Electronic Resource

Robustness in statistical forecasting

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Traditional procedures in the statistical forecasting of time series, which are proved to be optimal under the hypothetical model, are often not robust under relatively small distortions (misspecification, outliers, missing values, etc.), leading to actual forecast risks (mean square errors of prediction) that are much higher than the theoretical values. This monograph fills a gap in the literature on robustness in statistical forecasting, offering solutions to the following topical problems:

- developing mathematical models and descriptions of typical distortions in applied forecasting problems;

- evaluating the robustness for traditional forecasting procedures under distortions;

- obtaining the maximal distortion levels that allow the “safe” use of the traditional forecasting algorithms;

- creating new robust forecasting procedures to arrive at risks that are less sensitive to definite distortion types.

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Ketersediaan

Informasi Detail

Judul Seri

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No. Panggil

E711

Penerbit

Swiss : Springer Cham., 2013

Deskripsi Fisik

xvi, 356 hlm.

Bahasa

English

ISBN/ISSN

978-3-319-34568-0

Klasifikasi

NONE

Tipe Isi

text

Tipe Media

computer

Tipe Pembawa

online resource

Edisi

Ed.1

Subjek

Metode dan Teori Statistik

Info Detail Spesifik

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Pernyataan Tanggungjawab

Yuriy Kharin

Versi lain/terkait

Lampiran Berkas

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