Results 161 to 170 of about 4,792,820 (293)

Biologically-Inspired Spike-Based Automatic Speech Recognition of Isolated Digits Over a Reproducing Kernel Hilbert Space. [PDF]

Front Neurosci, 2018
Li K, Príncipe JC.
europepmc   +1 more source

Change Point Analysis for Functional Data Using Empirical Characteristic Functionals

Journal 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, Gregory Rice, Jeremy VanderDoes   +2 more
wiley   +1 more source

Functional Sieve Bootstrap for the Partial Sum Process With an Application to Change‐Point Detection

Journal of Time Series Analysis, EarlyView.
ABSTRACT This article applies the functional sieve bootstrap (FSB) to estimate the distribution of the partial sum process for time series stemming from a weakly stationary functional process. Consistency of the FSB procedure under weak assumptions on the underlying functional process is established.
Efstathios Paparoditis, Lea Wegner, Martin Wendler   +2 more
wiley   +1 more source

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

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

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

On the Existence of One‐Sided Representations for the Generalised Dynamic Factor Model

Journal of Time Series Analysis, EarlyView.
ABSTRACT We study the Generalised Dynamic Factor Model (GDFM) and show that the dynamic common component, that is, the common component of the GDFM, can be expressed using only current and past observations under mild assumptions. Specifically, we require (i) the dynamic common component to be purely non‐deterministic and (ii) the exclusion of ...
Philipp Gersing
wiley   +1 more source