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Springer Inference for Functional Data with Applications - Springer Statistics

Amazon

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Description

This comprehensive statistical reference presents modern methods for functional data analysis applied across geosciences, finance, economics and biology. The book emphasises inference based on second order statistics, with particular focus on functional principal component analysis techniques. It covers both independent and identically distributed functional data alongside dependent functional data structures including functional time series and spatially indexed functions. Designed for researchers and practitioners working with complex data patterns, this work provides the theoretical foundations and practical applications necessary for advanced statistical inference. Part of the Springer Series in Statistics collection, it serves as an essential resource for understanding how functional data analysis tools address real-world problems in quantitative disciplines.

Key Features

Covers functional principal component analysis methods essential for modern statistical inference on complex data structures.

Addresses dependent functional data structures including functional time series and spatially indexed functions beyond standard independent cases.

Presents applications across geosciences, finance, economics and biology demonstrating practical relevance of functional data methods.

Provides theoretical foundations based on second order statistics required for rigorous functional data analysis inference.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
09 May 2012
Listed Since
10 February 2012

Barcode

No barcode data available

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