Estimating a Signal In the Presence of an Unknown Background

We describe a method for fitting distributions to data which only requires knowledge of the parametric form of either the signal or the background but not both. The unknown distribution is fit using a non-parametric kernel density estimator. The method returns parameter estimates as well as errors on those estimates. Simulation studies show that these estimates are unbiased and that the errors are correct

Correcting the Minimization Bias in Searches for Small Signals

We discuss a method for correcting the bias in the limits for small signals if those limits were found based on cuts that were chosen by minimizing a criterion such as sensitivity. Such a bias is commonly present when a "minimization" and an "evaluation" are done at the same time. We propose to use a variant of the bootstrap to adjust the limits. A Monte Carlo study shows that these new limits have correct coverage.Comment: 14 pages, 5 figue

Limits and Confidence Intervals in the Presence of Nuisance Parameters

We study the frequentist properties of confidence intervals computed by the method known to statisticians as the Profile Likelihood. It is seen that the coverage of these intervals is surprisingly good over a wide range of possible parameter values for important classes of problems, in particular whenever there are additional nuisance parameters with statistical or systematic errors. Programs are available for calculating these intervals.Comment: 6 figure

A Test for the Presence of a Signal, with Multiple Channels and Marked Poisson

We describe a statistical hypothesis test for the presence of a signal based on the likelihood ratio statistic. We derive the test for a special case of interest. We study extensions of the test to cases where there are multiple channels and to marked Poisson distributions. We show the results of a number of performance studies which indicate that the test works very well, even far out in the tails of the distribution and with multiple channels and marked Poisson.Comment: 21 pages, 6 figue

Confidence Intervals and Upper Bounds for Small Signals in the Presence of Background Noise

We discuss a new method for setting limits on small signals in the presence of background noise. The method is based on a combination of a two dimensional confidence region and the large sample approximation to the likelihood ratio test statistic. It automatically quotes upper limits for small signals and two-sided confidence intervals for larger samples. We show that this method gives the correct coverage and also has good power.Comment: Document was created by Sciword V3.0, it consists of one main document (lrt.tex), eight figures (figure1.eps - figure8.eps) and one table (table.tex). Paper was revised after being accepted for publication in NIM A Paper was revised after being accepted for publication in NIM

The Power to See: A New Graphical Test of Normality

Many statistical procedures assume the underlying data generating process involves Gaussian errors. Among the well-known procedures are ANOVA, multiple regression, linear discriminant analysis and many more. There are a few popular procedures that are commonly used to test for normality such as the Kolmogorov-Smirnov test and the ShapiroWilk test. Excluding the Kolmogorov-Smirnov testing procedure, these methods do not have a graphical representation. As such these testing methods offer very little insight as to how the observed process deviates from the normality assumption. In this paper we discuss a simple new graphical procedure which provides confidence bands for a normal quantile-quantile plot. These bands define a test of normality and are much narrower in the tails than those related to the Kolmogorov-Smirnov test. Correspondingly the new procedure has much greater power to detect deviations from normality in the tails