Laplace Transform-Based Nonparametric Test of Exponentiality against DMRL class with preservation under the Homogeneous Poisson Shock Model and applications in survival analysis and reliability. [PDF]
El-Atfy ES +5 more
europepmc +1 more source
ABSTRACT Traditional graph representations are insufficient for modelling real‐world phenomena involving multi‐entity interactions, such as collaborative projects or protein complexes, necessitating the use of hypergraphs. While hypergraphs preserve the intrinsic nature of such complex relationships, existing models often overlook temporal evolution in
Xianghe Zhu, Qiwei Yao
wiley +1 more source
Re-weighted estimation of the transition probability density for second-order diffusion processes. [PDF]
Li Y, Wang Y, Tang M.
europepmc +1 more source
Testing for Rough Volatility When Prices Are Purely Discontinuous
ABSTRACT We consider the problem of nonparametric testing for rough volatility, using high‐frequency data with a fixed time span, in a setting where the price is purely discontinuous. More specifically, we analyze the asymptotic properties of a test we developed in previous work in a pure‐jump setting.
Carsten H. Chong, Viktor Todorov
wiley +1 more source
Maximal Dissipation and Well-Posedness of the Euler System of Gas Dynamics. [PDF]
Feireisl E +2 more
europepmc +1 more source
Penalized Convex Estimation in Dynamic Location Models
ABSTRACT This paper studies L1$$ {L}^1 $$‐penalized estimation for location models yt=mt+ϵt$$ {y}_t={m}_t+{\epsilon}_t $$, where mt$$ {m}_t $$ is defined by a possibly non‐Markovian recursion and ϵt$$ {\epsilon}_t $$ is a martingale difference sequence with possibly time‐varying conditional variance.
Reda Alami Chentoufi
wiley +1 more source
On the mixed-model analysis of covariance in cluster-randomized trials. [PDF]
Wang B +5 more
europepmc +1 more source
Moving Aggregate Modified Autoregressive Copula‐Based Time Series Models (MAGMAR‐Copulas)
ABSTRACT Copula‐based time series models can model univariate and stationary time series in a flexible way by decomposing the joint distribution of consecutive observations into a copula and the stationary distribution. Implicitly, this approach assumes a finite Markov order. In reality, a time series may not follow the Markov property.
Sven Pappert
wiley +1 more source
Design of Bayesian Clinical Trials With Clustered Data. [PDF]
Hagar L, Golchi S.
europepmc +1 more source
Sparse Causal Dynamic Linear Regression
ABSTRACT We develop a sparse causal dynamic regression framework for long multivariate time series. With very long time series, the potentially large number of lags and leads in a dynamic regression model often makes time‐domain estimation numerically unstable or intractable.
Rui Huang, Kung‐Sik Chan
wiley +1 more source

