Bayesian Demographic Estimation and Forecasting

Name: Bayesian Demographic Estimation and Forecasting
Brand: Unknown
SKU: 9781498762625
Price: 247.0 NZD
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By John Bryant and Junni L. Zhang

Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

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Summary

Bayesian Demographic Estimation and Forecasting introduces three cutting-edge statistical frameworks designed for modern demographic estimation and forecasting. Drawing on recent advances in statistical methodology, it offers innovative tools to address challenges such as data disaggregation, measurement error, missing data, and the combination of multiple data sources. These methods are applicable to both single demographic series and entire demographic systems, unifying estimation and forecasting while providing detailed uncertainty measures.

The book requires minimal prior knowledge of statistics and no previous background in demography. Complementing the theoretical content, the authors provide a suite of R packages, with data and code accessible at www.bdef-book.com.
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Format: Hardback

$247⁰⁰

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Bayesian Demographic Estimation and Forecasting

$247⁰⁰

Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?

Bayesian Demographic Estimation and Forecasting is ideal for researchers, statisticians, and demographers seeking advanced yet accessible methods for demographic analysis and forecasting. It is also suitable for practitioners interested in applying Bayesian approaches using R, with minimal prior statistical or demographic expertise required.

Demographic estimation and forecasting is an important practical problem. Population projections are used to guide billions of dollars of expenditure on things such as roads, housing, shopping complexes, and hospitals. Policy evaluations require detailed estimates of populations at risk, or of demographic outcomes such as mortality.

Description

Bayesian Demographic Estimation and Forecasting presents three statistical frameworks for modern demographic estimation and forecasting. The frameworks draw on recent advances in statistical methodology to provide new tools for tackling challenges such as disaggregation, measurement error, missing data, and combining multiple data sources. The methods apply to single demographic series, or to entire demographic systems. The methods unify estimation and forecasting, and yield detailed measures of uncertainty.

The book assumes minimal knowledge of statistics, and no previous knowledge of demography. The authors have developed a set of R packages implementing the methods. Data and code for all applications in the book are available on www.bdef-book.com.

This book will be welcome for the scientific community of forecasters…as it presents a new approach which has already given important results and which, in my opinion, will increase its importance in the future. ~Daniel Courgeau, Institut national d'études démographiques

Book Hero Magic summarised reviews for this book. While it's new and still learning, it may not be perfect - your feedback is welcome! HOW HAS THIS BEEN REVIEWED?

“This book will be welcome for the scientific community of forecasters…as it presents a new approach which has already given important results and which, in my opinion, will increase its importance in the future.” — Daniel Courgeau, Institut national d'études démographiques

Book Details

INFORMATION

ISBN: 9781498762625

Publisher: Taylor & Francis Inc

Format: Hardback

Date Published: 03 July 2018

Country: United States

Imprint: Chapman & Hall/CRC

Audience: Professional and scholarly

DIMENSIONS

Width: 156.0mm

Height: 234.0mm

Weight: 584g

Pages: 280

Collections

Education & Reference John Bryant Junni L. Zhang Non-Fiction Science & Nature Series: Chapman & Hall/CRC Statistics in the Social and Behavioral Sciences

About the Author

John Bryant is a senior researcher at Statistics New Zealand. He has previously worked at the New Zealand Treasury, and at universities in New Zealand and Thailand. He has consulted for many international organizations, including UNICEF, the FAO, and the World Bank. His research interests include applied demography, data science, and Bayesian statistics.

Junni L. Zhang is an associate professor of statistics at Guanghua School of Management, Peking University. Her research interests include Bayesian statistics, text mining, and causal inference. She has extensive experience teaching undergraduate, graduate, MBA and executive courses, and is the author of Data Mining and Its Applications (in Chinese).