Our ltsa package implements the Durbin-Levinson and Trench algorithms and provides a general approach to the problems of fitting, forecasting and simulating linear time series models as well as fitting regression models with linear time series errors. For computational efficiency both algorithms are implemented in C and interfaced to R. Examples are given which illustrate the efficiency and accuracy of the algorithms. We provide a second package FGN which illustrates the use of the ltsa package with fractional Gaussian noise (FGN). It is hoped that the ltsa will provide a base for further time series software
Extracting and forecasting the volatility of financial markets is an important empirical problem. Ti...
Abstract—Maximum-likelihood (ML) theory presents an ele-gant asymptotic solution for the estimation ...
The objective of this thesis is to develop and refine statistical methods which can be used for solv...
Our ltsa package implements the Durbin-Levinson and Trench algorithms and provides a general approac...
In this paper we present a very brief description of least mean square algorithm with applications i...
In this work we introduce a new bootstrap approach based on a result of Ramsey (1974) and on the Dur...
This study explores the usage of linear programming (LP) as a tool to optimise the parameters of tim...
This thesis is concerned with various investigations relating to time series analysis and forecastin...
International audienceWe present two approaches for linear prediction of long-memory time series. Th...
SUMMARY There is an increasing awareness of the importance of long-memory models in statistical appl...
In practice, several time series exhibit long-range dependence or per-sistence in their observations...
In this work we investigate an alternative bootstrap approach based on a result of Ramsey (1974) and...
For long-memory time series, we show that the Toeplitz system §n(f)x = b can be solved in O(n log5=2...
This chapter reviews semiparametric methods of inference on different aspects of long memory time s...
We study the properties of Mallows ' CL criterion for selecting a fractional exponential �FEXP�...
Extracting and forecasting the volatility of financial markets is an important empirical problem. Ti...
Abstract—Maximum-likelihood (ML) theory presents an ele-gant asymptotic solution for the estimation ...
The objective of this thesis is to develop and refine statistical methods which can be used for solv...
Our ltsa package implements the Durbin-Levinson and Trench algorithms and provides a general approac...
In this paper we present a very brief description of least mean square algorithm with applications i...
In this work we introduce a new bootstrap approach based on a result of Ramsey (1974) and on the Dur...
This study explores the usage of linear programming (LP) as a tool to optimise the parameters of tim...
This thesis is concerned with various investigations relating to time series analysis and forecastin...
International audienceWe present two approaches for linear prediction of long-memory time series. Th...
SUMMARY There is an increasing awareness of the importance of long-memory models in statistical appl...
In practice, several time series exhibit long-range dependence or per-sistence in their observations...
In this work we investigate an alternative bootstrap approach based on a result of Ramsey (1974) and...
For long-memory time series, we show that the Toeplitz system §n(f)x = b can be solved in O(n log5=2...
This chapter reviews semiparametric methods of inference on different aspects of long memory time s...
We study the properties of Mallows ' CL criterion for selecting a fractional exponential �FEXP�...
Extracting and forecasting the volatility of financial markets is an important empirical problem. Ti...
Abstract—Maximum-likelihood (ML) theory presents an ele-gant asymptotic solution for the estimation ...
The objective of this thesis is to develop and refine statistical methods which can be used for solv...