2016/01/15 by Aleksey S. Polunchenko, Grigory Sokolov · 11 citations
Decision Sciences · Mathematics · #Advanced Statistical Methods and Models #Advanced Statistical Process Monitoring #Applied mathematics #Brownian motion #Distribution (mathematics) #Markov chain #Mathematical analysis #Mathematics #Probability density function #Stationary distribution #Statistic #Statistical Distribution Estimation and Applications #Statistical physics #Statistics #math.ST #msc:60G10 #msc:60J60 #msc:62L10 #msc:62M15 #stat.AP #stat.ME #stat.TH
paper · pdf · doi:10.1007/s11009-016-9478-7
published in Methodology And Computing In Applied Probability 18(4), 1153-1195 (Springer Science+Business Media) · 45 pages; 8 figures; to appear in Methodology and Computing in Applied Probability
arxiv created 2016/01/15 · arxiv updated 2016/01/18 · openalex publication_date 2016/01/21 · openalex created_date 2021/02/01 · openalex updated_date 2026/08/05
We consider the quickest change-point detection problem where the aim is to detect the onset of a pre-specified drift in "live"-monitored standard Brownian motion; the change-point is assumed unknown (nonrandom). The topic of interest is the distribution of the Generalized Shryaev-Roberts (GSR) detection statistic set up to "sense" the presence of the drift. Specifically, we derive a closed-form formula for the transition probability density function (pdf) of the time-homogeneous Markov diffusion process generated by the GSR statistic when the Brownian motion under surveillance is "drift-free", i.e., in the pre-change regime; the GSR statistic's (deterministic) nonnegative headstart is assumed arbitrarily given. The transition pdf formula is found analytically, through direct solution of the respective Kolmogorov forward equation via the Fourier spectral method to achieve separation of the spacial and temporal variables. The obtained result generalizes the well-known formula for the (pre-change) stationary distribution of the GSR statistic: the latter's stationary distribution is the temporal limit of the distribution sought in this work. To conclude, we exploit the obtained formula numerically and briefly study the pre-change behavior of the GSR statistic versus three factors: (a) drift-shift magnitude, (b) time, and (c) the GSR statistic's headstart.