In this paper we consider trend to be smooth deterministic changes over long scales, and tackle the problem of trend estimation in the presence of long memory errors (slowly decaying autocorrelations). Using the fractionally differenced (FD) process as a motivating example of such a long memory process, we demonstrate how the discrete wavelet transform (DWT) is a natural choice at extracting a polynomial trends from such an error process. We investigate the statistical properties of the trend estimate obtained from the DWT, and provide pointwise and simultaneous confidence intervals for the estimate. Based on evaluating the power in the trend estimate relative to the estimated errors, we provide a test of nonzero trend. We finish by applyin...
Various wavelet-based estimators of self-similarity or long-range dependence scaling exponent are st...
Trend identification is a substantial issue in hydrologic series analysis, but it is also a difficul...
We consider the problem of testing for homogeneity of variance in a time series with long memory str...
We consider the problem of estimating the parameters for a stochastic process using a time series co...
This thesis presents methods of testing the periodicity and trend for the time series, which exhibit...
Abstract — We consider the problem of estimating the param-eters for a stochastic process using a ti...
The objective of this dissertation is to study ways of modeling a long memory process using wavelet ...
This paper studies the estimation of time series regression when both regressors and disturbances ha...
Two wavelet based estimators are considered in this paper for the two parameters that characterize l...
Not AvailablePresence of long memory in climatic variables is frequently observed. The trend assessm...
International audienceIn this paper, we analyze the performance of five estimation methods for the l...
Risk of investing in a financial asset is quantified by functionals of squared returns. Discrete tim...
The first paper describes an alternative approach for testing the existence of trend among time seri...
We introduce the multiscale analysis of seasonal persistent processes, that is, time series models w...
Wavelets are a new class of basis functions that are finding wide use for analyzing and interpreting...
Various wavelet-based estimators of self-similarity or long-range dependence scaling exponent are st...
Trend identification is a substantial issue in hydrologic series analysis, but it is also a difficul...
We consider the problem of testing for homogeneity of variance in a time series with long memory str...
We consider the problem of estimating the parameters for a stochastic process using a time series co...
This thesis presents methods of testing the periodicity and trend for the time series, which exhibit...
Abstract — We consider the problem of estimating the param-eters for a stochastic process using a ti...
The objective of this dissertation is to study ways of modeling a long memory process using wavelet ...
This paper studies the estimation of time series regression when both regressors and disturbances ha...
Two wavelet based estimators are considered in this paper for the two parameters that characterize l...
Not AvailablePresence of long memory in climatic variables is frequently observed. The trend assessm...
International audienceIn this paper, we analyze the performance of five estimation methods for the l...
Risk of investing in a financial asset is quantified by functionals of squared returns. Discrete tim...
The first paper describes an alternative approach for testing the existence of trend among time seri...
We introduce the multiscale analysis of seasonal persistent processes, that is, time series models w...
Wavelets are a new class of basis functions that are finding wide use for analyzing and interpreting...
Various wavelet-based estimators of self-similarity or long-range dependence scaling exponent are st...
Trend identification is a substantial issue in hydrologic series analysis, but it is also a difficul...
We consider the problem of testing for homogeneity of variance in a time series with long memory str...