Empirical Likelihood for Dependent Data - Daniel Nordman,Soumendra N. Lahiri
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Empirical likelihood (EL) for time series and spatial data is an active area of research in statistics and econometrics. This book provides an accessible account of the empirical likelihood methodology and an up-to-date account of the latest advances on the topic. Applications for time series, econometrics, high-dimensional data and spatial data are covered as well as frequency domain empirical likelihood a ... Full description
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Description
Empirical likelihood (EL) for time series and spatial data is an active area of research in statistics and econometrics. This book provides an accessible account of the empirical likelihood methodology and an up-to-date account of the latest advances on the topic. Applications for time series, econometrics, high-dimensional data and spatial data are covered as well as frequency domain empirical likelihood and long-range dependence.
The book is intended for statisticians who want to learn about EL methodology and conduct research in this area. It is also useful for practitioners who want to apply the EL methodology in their work, but do not have the time to cover the literature scattered over various journal articles. In addition to statisticians, researchers working in economics, finance, electrical engineering (signal processing, communications), climatology and environmental sciences will find the book useful.
More Information
| Author | Daniel Nordman, Soumendra N. Lahiri |
|---|---|
| Publisher | Wiley |
| Series | Wiley Probability and Statisti |
| Release year | 2021 |
| Cover type | Hardcover |
| EAN | 9781119312765 |