1,584 research outputs found
Voyage Illustre dans Les Cinq Parties Du Monde par Adolphe Joanne
No full textFrontispiece depicting images from the travel book, "Voyage Illustre dans Les Cinq Parties Du Monde" by Adolphe Laurent Joanne. At center in front of a large globe, a woman wearing a laurel wreath on her head is seated writing in a book. Various people and animals from around the world are gathered behind her. Books, instruments and weapons lie in front of her, and famous buildings and landscapes are shown in the background. A balloon flies overhead.For more information about this item, visit https://archivesspace.mit.edu/repositories/2/digital_objects/79
Stochastic modelling and projection of age-specific fertility rates
No full textFertility projections are a key determinant of population forecasts, which are widely used by government policymakers and planners. They are also vital to anticipate demand for maternity and childcare services, as well as school places and housing. As such, models that can generate plausible fertility forecasts with appropriate uncertainty are in high demand. To this end, in this thesis we develop two distinct Bayesian fertility projection models, using multiple data sources and state-of-the-art computational methodology.In the first approach we take an international perspective, working with population-level data indexed by age and cohort from the Human Fertility Database. In keeping with the recent literature, we propose an intuitive and transparent hierarchical Bayesian model which borrows strength across ages and cohorts. Using Hamiltonian Monte Carlo methods, we obtain forecasts for 30 countries. Quantitative assessment of the predictive accuracy using scoring rules indicates that our model predicts at a comparable level to that of the best-performing models in the current literature overall, with stronger performance for countries without a recent structural shift. Our findings support the position of hierarchical Bayesian modelling at the forefront of population forecasting methods.Our second approach focuses on England and Wales, modelling individual-level data in the form of fertility histories and additional information collected from 18,218 women interviewed in Understanding Society. We progress the discrete-time event history analysis literature in this context by applying the smooth, flexible framework of generalized additive models (GAMs). Through fitting parity-specific logistic GAMs to the survey data, we learn about the effects of age, cohort, time since last birth and qualification on fertility. We then develop our chosen GAMs into a Bayesian projection model incorporating population-level data. Our innovative integration method enables assessment of forecast sensitivity in relation to the balance of the two datasets, thus making important advances in statistical methodology
Fashion and Physique Symposium:Dr. Joanne Entwistle “New Models of Diversity”
No full textDr. Joanne Entwistle presenting “New Models of Diversity” at The Museum at FIT's 19th fashion symposium, Fashion and Physique, held on Friday, February 23, 2018.The one-day symposium featured lectures and panels on topics such as the emergence of the plus-size fashion industry in the early twentieth century, the impact of popular culture on how we assess the female body, and fashion accessibility for the disabled in the technological age.Dr. Joanne Entwistle is a reader in culture, media and creative industries at King’s College, London. She is author of "The Fashioned Body: Fashion, Dress and Modern Social Theory.
Can incorporating parity information improve the reliability of completed cohort fertility projections? Insights from a Bayesian generalized additive model approach
No full textFertility projections inform population projections and are used to plan for thefuture provision of vital services such as maternity care and schooling. Existingfertility forecasting models tend to use aggregate births data indexed by age and time alone, thereby neglecting to include information about parity, i.e. the number of previous live-born children. This omission risks ignoring a crucial mechanism of ffertility dynamics. We propose a Bayesian parity-specific fertility projection model to complete cohort fertility, within a generalized additive model (GAM) framework. The use of GAMs enables a smooth age-cohort rate surface to be estimated for each parity simultaneously. We constrain our model using aggregate data and additionally introduce random walk priors on completed family size and parity progression ratios, which are summary fertility measures known to change relatively slowly over time. Using Hamiltonian Monte Carlo methods and data from the Human Fertility Database, we fit our model to 16 countries. We compare our forecasts with the best-performing existing models to quantify the impact of including the parity dimension on predictiveaccuracy. Our findings indicate that a parity-specific approach could lead to more plausible and reliable fertility projections, aiding government planners in their decision-making and enabling more tailored policy solutions
Forecasting of cohort fertility under a hierarchical Bayesian approach
No full textFertility projections are a key determinant of population forecasts, which are widely used by government policy makers and planners. In keeping with the recent literature, we propose an intuitive and transparent hierarchical Bayesian model to forecast cohort fertility. Using Hamiltonian Monte Carlo methods and a data set from the human fertility database, we obtain fertility forecasts for 30 countries. We use scoring rules to assess the predictive accuracy of the forecasts quantitatively; these indicate that our model predicts with an accuracy comparable with that of the best-performing models in the current literature overall, with stronger performance for countries without a recent structural shift. Our findings support the position of hierarchical Bayesian modelling at the forefront of population forecasting methods.</p
Investigating the application of generalized additive models to discrete-time event history analysis for birth events
No full textDiscrete-time event history analysis (EHA) is the standard approach taken when modelling fertility histories collected in surveys, where the date of birth is often recorded imprecisely. This method is commonly used to investigate the factors associated with the time to a first or subsequent conception or birth. Although there is an emerging trend towards the smooth incorporation of continuous covariates in the broader literature, this is yet to be formally embraced in the context of birth events. We investigate the formal application of smooth methods implemented via generalized additive models (GAMs) to the analysis of fertility histories. We also determine whether and where GAMs offer a practical improvement over existing approaches. We fit parity-specific logistic GAMs to data from the UK Household Longitudinal Study, learning about the effects of age, period, time since last birth, educational qualification and country of birth. First we select the most parsimonious GAMs that fit the data sufficiently well. Then we compare them with corresponding models that use the existing methods of categorical, polynomial and piecewise linear spline representations in terms of fit, complexity, and substantive insights gained. We find that smooth terms can offer considerable improvements in precision and efficiency, particularly for highly non-linear effects and interactions between continuous variables. Their flexibility enables the detection of important features that are missed or estimated imprecisely by comparator methods. Our findings suggest that GAMs are a useful addition to the demographer’s toolkit. They are highly relevant for motivating future methodological developments in EHA, both for birth events and more generally
Estimating the 2011 total fertility rate for England & Wales and Scotland using alternative data sources
No full textIt is important to estimate fertility rates as accurately as possible in order to make appropriate comparisons of fertility levels across time and space and to inform fertility projections. This paper compares estimates of the 2011 total fertility rate (TFR) for all, UK-born and non-UKborn women in England & Wales and Scotland, obtained using several data sources. The three data sources we use are vital registration (VR) data, longitudinal studies (linked census and vital events data) and census household microdata samples from the respective countries. Although estimates based on VR data are classed as official, the event and risk population information come from different sources. Surveys and census data do not suffer from this issue, but their analysis requires decisions regarding the selection of the sample and how to deal with exits and entries to the UK. We find:• TFR estimates from the census microdata tend to be closest to those from VR data, particularly for Scotland. For England & Wales, the census estimates are lower than those from VR data, especially for non-UK-born women.• The longitudinal study estimates are the lowest among the three data sources for Scotland, while for England & Wales they are lower or higher than the corresponding VR estimate with this generally depending on the precise estimation method used.• Overall, this study finds some small variation in the TFR estimates from these different sources, owing to their contrasting coverage, mode of collection and sample size.• The reasonable consistency of the census-linked data and the census household microdata with the VR estimates shows that they are an important source of information which allows the examination of subgroup differences in childbearing behaviour
Thesis code: Stochastic modelling and projection of age-specific fertility rates
No full textThis dataset contains R code and Stan files to perform analyses in Chapters 2, 3 and 4 of the PhD thesis entitled 'Stochastic modelling and projection of age-specific fertility rates' awarded by the University of Southampton in 2021.
See the readme file for further information.</span
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