘classical method’ E.g. for upper limit on s, take critical region to be low values of n, limit sup at confidence level 1 - b thus found from Similarly for lower limit at confidence level 1 - a, Sometimes choose a = b = g /2 → central confidence interval.

Slide20: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester Likelihood ratio limits (Feldman-Cousins) Define likelihood ratio for hypothesized parameter value s: Here is the ML estimator, note Critical region defined by low values of likelihood ratio. Resulting intervals can be one- or two-sided (depending on n). (Re)discovered for HEP by Feldman and Cousins, Phys. Rev. D 57 (1998) 3873.

Slide21: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester Nuisance parameters and limits In general we don’t know the background b perfectly. Suppose we have a measurement of b, e.g., bmeas ~ N (b, b) So the data are really: n events and the value bmeas. In principle the confidence interval recipe can be generalized to two measurements and two parameters. Difficult and rarely attempted, but see e.g. talk by G. Punzi at PHYSTAT05. G. Punzi, PHYSTAT05

Slide22: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester Bayesian limits with uncertainty on b Uncertainty on b goes into the prior, e.g., Put this into Bayes’ theorem, Marginalize over b, then use p(s|n) to find intervals for s with any desired probability content. Controversial part here is prior for signal s(s) (treatment of nuisance parameters is easy).

Slide23: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester Cousins-Highland method Regard b as ‘random’, characterized by pdf (b). Makes sense in Bayesian approach, but in frequentist model b is constant (although unknown). A measurement bmeas is random but this is not the mean number of background events, rather, b is. Compute anyway This would be the probability for n if Nature were to generate a new value of b upon repetition of the experiment with b(b). Now e.g. use this P(n;s) in the classical recipe for upper limit at CL = 1 - b: Result has hybrid Bayesian/frequentist character.

Slide24: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester ‘Integrated likelihoods’ Consider again signal s and background b, suppose we have uncertainty in b characterized by a prior pdf b(b). Define integrated likelihood as also called modified profile likelihood, in any case not a real likelihood. Now use this to construct likelihood ratio test and invert to obtain confidence intervals. Feldman-Cousins andamp; Cousins-Highland (FHC2), see e.g. J. Conrad et al., Phys. Rev. D67 (2003) 012002 and Conrad/Tegenfeldt PHYSTAT05 talk. Calculators available (Conrad, Tegenfeldt, Barlow).

Slide25: 

Glen Cowan Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester Interval from inverting profile LR test Suppose we have a measurement bmeas of b. Build the likelihood ratio test with profile likelihood: and use this to construct confidence intervals. See PHYSTAT05 talks by Cranmer, Feldman, Cousins, Reid.

Wrapping up: 

Wrapping up Glen Cowan I’ve shown a few ways of treating nuisance parameters in two examples (fitting line, Poisson mean with background). No guarantee this will bear any relation to the problem you need to solve... At recent PHYSTAT meetings the statisticians have encouraged physicists to: learn Bayesian methods, don’t get too fixated on coverage, try to see statistics as a ‘way of thinking’ rather than a collection of recipes. I tend to prefer the Bayesian methods for systematics but still a very open area of discussion. Statistics in HEP, IoP Half Day Meeting, 16 November 2005, Manchester