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Springer Discretization of Processes: Stochastic Modelling Volume 67

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Description

This comprehensive academic text establishes rigorous theory for estimating parameters and underlying structures in stochastic processes, essential for researchers in mathematical finance and applied statistics. Using classic statistical tools including the law of large numbers and central limit theorem, the book addresses how to justify model assumptions through empirical data analysis. Ideal for statisticians and quantitative researchers who need to validate random time-dependent event models with real-world data, this volume provides the theoretical foundation for moving beyond assumptions to data-driven decision making in financial modelling and econometric applications.

Key Features

Part of the Springer Stochastic Modelling and Applied Probability series, volume 67, offering peer-reviewed academic rigour.

Covers estimation theory for both scalar parameters and underlying model structures in stochastic processes.

Applies classic statistical methods including the law of large numbers to validate financial and econometric models.

Designed for researchers in mathematical finance who need to justify process assumptions using empirical data analysis.

Product Specifications

Format
paperback
Domain
Amazon UK
Publication Date
28 November 2013
Listed Since
01 November 2013

Barcode

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