Signal detection using change point analysis in postmarket surveillance

Signal detection using change point analysis in postmarket surveillance†

PURPOSE Signal detection methods have been used extensively in postmarket surveillance to identify elevated risks of adverse events associated with medical products (drugs, vaccines, and devices). However, current popular disproportionality methods ignore useful information such as trends when the data are aggregated over time for signal detection. METHODS In this paper, we applied change poi...

Long signal change-point detection

The detection of change-points in a spatially or time ordered data sequence is an important problem in many fields such as genetics and finance. We derive the asymptotic distribution of a statistic recently suggested for detecting change-points. Simulation of its estimated limit distribution leads to a new and computationally efficient change-point detection algorithm, which can be used on very...

Real-time financial surveillance via quickest change-point detection methods

Change-Point Detection using Krylov Subspace Learning

We propose an efficient algorithm for principal component analysis (PCA) that is applicable when only the inner product with a given vector is needed. We show that Krylov subspace learning works well both in matrix compression and implicit calculation of the inner product by taking full advantage of the arbitrariness of the seed vector. We apply our algorithm to a PCA-based change-point detecti...

Signal detection Using Rational Function Curve Fitting

In this manuscript, we proposed a new scheme in communication signal detection which is respect to the curve shape of received signal and based on the extraction of curve fitting (CF) features. This feature extraction technique is proposed for signal data classification in receiver. The proposed scheme is based on curve fitting and approximation of rational fraction coefficients. For each symbo...