Add to ORKGThis article is concerned with the problem of discrimination between two classes of locally stationary time series based on minimum discrimination information. We view the observed signals as realizations of Gaussian locally stationary wavelet (LSW) processes. The asymptotic Kullback - Leibler discrimination information and Chernoff discrimination information are developed as discriminant criteria for LSW processes. The simulation study showed that our procedure performs as well as other procedures and in some cases better than some other classification methods. Applications to classifying real data show the usefulness of our discriminant criteria.Este artículo se refiere al problema de discriminación entre dos clases de series de tiempo es...
A series of observations indexed in time often exhibits patterns that may serve as bases for allocat...
This article defines and studies a new class of non-stationary random processes constructed from dis...
In this article a statistical procedure for identifying if a time series set follows the same model ...
A class of processes with a time varying spectral representationis introduced. A time varying spectr...
Statistical discrimination for nonstationary random processes is important in many applications. Our...
In this note we show that the locally stationary wavelet process can be decomposed into a sum of sig...
This thesis proposes novel methods for the modelling of multivariate time series. The work presented...
This thesis proposes novel methods for the modelling of multivariate time series. The work presented...
Statistical discrimination for nonstationary random processes have developed into a widely practiced...
AbstractIn this paper, we discuss discriminant analysis for locally stationary processes, which cons...
This article proposes a test to detect changes in general autocovariance structure in nonstationary ...
This article proposes a test to detect changes in general autocovariance structure in nonstationary ...
AbstractIn this paper, we discuss discriminant analysis for locally stationary processes, which cons...
In this paper we consider the problem of classifying non-stationary time series. The method that we ...
Methods for the supervised classification of signals generally aim to assign a signal to one class f...
A series of observations indexed in time often exhibits patterns that may serve as bases for allocat...
This article defines and studies a new class of non-stationary random processes constructed from dis...
In this article a statistical procedure for identifying if a time series set follows the same model ...
A class of processes with a time varying spectral representationis introduced. A time varying spectr...
Statistical discrimination for nonstationary random processes is important in many applications. Our...
In this note we show that the locally stationary wavelet process can be decomposed into a sum of sig...
This thesis proposes novel methods for the modelling of multivariate time series. The work presented...
This thesis proposes novel methods for the modelling of multivariate time series. The work presented...
Statistical discrimination for nonstationary random processes have developed into a widely practiced...
AbstractIn this paper, we discuss discriminant analysis for locally stationary processes, which cons...
This article proposes a test to detect changes in general autocovariance structure in nonstationary ...
This article proposes a test to detect changes in general autocovariance structure in nonstationary ...
AbstractIn this paper, we discuss discriminant analysis for locally stationary processes, which cons...
In this paper we consider the problem of classifying non-stationary time series. The method that we ...
Methods for the supervised classification of signals generally aim to assign a signal to one class f...
A series of observations indexed in time often exhibits patterns that may serve as bases for allocat...
This article defines and studies a new class of non-stationary random processes constructed from dis...
In this article a statistical procedure for identifying if a time series set follows the same model ...