Consistent nonparametric change point detection combining CUSUM and marked empirical processes
نویسندگان
چکیده
منابع مشابه
BaSTA: consistent multiscale multiple change-point detection for ARCH processes
The emergence of the recent financial crisis, during which markets frequently underwent changes in their statistical structure over a short period of time, illustrates the importance of non-stationary modelling in financial time series. Motivated by this observation, we propose a fast, well-performing and theoretically tractable method for detecting multiple change-points in the structure of an...
متن کاملEmpirical Bayesian Change Point Detection
This paper explores a Bayesian method for the detection of sudden changes in the generative parameters of a data series. The problem is phrased as a hidden Markov model, where change point locations correspond to unobserved states, which grow in number with the number of observations. Our interest lies in the marginal change point posterior density. Rather than optimize a likelihood function of...
متن کاملStable Marked Point Processes
In many contexts, such as queueing theory, spatial statistics, geostatistics and meteorology, data are observed at irregular spatial positions. One model of this situation is to consider the observation points as generated by a Poisson Process. Under this assumption, we study the limit behavior of the partial sums of the Marked Point Process {(ti, X(ti))}, where X(t) is a stationary random fiel...
متن کاملRescaling Marked Point Processes
In 1971, Meyer showed how one could use the compensator to rescale a multivariate point process, forming independent Poisson processes with intensity one. Meyer’s result has been generalized to multi-dimensional point processes. Here, we explore generalization of Meyer’s theorem to the case of marked point processes, where the mark space may be quite general. Assuming simplicity and the existen...
متن کاملChange Detection with Kalman Filter and CUSUM
Knowledge discovery systems are constrained by three main limited resources: time, memory and sample size. Sample size is traditionally the dominant limitation, but in many present-day data-mining applications the time and memory are the major limitations [6]. Several incremental learning algorithms have been proposed to deal with this limitations (e.g., [5, 12, 6]). However most learning algor...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Electronic Journal of Statistics
سال: 2020
ISSN: 1935-7524
DOI: 10.1214/20-ejs1715