Results 181 to 190 of about 776,491 (337)
Detecting Relevant Deviations From the White Noise Assumption for Non‐Stationary Time Series
Journal of Time Series Analysis, EarlyView.ABSTRACT We consider the problem of detecting deviations from a white noise assumption in time series. Our approach differs from the numerous methods proposed for this purpose with respect to two aspects. First, we allow for non‐stationary time series. Second, we address the problem that a white noise test is usually not performed because one believes ...
Patrick Bastian
wiley +1 more source
On general bootstrap of empirical estimator of a semi-Markov kernel with applications
Journal of Multivariate Analysis, 2013 Salim Bouzebda, N. Limnios
semanticscholar +1 more source
Sharp edge, vertex, and mixed Cheeger type inequalities for finite Markov kernels [PDF]
, 2007 Ravi Montenegro
openalex +1 more source
Empirical‐Process Limit Theory and Filter Approximation Bounds for Score‐Driven Time Series Models
Journal of Time Series Analysis, EarlyView.ABSTRACT This article examines the filtering and approximation‐theoretic properties of score‐driven time series models. Under specific Lipschitz‐type and tail conditions, new results are derived, leading to maximal and deviation inequalities for the filtering approximation error using empirical process theory.
Enzo D'Innocenzo
wiley +1 more source
Measure‐valued processes for energy markets
Mathematical Finance, Volume 35, Issue 2, Page 520-566, April 2025.Abstract We introduce a framework that allows to employ (non‐negative) measure‐valued processes for energy market modeling, in particular for electricity and gas futures. Interpreting the process' spatial structure as time to maturity, we show how the Heath–Jarrow–Morton approach can be translated to this framework, thus guaranteeing arbitrage free ...
Christa Cuchiero +3 more
wiley +1 more source
On goodness‐of‐fit testing for self‐exciting point processes
Scandinavian Journal of Statistics, EarlyView.Abstract Despite the wide usage of parametric point processes in theory and applications, a sound goodness‐of‐fit procedure to test whether a given parametric model is appropriate for data coming from a self‐exciting point process has been missing in the literature.
José Carlos Fontanesi Kling +1 more
wiley +1 more source
Designing Simple and Efficient Markov Chain Monte Carlo Proposal Kernels
, 2017 Yuttapong Thawornwattana +2 more
openalex +1 more source
An Overview of Deep Learning Techniques for Big Data IoT Applications
International Journal of Communication Systems, Volume 39, Issue 4, 10 March 2026.Reviews deep learning integration with cloud, fog, and edge computing in IoT architectures. Examines model suitability across IoT applications, key challenges, and emerging trends Provides a comparative analysis to guide future deep learning research in IoT environments.
Gagandeep Kaur +2 more
wiley +1 more source
Quantum speedup for nonreversible Markov chains. [PDF]
Nat CommunClaudon B, Piquemal JP, Monmarché P.
europepmc +1 more source