Advances in Time Series Methods and Applications

Wai Keung Li

ISBN 10: 1493965670 / ISBN 13: 9781493965670

Published by Springer New York Dez 2016, 2016

About this Item

This item is printed on demand - it takes 3-4 days longer - Neuware -This volume reviews and summarizes some of A. I. McLeod's significant contributions to time series analysis. It also contains original contributions to the field and to related areas by participants of the festschrift held in June 2014 and friends of Dr. McLeod. Covering a diverse range of state-of-the-art topics, this volume well balances applied and theoretical research across fourteen contributions by experts in the field. It willbe of interest to researchers and practitioners in time series, econometricians, and graduate students in time series or econometrics, as well as environmental statisticians, data scientists, statisticians interested in graphical models, and researchers in quantitative risk management. 304 pp. Englisch.

Seller Inventory # 9781493965670

Bibliographic Details

• Title: Advances in Time Series Methods and Applications
• Publisher: Springer New York Dez 2016
• Publication Date: 2016
• Language: English
• ISBN 10: 1493965670
• ISBN 13: 9781493965670
• Binding: Buch
• Condition: Neu
• Dimensions: 241W x 160H x 22D millimeters 241 millimeters width by 160 millimeters height by 22 millimeters depth
• Weight: 623 grams

About this title

Synopsis

This volume reviews and summarizes some of A. I. McLeod's significant contributions to time series analysis. It also contains original contributions to the field and to related areas by participants of the festschrift held in June 2014 and friends of Dr. McLeod. Covering a diverse range of state-of-the-art topics, this volume well balances applied and theoretical research across fourteen contributions by experts in the field. It will be of interest to researchers and practitioners in time series, econometricians, and graduate students in time series or econometrics, as well as environmental statisticians, data scientists, statisticians interested in graphical models, and researchers in quantitative risk management.