Results 121 to 130 of about 8,616 (247)
Adaptive Estimation for Weakly Dependent Functional Times Series
ABSTRACT We propose adaptive mean and autocovariance function estimators for stationary functional time series under đpâmâapproximability assumptions. These estimators are designed to adapt to the regularity of the curves and to accommodate both sparse and dense data designs.
Hassan Maissoro +2 more
wiley +1 more source
ABSTRACT We propose a new formulation of the VaĹĄiÄekmodel within the framework of functional data analysis. We treat observations (continuousâtime rates) within a suitably defined trading day as a single statistical object. We then consider a sequence of such objects, indexed by day.
Piotr Kokoszka +4 more
wiley +1 more source
DensityâValued ARMA Models by Spline Mixtures
ABSTRACT This paper proposes a novel framework for modeling time series of probability density functions by extending autoregressive moving average (ARMA) models to densityâvalued data. The method is based on a transformation approach, wherein each density function on a compact domain [0,1]d$$ {\left[0,1\right]}^d $$ is approximated by a Bâspline ...
Yasumasa Matsuda, Rei Iwafuchi
wiley +1 more source
Robust CDFâFiltering of a Location Parameter
ABSTRACT This paper introduces a novel framework for designing robust filters associated with signal plus noise models having symmetric observation density. The filters are obtained by a recursion where the innovation term is a transform of the cumulative distribution function of the residuals.
Leopoldo Catania +2 more
wiley +1 more source
A Note on Local Polynomial Regression for Time Series in Banach Spaces
ABSTRACT This work extends local polynomial regression to Banach spaceâvalued time series for estimating smoothly varying means and their derivatives in nonâstationary data. The asymptotic properties of both the standard and biasâreduced Jackknife estimators are analyzed under mild moment conditions, establishing their convergence rates.
Florian Heinrichs
wiley +1 more source
Sequential Outlier Detection in Nonstationary Time Series
ABSTRACT A novel method for sequential outlier detection in nonstationary time series is proposed. The method tests the null hypothesis of âno outlierâ at each time point, addressing the multiple testing problem by bounding the error probability of successive tests, using extremeâvalue theory. The asymptotic properties of the test statistic are studied
Florian Heinrichs +2 more
wiley +1 more source
ABSTRACT This article examines the filtering and approximationâtheoretic properties of scoreâdriven time series models. Under specific Lipschitzâtype 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
Testing Distributional Granger Causality With Entropic Optimal Transport
ABSTRACT We develop a novel nonparametric test for Granger causality in distribution based on entropic optimal transport. Unlike classical meanâbased approaches, the proposed method directly compares the full conditional distributions of a response variable with and without the history of a candidate predictor.
Tao Wang
wiley +1 more source
In this article, we consider the exterior problem for the nonlinear degenerate parabolic equation $$ u_t - \Delta b(u) + \nabla \cdot \Phi(u) = F(u), \quad (t,x) \in (0,T) \times \Omega, $$ $\Omega$ is the exterior domain of $\Omega_0$ (a closed ...
Li Zhang, Ning Su
doaj
MarchenkoâPastur Laws for Daniell Smoothed Periodograms
ABSTRACT Given a sample X0,âŚ,Xnâ1$$ {X}_0,\dots, {X}_{n-1} $$ from a d$$ d $$âdimensional stationary time series (Xt)tââ¤$$ {\left({X}_t\right)}_{t\in \mathbb{Z}} $$, the most commonly used estimator for the spectral density matrix F(θ)$$ F\left(\theta \right) $$ at a given frequency θâ[0,2Ď)$$ \theta \in \left[0,2\pi \right) $$ is the Daniell smoothed ...
Ben Deitmar
wiley +1 more source

