Empirical likelihood for change point detection in autoregressive models
نویسندگان
چکیده
منابع مشابه
Bootstrap procedures for sequential change point analysis in autoregressive models
We compare numerically the behavior of several bootstrap procedures for monitoring changes in the error distribution of autoregressive time series. The proposed procedures include classical approaches based on the empirical distribution function as well as Fourier-type methods which utilize the empirical characteristic function, both functions being computed on the basis of properly estimated r...
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 کاملEmpirical likelihood test in a posteriori change-point nonlinear model
In this paper, in order to test whether changes have occurred in a nonlinear parametric regression, we propose a nonparametric method based on the empirical likelihood. Firstly, we test the null hypothesis of no-change against the alternative of one change in the regression parameters. The asymptotic behaviour of the empirical likelihood statistic under the null hypothesis and its alternative i...
متن کاملDynamic Frailty and Change Point Models for Recurrent Events Data
Abstract. We present a Bayesian analysis for recurrent events data using a nonhomogeneous mixed Poisson point process with a dynamic subject-specific frailty function and a dynamic baseline intensity func- tion. The dynamic subject-specific frailty employs a dynamic piecewise constant function with a known pre-specified grid and the baseline in- tensity uses an unknown grid for the piecewise ...
متن کاملModified Maximum Likelihood Estimation in First-Order Autoregressive Moving Average Models with some Non-Normal Residuals
When modeling time series data using autoregressive-moving average processes, it is a common practice to presume that the residuals are normally distributed. However, sometimes we encounter non-normal residuals and asymmetry of data marginal distribution. Despite widespread use of pure autoregressive processes for modeling non-normal time series, the autoregressive-moving average models have le...
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: Journal of the Korean Statistical Society
سال: 2020
ISSN: 1226-3192,2005-2863
DOI: 10.1007/s42952-020-00061-w