Unified approach to the classical statistical analysis of small signals Gary J. Feldman and Robert...
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![Page 1: Unified approach to the classical statistical analysis of small signals Gary J. Feldman and Robert D. Cousins.](https://reader036.fdocuments.us/reader036/viewer/2022062421/56649c7b5503460f9492e75d/html5/thumbnails/1.jpg)
Unified approach to the classical statistical analysis of
small signals
Gary J. Feldman and
Robert D. Cousins
![Page 2: Unified approach to the classical statistical analysis of small signals Gary J. Feldman and Robert D. Cousins.](https://reader036.fdocuments.us/reader036/viewer/2022062421/56649c7b5503460f9492e75d/html5/thumbnails/2.jpg)
Statistical Methods and Notation
• Frequentist - P(x0 | t)
• Bayesian - P(t | x0) - needs subjective P(t)
• Feldman and Cousins use a frequentist (classical) model
• Confidence interval - [1, 2]– A percentage (typically 90%) of all attempts to
measure the value will give a value in this range
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Confidence Belt
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FIG. 1. A generic confidence belt construction and its use. For each value of m, one draws a horizontal acceptance interval [x1 ,x2] such that P(x [x1 ,x2]|) = . Upon performing an experiment to measure x and obtaining the value x0, one draws the dashed vertical line through x0 . The confidence interval [m1 ,m2] is the union of all values of m for which the corresponding acceptance interval is intercepted by the vertical line.
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Gaussian Curve
y = 1/sqrt(2)*exp(-x2/2)
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FIG. 2. Standard confidence belt for 90% C.L. upper limits for the mean of a Gaussian, in units of the rms deviation. The second line in the belt is at x = +.
FIG. 3. Standard confidence belt for 90% C.L. central confidence intervals for the mean of a Gaussian, in units of the rms deviation.
Gaussian
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FIG. 4. Plot of confidence belts implicitly used for 90% C.L. confidence intervals (vertical intervals between the belts) quoted by flip-flopping physicist X, described in the text. They are not valid confidence belts, since they can cover the true value at a frequency less than the stated confidence level. For 1.36<<4.28, the coverage (probability contained in the horizontal acceptance interval) Is 85%.
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Poisson Distribution
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FIG. 5. Standard confidence belt for 90% C.L. upper limits, for unknown Poisson signal mean m in the presence of a Poisson background with known mean b=3.0. The second line in the belt is at n=+.
FIG. 6. Standard confidence belt for 90% C.L. central confidence intervals, for unknown Poisson signal mean m in the presence of a Poisson background with known mean b=3.0.
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Feldman-Cousins Construction
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Solves Null Set problem
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FIG. 7. Confidence belt based on our ordering principle, for 90% C.L. confidence intervals for unknown Poisson signal mean m in the presence of a Poisson background with known mean b=3.0.
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Background
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FIG. 8. Upper end m2 of our 90% C.L. confidence intervals [m1 , m2], for unknown Poisson signal mean m in the presence of an expected Poisson background with known mean b. The curves for the cases n0 from 0 through 10 are plotted. Dotted portions on the upper left indicate regions where m1 is non-zero ~and shown in the following figure!. Dashed portions in the lower right indicate regions where the probability of obtaining the number of events observed or fewer is less than 1%, even if m=0.
FIG. 9. Lower end m1 of our 90% C.L. confidence intervals [m1 ,m2], for unknown Poisson signal mean m in the presence of an expected Poisson background with known mean b. The curves correspond to the dotted regions in the plots of m2 of the previous figure, with again n0=10 for the upper right curve, etc.
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Gaussian Application
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FIG. 10. Plot of our 90% confidence intervals for the mean of aGaussian, constrained to be non-negative, described in the text.
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Fewer Events than Predicted Background
• A good experiment could get a worse upper limit than a bad one if the predicted background was greater
• Define new term - “sensitivity”– Upper limit that would be obtained by an
ensemble of experiments with the expected background and no true signal
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Sensitivity
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FIG. 15. Comparison of the confidence region for an example of the toy model in which sin2(2)=0 and the sensitivity of the experiment, as defined in the text.
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Goodness of fit
• Decouples confidence interval from goodness-of-fit C.L.
• Standard classical intervals can give empty sets for confidence interval– Gaussian: if = 0, then 10% of the time,
the empty set is obtained– This is equivalent to a failed goodness of fit
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Feldman Cousins Approach
• Unified approach - no distinct choices on whether to use an upper limit of central limit
• Solves under-coverage problem (85%, when stated 90%)
• Cost of a little over-coverage