£108.78

Springer - Discretization of Processes: 67 (Stochastic Modelling)

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Description

In fields such as mathematical finance, random time-dependent events are frequently modeled as stochastic processes. To ensure these models are accurate, researchers must justify their structural assumptions through rigorous data analysis. This book, Discretization of Processes (Stochastic Modelling and Applied Probability, 67), provides the theoretical framework necessary for this task. Rather than focusing solely on estimating scalar parameters, this work establishes methods for estimating the underlying structure of a proposed model itself. By utilizing classic statistical tools such as the law of large numbers, the text offers a deep dive into the methodology required to bridge the gap between theoretical models and real-world data. It is an essential resource for statisticians and researchers who require a data-driven approach to stochastic modeling and applied probability.

Key Features

Provides a theoretical foundation for estimating the underlying structure of proposed models rather than just scalar parameters.

Addresses the needs of researchers in mathematical finance and other fields involving random time-dependent events.

Utilizes classic statistical tools, including the law of large numbers, to support model estimation.

Part of the Stochastic Modelling and Applied Probability series (Volume 67) from Springer.

Focuses on justifying model assumptions through the practical application of data.

Product Specifications

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

Barcode

No barcode data available

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