Name: Estimation of Stochastic Processes With Stationary Increments and Cointegrated Sequences (Mathematics and Statistics) (en Inglés)
SKU: 52823588
Price: 3253.43 MXN
Availability: InStock
Author: Maksym Luz; Mikhail Moklyachuk
ISBN: 9781786305039

portada Estimation of Stochastic Processes With Stationary Increments and Cointegrated Sequences (Mathematics and Statistics) (en Inglés)

Estimation of Stochastic Processes With Stationary Increments and Cointegrated Sequences (Mathematics and Statistics) (en Inglés)

Formato

Libro Físico

Editorial

Iste Ltd

Autor

Maksym Luz; Mikhail Moklyachuk

Año

2019

Idioma

Inglés

N° páginas

308

Encuadernación

Tapa Dura

ISBN13

9781786305039

N° edición

Estimation of Stochastic Processes With Stationary Increments and Cointegrated Sequences (Mathematics and Statistics) (en Inglés)

Maksym Luz; Mikhail Moklyachuk (Autor) · Iste Ltd · Tapa Dura

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Reseña del libro "Estimation of Stochastic Processes With Stationary Increments and Cointegrated Sequences (Mathematics and Statistics) (en Inglés)"

Estimation of Stochastic Processes is intended for researchers in the field of econometrics, financial mathematics, statistics or signal processing. This book gives a deep understanding of spectral theory and estimation techniques for stochastic processes with stationary increments. It focuses on the estimation of functionals of unobserved values for stochastic processes with stationary increments, including ARIMA processes, seasonal time series and a class of cointegrated sequences. Furthermore, this book presents solutions to extrapolation (forecast), interpolation (missed values estimation) and filtering (smoothing) problems based on observations with and without noise, in discrete and continuous time domains. Extending the classical approach applied when the spectral densities of the processes are known, the minimax method of estimation is developed for a case where the spectral information is incomplete and the relations that determine the least favorable spectral densities for the optimal estimations are found.