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The bootstrap for testing the equality of two multivariate time series with an application to financial markets
(Elsevier, 2022)
[Abstract]: The problem of testing the equality of the generating processes of two multivariate time series is addressed in this work. To this aim, we construct four tests based on a distance measure between stochastic ...
Hard and soft clustering of categorical time series based on two novel distances with an application to biological sequences
(Elsevier, 2023)
[Abstract]: Two novel distances between categorical time series are introduced. Both of them measure discrepancies between extracted features describing the underlying serial dependence patterns. One distance is based on ...
Recursive local polynomial regression under dependence conditions
(Springer, 2000)
In the case of the random design nonparametric regression, one recursive local polynomial smoother is considered. Expressions for the bias and the variance matrix of the estimators of the regression function and its ...
Bootstrap tests for nonparametric comparison of regression curves with dependent errors
(Springer, 2007)
In this paper, the problem of testing the equality of regression curves with dependent data is studied. Several methods based on nonparametric estimators of the regression function are described. In this setting, the ...
On the uniform strong consistency of local polynomial regression under dependence conditions
(Taylor & Francis, 2003)
[Abstract] In this paper, nonparametric estimators of the regression function, and its derivatives, obtained by means of weighted local polynomial fitting are studied. Consider the fixed regression model where the error ...
Outlier Detection for Multivariate Time Series: A Functional Data Approach ®
(Elsevier, 2021)
[Abstract] A method for detecting outlier samples in a multivariate time series dataset is proposed. It is assumed that an outlying series is characterized by having been generated from a different process than those ...
Quantile Cross-Spectral Density: A Novel and Effective Tool for Clustering Multivariate Time Series
(Elsevier, 2021)
[Abstract] Clustering of multivariate time series is a central problem in data mining with applications in many fields. Frequently, the clustering target is to identify groups of series generated by the same multivariate ...
Robust Methods for Soft Clustering of Multidimensional Time Series
(MDPI, 2021)
[Abstract] Three robust algorithms for clustering multidimensional time series from the perspective of underlying processes are proposed. The methods are robust extensions of a fuzzy C-means model based on estimates of the ...
F4: An All-Purpose Tool for Multivariate Time Series Classification
(MDPI, 2021)
[Abstract] We propose Fast Forest of Flexible Features (F4), a novel approach for classifying multivariate time series, which is aimed to discriminate between underlying generating processes. This goal has barely been ...
The Bootstrap for Testing the Equality of Two Multivariate Stochastic Processes with an Application to Financial Markets
(MDPI, 2022)
[Abstract] The problem of testing the equality of generating processes of two multivariate time series is addressed in this work. To this end, we construct two tests based on a distance measure between stochastic processes. ...