A0282
Title: Multiple testing of local extrema for detecting change points under nonstationary Gaussian noise
Authors: Dan Cheng - Arizona State University (United States) [presenting]
Abstract: A new approach to detect change points based on differential smoothing and multiple testing is presented for data sequences modelled as piecewise constant functions plus nonstationary Gaussian noise. The method detects change points as significant local maxima and minima after smoothing and differentiating the observed sequence. The algorithm, combined with the Benjamini Hochberg procedure for thresholding p values, provides asymptotic strong control of the False Discovery Rate (FDR) and power consistency as the frequency of observations and the size of the jumps get large. Simulations show that FDR levels are maintained in non-asymptotic conditions and guide the choice of smoothing bandwidth.
