NP-Optimal Kernels for Nonparametric Sequential Detection Rules
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Ansgar Steland
Abstract
An attractive nonparametric method to detect change-points sequentially is to apply control charts based on kernel smoothers. Recently, the strong convergence of the associated normed delay associated with such a sequential stopping rule has been studied under sequences of out-of-control models. Kernel smoothers employ a kernel function to downweight past data. Since kernel functions with values in the unit interval are sufficient for that task, we study the problem to optimize the asymptotic normed delay over a class of kernels ensuring that restriction and certain additional moment constraints. We apply the key theorem to discuss several important examples where explicit solutions exist to illustrate that the results are applicable.
© Heldermann Verlag
Articles in the same Issue
- NP-Optimal Kernels for Nonparametric Sequential Detection Rules
- Availability Formulas and Performance Measures for Separable Degradable Networks
- Bayesian Predictions for Exponentially Distributed Failure Times With One Change-Point
- Bayes Inference Problems in Failure-Repair Processes
- Forecasting of Categorical Time Series Using a Regression Model
- Estimation of a Threshold-Value in the Context of Air Pollution and Health
- Estimation of the Jump-Point in a Hazard Function
- Distributions of the Estimated Process Capability Index Cpk
Articles in the same Issue
- NP-Optimal Kernels for Nonparametric Sequential Detection Rules
- Availability Formulas and Performance Measures for Separable Degradable Networks
- Bayesian Predictions for Exponentially Distributed Failure Times With One Change-Point
- Bayes Inference Problems in Failure-Repair Processes
- Forecasting of Categorical Time Series Using a Regression Model
- Estimation of a Threshold-Value in the Context of Air Pollution and Health
- Estimation of the Jump-Point in a Hazard Function
- Distributions of the Estimated Process Capability Index Cpk
