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ORTHONORMAL SERIES ESTIMATORS

Autores

Parámetros

  • 304 páginas
  • 11 horas de lectura

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Focusing on advanced statistical methods, this book explores the approximation and estimation of nonparametric functions through projections onto orthonormal function bases. It introduces series estimators that enhance density estimators for various complex models, including mixture, deconvolution, and semi-parametric models. The authors demonstrate optimal convergence rates in Hilbert spaces and analyze mean square errors relative to basis size, utilizing cross-validation for consistent estimation. Additionally, wavelet estimators are examined within these frameworks, providing a comprehensive approach to modern data analysis techniques.

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ORTHONORMAL SERIES ESTIMATORS, Odile Pons

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Publicado en
2020
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Título
ORTHONORMAL SERIES ESTIMATORS
Idioma
Inglés
Autores
Odile Pons
Publicado en
2020
Formato
Tapa dura
Páginas
304
ISBN13
9789811210686
Serie
Descripción
Focusing on advanced statistical methods, this book explores the approximation and estimation of nonparametric functions through projections onto orthonormal function bases. It introduces series estimators that enhance density estimators for various complex models, including mixture, deconvolution, and semi-parametric models. The authors demonstrate optimal convergence rates in Hilbert spaces and analyze mean square errors relative to basis size, utilizing cross-validation for consistent estimation. Additionally, wavelet estimators are examined within these frameworks, providing a comprehensive approach to modern data analysis techniques.