Browsing by Subject "Time series"
Now showing items 120 of 35

Article
AAA Note on the Behaviour of Nonparametric Density and Spectral Density Estimators at Zero Points of their Support
(2015)The asymptotic behaviour of nonparametric estimators of the stationary density and of the spectral density function of a stationary process have been studied in some detail in the last 5060years. Nevertheless, less is ...

Article
Adaptive bandwidth choice
(2003)In this paper, we consider the problem of bandwidth choice in the parallel settings of nonparametric kernel smoothed spectra] density and probability density estimation. We propose a new class of 'plugin' type bandwidth ...

Article
Artificial neural networks for earthquake prediction using time series magnitude data or Seismic Electric Signals
(2011)The aim of this study is to evaluate the performance of artificial neural networks in predicting earthquakes occurring in the region of Greece with the use of different types of input data. More specifically, two different ...

Article
Automatic BlockLength Selection for the Dependent Bootstrap
(2004)We review the different block bootstrap methods for time series, and present them in a unified framework. We then revisit a recent result of Lahiri [Lahiri, S. N. (1999b). Theoretical comparisons of block bootstrap methods, ...

Article
Baxter’s inequality for triangular arrays
(2015)A central problem in time series analysis is prediction of a future observation. The theory of optimal linear prediction has been well understood since the seminal work of A. Kolmogorov and N. Wiener during World War II. ...

Article
Biological applications of time series frequency domain clustering
(2012)Clustering methods are used routinely to form groups of objects with similar characteristics. Collections of time series datasets appear in several biological applications. Some of these applications require grouping the ...

Article
Bootstrap methods for dependent data: A review
(2011)This paper gives a review on a variety of bootstrap methods for dependent data. The main focus is not on an exhaustive listing and description of bootstrap procedures but on general principles which should be taken into ...

Article
Bootstrap prediction intervals for linear, nonlinear and nonparametric autoregressions
(2016)In order to construct prediction intervals without the cumbersomeand typically unjustifiableassumption of Gaussianity, some form of resampling is necessary. The regression setup has been wellstudied in the literature ...

Article
Bootstrap prediction intervals for Markov processes
(2014)Given time series data X1,…,Xn, the problem of optimal prediction of Xn+1 has been wellstudied. The same is not true, however, as regards the problem of constructing a prediction interval with prespecified coverage ...

Article
Bootstrap technology and applications
(1992)Bootstrap resampling methods have emerged as powerful tools for constructing inferential procedures in modern statistical data analysis. Although these methods depend on the availability of fast, inexpensive computing, ...

Conference Object
Cointegration and nonstationarity in the context of multiresolution analysis
(Institute of Physics Publishing, 2011)Cointegration has established itself as a powerful means of projecting out longterm trends from timeseries data in the context of econometrics. Recent work by the current authors has further established that cointegration ...

Article
Consistent Testing for Pairwise Dependence in Time Series
(2017)We consider the problem of testing pairwise dependence for stationary time series. For this, we suggest the use of a Box–Ljungtype test statistic that is formed after calculating the distance covariance function among ...

Article
Distribution theory for the studentized mean for long, short, and negative memory time series
(2013)We consider the problem of estimating the variance of the partial sums of a stationary time series that has either long memory, short memory, negative/intermediate memory, or is the firstdifference of such a process. The ...

Article
Financial time series
(2009)The evolution of financial markets is a complicated realworld phenomenon that ranks at the top in terms of difficulty of modeling and/or prediction. One reason for this difficulty is the welldocumented nonlinearity that ...

Article
A finetuned estimator of a general convergence rate
(2008)Summary A general rate estimation method based on the insample evolution of appropriately chosen diverging/converging statistics has recently been proposed by D.N. Politis [C. R. Acad. Sci. Paris, Ser. I, vol. 335, pp. ...

Conference Object
Frequencydomain characterization of Singular Spectrum Analysis eigenvectors
(Institute of Electrical and Electronics Engineers Inc., 2017)Singular Spectrum Analysis (SSA) is a nonparametric approach used to decompose a time series into meaningful components, related to trends, oscillations and noise. SSA can be seen as a spectral decomposition, where each ...

Article
Highdimensional autocovariance matrices and optimal linear prediction
(2015)A new methodology for optimal linear prediction of a stationary time series is introduced. Given a sample X1,…,Xn, the optimal linear predictor of Xn+1 is Xn+1 = Φ1(n)Xn + Φ2(n)Xn−1 + + Φn(n)X1. In practice, the coefficient ...

Article
Higherorder accurate polyspectral estimation with flattop lagwindows
(2009)Improved performance in higherorder spectral density estimation is achieved using a general class of infiniteorder kernels. These estimates are asymptotically less biased but with the same order of variance as compared ...

Article
An improved divergence information criterion for the determination of the order of an ar process
(2010)In this article we propose a modification of the recently introduced divergence information criterion (DIC, Mattheou et al., 2009) for the determination of the order of an autoregressive process and show that it is an ...

Article
Integervalued time series
(2009)Integervalued time series data appear in several diverse applications. However, modeling and inference for these types of dependent data pose several questions and interesting problems. The method of generalized linear ...