H
Hana Ševčíková
Researcher at University of Washington
Publications - 39
Citations - 2371
Hana Ševčíková is an academic researcher from University of Washington. The author has contributed to research in topics: Population & Probabilistic logic. The author has an hindex of 13, co-authored 35 publications receiving 1938 citations. Previous affiliations of Hana Ševčíková include Helmut Schmidt University & University of Hamburg.
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Journal ArticleDOI
World population stabilization unlikely this century
Patrick Gerland,Adrian E. Raftery,Hana Ševčíková,Nan Li,Danan Gu,Thomas Spoorenberg,Leontine Alkema,Bailey K. Fosdick,Jennifer Chunn,Nevena Lalic,Guiomar Bay,Thomas Buettner,Gerhard K. Heilig,John R. Wilmoth +13 more
TL;DR: World population is likely to continue growing for the rest of the century, with at least a 3.5-fold increase in the population of Africa and the ratio of working-age people to older people is almost certain to decline substantially in all countries, not just currently developed ones.
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Bayesian probabilistic population projections for all countries
TL;DR: The results suggest that the current United Nations high and low variants greatly underestimate uncertainty about the number of oldest old from about 2050 and that they underestimate uncertainty for high fertility countries and overstate uncertainty for countries that have completed the demographic transition.
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Estimators of Fractal Dimension: Assessing the Roughness of Time Series and Spatial Data
TL;DR: In this paper, the authors review and assess estimators of fractal dimension by their large sample behavior under infill asymptotics, in extensive finite sample simulation studies, and in a data example on arctic sea-ice profiles.
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Bayesian Probabilistic Projections of Life Expectancy for All Countries
TL;DR: A Bayesian hierarchical model for producing probabilistic forecasts of male period life expectancy at birth for all the countries of the world to 2100 is proposed and illustrated with results from Madagascar, Latvia, Japan, and Japan.
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Assessing Uncertainty in Urban Simulations Using Bayesian Melding
TL;DR: A method for assessing uncertainty about quantities of interest using urban simulation models is developed, and Bayesian melding is applied to the projection of future household numbers by traffic activity zone in Eugene-Springfield, Oregon, using the UrbanSim model developed at the University of Washington.