Results 191 to 200 of about 4,790,611 (321)

Adaptive Estimation for Weakly Dependent Functional Times Series

Journal of Time Series Analysis, EarlyView.
ABSTRACT We propose adaptive mean and autocovariance function estimators for stationary functional time series under 𝕃p−m‐approximability assumptions. These estimators are designed to adapt to the regularity of the curves and to accommodate both sparse and dense data designs.
Hassan Maissoro, Valentin Patilea, Myriam Vimond   +2 more
wiley   +1 more source

Control problems for semilinear neutral differential equations in Hilbert spaces. [PDF]

ScientificWorldJournal, 2014
Jeong JM, Cho SH.
europepmc   +1 more source

Duals of a frame in quaternionic Hilbert spaces [PDF]

, 2018
Sidharth Sharma, Ghanshyam Singh, Soniya Sahu   +2 more
openalex   +1 more source

A New Approach to Statistical Inference for Functional Time Series

Journal of Time Series Analysis, EarlyView.
ABSTRACT The analysis of time‐indexed functional data plays an important role in the field of business and economic statistics. In the literature, statistical inference for functional time series often involves reducing the dimension of functional data to a finite dimension K$$ K $$, followed by the use of tools from multivariate analysis.
Hanjia Gao, Yi Zhang, Xiaofeng Shao
wiley   +1 more source

Nonparametric Inference of Conditional Expectile Functions in Large‐Scale Time Series Data With Improved Efficiency

Journal of Time Series Analysis, EarlyView.
ABSTRACT Expectile is a coherent and elicitable law‐invariant risk measure widely applied in risk management. Existing methods based on iteratively reweighted least squares (IWLS) are not computationally efficient for large‐scale sample sizes. To overcome the issue, we develop a direct nonparametric conditional expectile function estimator by inverting
Feipeng Zhang, Ping‐Shou Zhong
wiley   +1 more source

Density‐Valued ARMA Models by Spline Mixtures

Journal of Time Series Analysis, EarlyView.
ABSTRACT This paper proposes a novel framework for modeling time series of probability density functions by extending autoregressive moving average (ARMA) models to density‐valued 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 B‐spline ...
Yasumasa Matsuda, Rei Iwafuchi
wiley   +1 more source

Exploring novel semi-inner product reproducing Kernels in Banach space for robust Kernel methods. [PDF]

PLoS One
Ding Y, Zhao Y, Pei Y.
europepmc   +1 more source

Comparing Robust Versions of Distance Covariance: A Comment on the Biloop Approach

International Statistical Review, EarlyView.
Dominic Edelmann
wiley   +1 more source

A Note on Local Polynomial Regression for Time Series in Banach Spaces

Journal of Time Series Analysis, EarlyView.
ABSTRACT This work extends local polynomial regression to Banach space‐valued time series for estimating smoothly varying means and their derivatives in non‐stationary data. The asymptotic properties of both the standard and bias‐reduced Jackknife estimators are analyzed under mild moment conditions, establishing their convergence rates.
Florian Heinrichs
wiley   +1 more source

Cyclicity in Reproducing Kernel Hilbert Spaces of Analytic Functions

, 2014
Emmanuel Fricain, Javad Mashreghi, Daniel Seco   +2 more
openalex   +2 more sources