A new ordering principle for the classical statistical analysis of Poisson processes with background
نویسنده
چکیده
Inspired by the recent proposal by Feldman and Cousins [1] of a “unified approach to the classical statistical analysis of small signals” based on a choice of ordering in Neyman’s construction of classical confidence intervals, I propose a new ordering principle for the classical statistical analysis of Poisson processes with background which minimizes the effect on the resulting confidence intervals of the observation of less background events than expected. The new ordering principle is applied to the calculation of the confidence region implied by the recent null result of the KARMEN neutrino oscillation experiment [2]. PACS numbers: 06.20.Dk, 14.60.Pq Published in Phys. Rev. D 59, 053001 (1999). Typeset using REVTEX
منابع مشابه
One-for-One Period Policy and its Optimal Solution
In this paper we introduce the optimal solution for a simple and yet practical inventory policy with the important characteristic which eliminates the uncertainty in demand for suppliers. In this new policy which is different from the classical inventory policies, the time interval between any two consecutive orders is fixed and the quantity of each order is one. Assuming the fixed ordering cos...
متن کاملPoisson-Lindley INAR(1) Processes: Some Estimation and Forecasting Methods
This paper focuses on different methods of estimation and forecasting in first-order integer-valued autoregressive processes with Poisson-Lindley (PLINAR(1)) marginal distribution. For this purpose, the parameters of the model are estimated using Whittle, maximum empirical likelihood and sieve bootstrap methods. Moreover, Bayesian and sieve bootstrap forecasting methods are proposed and predict...
متن کاملLearning Bayesian Network Structure Using Genetic Algorithm with Consideration of the Node Ordering via Principal Component Analysis
‎The most challenging task in dealing with Bayesian networks is learning their structure‎. ‎Two classical approaches are often used for learning Bayesian network structure;‎ ‎Constraint-Based method and Score-and-Search-Based one‎. ‎But neither the first nor the second one are completely satisfactory‎. ‎Therefore the heuristic search such as Genetic Alg...
متن کاملExact Statistical Inference for Some Parametric Nonhomogeneous Poisson Processes
Nonhomogeneous Poisson processes (NHPPs) are often used to model recurrent events, and there is thus a need to check model fit for such models. We study the problem of obtaining exact goodness-of-fit tests for certain parametric NHPPs, using a method based on Monte Carlo simulation conditional on sufficient statistics. A closely related way of obtaining exact confidence intervals in parametri...
متن کاملMoving Average Processes with Infinite Variance
The sample autocorrelation function (acf) of a stationary process has played a central statistical role in traditional time series analysis, where the assumption is made that the marginal distribution has a second moment. Now, the classical methods based on acf are not applicable in heavy tailed modeling. Using the codifference function as dependence measure for such processes be shown it be as...
متن کامل