abstract: the investigation of spectral densities has become an indispensible part of time series analysis which in turn is one of the most common tools of quantitative social science. this paper concentrates on the highlights of current research inspectral estimation and does not intend to give a full survey of the known methods. following an overview of foundations, the second part presents two methods of parametric inference, namely recursive and robust estimation. the third section deals with nonparametric procedures, in particular with some weighted covariance estimators; a new lag window is introduced which is optimal in the sense of mise. the algorithms described in this paper have been implemented by the authors at the institute of ...
A new time-varying autoregressive modeling has been proposed as a tool for time-frequency analysis o...
When we analyze a stationary time series, one of the questions we often meet is how to estimate its ...
When we analyze a stationary time series, one of the questions we often meet is how to estimate its ...
Spectral Analysis is one of the most important methods in signal processing. In practical applicatio...
This thesis is concerned with nonparametric techniques for inferring properties of time series. Firs...
This thesis is concerned with nonparametric techniques for inferring properties of time series. Firs...
Spectral density matrices provide a complete summary of the second order dynamics of a multivariate ...
We review different approaches to nonparametric density and regression estimation. Kernel estimators...
This thesis presents two main approaches to estimating the spectral density of a stationary time ser...
Abstract. Gaussian time-series models are often specified through their spec-tral density. Such mode...
International audienceGaussian time-series models are often specified through their spectral density...
International audienceGaussian time-series models are often specified through their spectral density...
International audienceGaussian time-series models are often specified through their spectral density...
International audienceGaussian time-series models are often specified through their spectral density...
Gaussian time-series models are often specified through their spectral density. Such models present ...
A new time-varying autoregressive modeling has been proposed as a tool for time-frequency analysis o...
When we analyze a stationary time series, one of the questions we often meet is how to estimate its ...
When we analyze a stationary time series, one of the questions we often meet is how to estimate its ...
Spectral Analysis is one of the most important methods in signal processing. In practical applicatio...
This thesis is concerned with nonparametric techniques for inferring properties of time series. Firs...
This thesis is concerned with nonparametric techniques for inferring properties of time series. Firs...
Spectral density matrices provide a complete summary of the second order dynamics of a multivariate ...
We review different approaches to nonparametric density and regression estimation. Kernel estimators...
This thesis presents two main approaches to estimating the spectral density of a stationary time ser...
Abstract. Gaussian time-series models are often specified through their spec-tral density. Such mode...
International audienceGaussian time-series models are often specified through their spectral density...
International audienceGaussian time-series models are often specified through their spectral density...
International audienceGaussian time-series models are often specified through their spectral density...
International audienceGaussian time-series models are often specified through their spectral density...
Gaussian time-series models are often specified through their spectral density. Such models present ...
A new time-varying autoregressive modeling has been proposed as a tool for time-frequency analysis o...
When we analyze a stationary time series, one of the questions we often meet is how to estimate its ...
When we analyze a stationary time series, one of the questions we often meet is how to estimate its ...