ORCID Profile
0000-0002-2563-5040
Current Organisation
University of Southampton
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Publisher: Royal Society of Chemistry (RSC)
Date: 2015
DOI: 10.1039/C4CP03711A
Abstract: An atomistic study of Ge-core–Si-shell nanocrystals gives a detailed picture of how strain and confinement effect the electronic and optical properties.
Publisher: Duke University Press
Date: 12-05-2015
DOI: 10.1007/S13524-015-0389-Y
Abstract: In this article, we develop a fully integrated and dynamic Bayesian approach to forecast populations by age and sex. The approach embeds the Lee-Carter type models for forecasting the age patterns, with associated measures of uncertainty, of fertility, mortality, immigration, and emigration within a cohort projection model. The methodology may be adapted to handle different data types and sources of information. To illustrate, we analyze time series data for the United Kingdom and forecast the components of population change to the year 2024. We also compare the results obtained from different forecast models for age-specific fertility, mortality, and migration. In doing so, we demonstrate the flexibility and advantages of adopting the Bayesian approach for population forecasting and highlight areas where this work could be extended.
Publisher: Elsevier BV
Date: 07-2016
Publisher: Max Planck Institute for Demographic Research
Date: 10-12-2013
Publisher: Wiley
Date: 23-04-2014
DOI: 10.1002/PSP.1856
Publisher: Walter de Gruyter GmbH
Date: 12-2013
Abstract: In this article, we first discuss the need to augment reported flows of international migration in Europe with additional knowledge gained from experts on measurement, quality and coverage. Second, we present our method for eliciting this information. Third, we describe how this information is converted into prior distributions for subsequent use in a Bayesian model for estimating migration flows amongst countries in the European Union (EU) and European Free Trade Association (EFTA). The article concludes with an assessment of the importance of expert information and a discussion of lessons learned from the elicitation process.
Publisher: Oxford University Press (OUP)
Date: 22-01-2016
DOI: 10.1111/RSSA.12177
Abstract: Age and sex patterns of migration are essential for understanding drivers of population change and heterogeneity of migrant groups. We develop a hierarchical Bayesian model to estimate such patterns for international migration in the European Union and European Free Trade Association from 2002 to 2008, which was a period of time when the number of members expanded from 19 to 31 countries. Our model corrects for the inadequacies and inconsistencies in the available data and estimates the missing patterns. The posterior distributions of the age and sex profiles are then combined with a matrix of origin–destination flows, resulting in a synthetic database with measures of uncertainty for migration flows and other model parameters.
Publisher: Springer Science and Business Media LLC
Date: 10-2010
DOI: 10.1057/PT.2010.23
Abstract: We compare official population projections with Bayesian time series forecasts for England and Wales. The Bayesian approach allows the integration of uncertainty in the data, models and model parameters in a coherent and consistent manner. Bayesian methodology for time-series forecasting is introduced, including autoregressive (AR) and stochastic volatility (SV) models. These models are then fitted to a historical time series of data from 1841 to 2007 and used to predict future population totals to 2033. These results are compared to the most recent projections produced by the Office for National Statistics. Sensitivity analyses are then performed to test the effect of changes in the prior uncertainty for a single parameter. Finally, in-s le forecasts are compared with actual population and previous official projections. The article ends with some conclusions and recommendations for future work.
Publisher: Informa UK Limited
Date: 09-2013
Publisher: Wiley
Date: 04-2018
Publisher: Ovid Technologies (Wolters Kluwer Health)
Date: 09-2014
Publisher: Elsevier BV
Date: 07-2022
Location: United Kingdom of Great Britain and Northern Ireland
Start Date: 2010
End Date: 2015
Funder: Engineering and Physical Sciences Research Council
View Funded ActivityStart Date: 2014
End Date: 2018
Funder: Economic and Social Research Council
View Funded Activity