Results 201 to 210 of about 311,052 (319)

Cointegrating Polynomial Regressions With Power Law Trends

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT The common practice in cointegrating polynomial regressions (CPRs) often confines nonlinearities in the variable of interest to stochastic trends, thereby overlooking the possibility that they may be caused by deterministic components. As an extension, we propose univariate and multivariate CPRs that incorporate power law deterministic trends.
Yicong Lin, Hanno Reuvers
wiley   +1 more source

Change Point Analysis for Functional Data Using Empirical Characteristic Functionals

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT We develop a new method to detect change points in the distribution of functional data based on integrated CUSUM processes of empirical characteristic functionals. Asymptotic results are presented under conditions allowing for low‐order moments and serial dependence in the data establishing the limiting null‐distribution of the proposed test ...
Lajos Horváth   +2 more
wiley   +1 more source

Fusion 3-Categories for Duality Defects. [PDF]

open access: yesCommun Math Phys
Bhardwaj L   +3 more
europepmc   +1 more source

Tests for Changes in Count Time Series Models With Exogenous Covariates

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT We deal with a parametric change in models for count time series with exogenous covariates specified via the conditional distribution, i.e., with integer generalized autoregressive conditional heteroscedastic models with covariates (INGARCH‐X).
Šárka Hudecová, Marie Hušková
wiley   +1 more source

Tensor Changepoint Detection and Eigenbootstrap

open access: yesJournal of Time Series Analysis, EarlyView.
ABSTRACT Tensor data consisting of multivariate outcomes over the items and across the subjects with longitudinal and cross‐sectional dependence are considered. A completely distribution‐free and tweaking‐parameter‐free detection procedure for changepoints at different locations is designed, which does not require training data.
Michal Pešta   +2 more
wiley   +1 more source

Home - About - Disclaimer - Privacy