In statistical data analysis it is often important to compare, classify, and cluster di¤erent time series. For these purposes various methods have been proposed in the literature, but they usually assume time series with the same sample size. In this paper, we propose a spectral domain method for han-dling time series of unequal length. The method make the spectral estimates comparable by producing statistics at the same frequency. The procedure is compared with other methods proposed in the literature by a Monte Carlo simulation study. As an illustrative example, the proposed spectral method is applied to cluster industrial production series of some developed countries
Abstract. We propose a general bootstrap procedure to approximate the null distri-bution of nonparam...
Comparing several groups of populations based on replicated data is one of the main concerns in stat...
It has been shown that the chosen numerical integration method corresponds to a realistic view of da...
In statistical data analysis it is often important to compare, classify, and cluster di¤erent time s...
In statistical data analysis it is often important to compare, classify, and cluster different time ...
The comparison and classification of time series is an important issue in practical time series anal...
The comparison and classification of time series is an important issue in practical time series anal...
Motivated by a recent paper of Caiado et al. (2009), we investigate testing problems for spectral de...
In the case of time-series analysis, it is often more convenient to rely on the frequency domain tha...
Data mining tools are generally used to extract useful information from large databases. Although th...
Data mining tools are generally used to extract useful information from large databases. Although th...
Data mining tools are generally used to extract useful information from large databases. Although th...
Empirical thesis.Bibliography: pages 95-97.1. Introduction -- 2. Literature review -- 3. Nonparametr...
Abstract. We propose a general bootstrap procedure to approximate the null distri-bution of nonparam...
We consider band-limited frequency-domain goodness-of-fit testing for stationary time series, withou...
Abstract. We propose a general bootstrap procedure to approximate the null distri-bution of nonparam...
Comparing several groups of populations based on replicated data is one of the main concerns in stat...
It has been shown that the chosen numerical integration method corresponds to a realistic view of da...
In statistical data analysis it is often important to compare, classify, and cluster di¤erent time s...
In statistical data analysis it is often important to compare, classify, and cluster different time ...
The comparison and classification of time series is an important issue in practical time series anal...
The comparison and classification of time series is an important issue in practical time series anal...
Motivated by a recent paper of Caiado et al. (2009), we investigate testing problems for spectral de...
In the case of time-series analysis, it is often more convenient to rely on the frequency domain tha...
Data mining tools are generally used to extract useful information from large databases. Although th...
Data mining tools are generally used to extract useful information from large databases. Although th...
Data mining tools are generally used to extract useful information from large databases. Although th...
Empirical thesis.Bibliography: pages 95-97.1. Introduction -- 2. Literature review -- 3. Nonparametr...
Abstract. We propose a general bootstrap procedure to approximate the null distri-bution of nonparam...
We consider band-limited frequency-domain goodness-of-fit testing for stationary time series, withou...
Abstract. We propose a general bootstrap procedure to approximate the null distri-bution of nonparam...
Comparing several groups of populations based on replicated data is one of the main concerns in stat...
It has been shown that the chosen numerical integration method corresponds to a realistic view of da...