Academic literature on the topic 'Generalized Age-Period-Cohort'
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Journal articles on the topic "Generalized Age-Period-Cohort"
Jürgens, Verena, Silvia Ess, Thomas Cerny, and Penelope Vounatsou. "A Bayesian generalized age-period-cohort power model for cancer projections." Statistics in Medicine 33, no. 26 (July 3, 2014): 4627–36. http://dx.doi.org/10.1002/sim.6248.
Full textJiang, Bei, and Keumhee C. Carriere. "Age-period-cohort models using smoothing splines: a generalized additive model approach." Statistics in Medicine 33, no. 4 (September 9, 2013): 595–606. http://dx.doi.org/10.1002/sim.5970.
Full textHall, M., and N. Friel. "Mortality Projections using Generalized Additive Models with applications to annuity values for the Irish population." Annals of Actuarial Science 5, no. 1 (November 12, 2010): 19–32. http://dx.doi.org/10.1017/s1748499510000011.
Full textChernyavskiy, Pavel, Mark P. Little, and Philip S. Rosenberg. "Spatially varying age–period–cohort analysis with application to US mortality, 2002–2016." Biostatistics 21, no. 4 (April 28, 2019): 845–59. http://dx.doi.org/10.1093/biostatistics/kxz009.
Full textGAO, Xuede, and Haoyun MA. "Changing trends of Chinese people's generalized trust: A dynamic analysis of the age-period-cohort effect." Acta Psychologica Sinica 57, no. 3 (2025): 463. https://doi.org/10.3724/sp.j.1041.2025.0463.
Full textYang, Yang, Wenjiang J. Fu, and Kenneth C. Land. "2. A Methodological Comparison of Age-Period-Cohort Models: The Intrinsic Estimator and Conventional Generalized Linear Models." Sociological Methodology 34, no. 1 (December 2004): 75–110. http://dx.doi.org/10.1111/j.0081-1750.2004.00148.x.
Full textGrasso, Maria Teresa, Stephen Farrall, Emily Gray, Colin Hay, and Will Jennings. "Thatcher’s Children, Blair’s Babies, Political Socialization and Trickle-down Value Change: An Age, Period and Cohort Analysis." British Journal of Political Science 49, no. 1 (January 26, 2017): 17–36. http://dx.doi.org/10.1017/s0007123416000375.
Full textMousavi-Jarrrahi, Seyed Houssein, Amir Kasaeian, Kamyar Mansori, Mehdi Ranjbaran, Mahmoud Khodadost, and Alireza Mosavi-Jarrahi. "Addressing the Younger Age at Onset in Breast Cancer Patients in Asia: An Age-Period-Cohort Analysis of Fifty Years of Quality Data from the International Agency for Research on Cancer." ISRN Oncology 2013 (September 2, 2013): 1–8. http://dx.doi.org/10.1155/2013/429862.
Full textRanjbaran, Mehdi, Mahmood Khodadost, Kamyar Mansori, Seyed Houssien Mousavi Jarrahi, Erfan Ayubi, Milad Nazarzadeh, and Alireza Mosavi Jarrahi. "Mobile Phone Use and Brain Tumor: An Age-Period-Cohort Analysis of Brain Tumor Rates in the Nordic Population." Asian Pacific Journal of Cancer Biology 1, no. 1 (April 25, 2016): 19–29. http://dx.doi.org/10.31557/apjcb.2016.1.1.19-29.
Full textMarsh, Herbert W. "Age and Gender Effects in Physical Self-Concepts for Adolescent Elite Athletes and Nonathletes: A Multicohort-Multioccasion Design." Journal of Sport and Exercise Psychology 20, no. 3 (September 1998): 237–59. http://dx.doi.org/10.1123/jsep.20.3.237.
Full textDissertations / Theses on the topic "Generalized Age-Period-Cohort"
Burg, Antoine. "Multivariate extensions for mortality modelling." Electronic Thesis or Diss., Université Paris sciences et lettres, 2025. http://www.theses.fr/2025UPSLD002.
Full textOver the past two centuries, life expectancy around the globe has increased considerably. While the long-term trend is fairly regular, the improvement in longevity can be broken down into several phases in the short term, which can most often be linked to medical progress and the reduction in specific causes of mortality. The year 2020 marks a turning point due to the scale of the Covid-19 pandemic and its consequences. Its direct and indirect effects on the economy and healthcare systems will also be felt through other major causes of death. To understand and anticipate mortality-related risks, it is becoming increasingly necessary for reinsurance players to reason and model in terms of causes of death. However, this type of modeling poses specific challenges. By its very nature, it involves multivariate models, whose complexity exceeds that of conventional actuary tools. In this thesis, we propose several avenues for extending mortality modeling to a multivariate framework. These are presented in the form of research articles. The first study deals with technical aspects of multivariate distributions within generalized linear models. When the explanatory variables are categorical, we propose new estimators for the multinomial, negative multinomial and Dirichlet distributions in the form of closed formulas, which notably enable considerable savings in computation time. These estimators are used in the second study to propose a new method for estimating the parameters of mortality models. This method extends the existing framework for all-cause mortality, and enables all mortality modeling issues to be addressed in a single step, particularly by cause-of-death. The third axis concerns mortality forecasts. We study neural networks specifically adapted to time series. Based on concrete use cases, we show that these models are sufficiently flexible and robust to offer a credible alternative to conventional models
Ishibashi, Raphael Akira Siqueira. "Análise da incidência dos linfomas no município de São Paulo, 1997 a 2012." Universidade de São Paulo, 2018. http://www.teses.usp.br/teses/disponiveis/6/6143/tde-01112018-105613/.
Full textIntroduction: Lymphomas comprise a heterogeneous group of neoplasias originating in the lymphatic system, different in their histology, prognosis and epidemiology, although there may be a large number of common clinical aspects. According to their morphology, two groups are divided: Hodgkin\'s lymphomas (HL) and non-Hodgkin\'s lymphomas (NHL). Objective: To evaluate the temporal trend of the incidence of lymphomas in the period from 1997 to 2012, identifying the influence of factors such as sex, age, period and cohort and birth. Methodology: This is an ecological study. Information about all new cases of lymphomas in the city of São Paulo, diagnosed in the period from 1997 to 2012, were obtained from the São Paulo Population Based Cancer Registry. Information on the number of inhabitants of the Municipality were obtained online through the website of the SUS Department of Informatics. To evaluate the trend of lymphoma incidence during the period, according to sex and age group, generalized linear models (GLM) were adjusted. The age-period-cohort (APC) model was used to evaluate the influence of age, diagnosis period and birth cohorts on the trend of lymphoma incidence. Results and conclusions: Of the 18,037 cases analyzed, 20.5% were HL type and 79.5% NHL type. Among the cases of HL, 52.6% were men and 70.0% were between 20 and 39 years old. The age-standardized incidence rate of HL per 100,000 population ranged from 5.0 in 1997 to 4.0 in 2012, however, no significant trend in incidence over time was detected (p> 0.05). The risk of developing HL was greater in males than in females only in the age group 0 to 14 years (p <0.001), in the others, the risk was similar for both sexes (p> 0.05). The risk of developing HL according to the age group presented a bimodal age pattern. In females, the highest risks occurred in the 20-39 and 65-year-olds, and in the male, in the 15-19 and 65 years and older ranges. The APC model pointed to an influence of the birth cohort on the incidence of HL in women: for those born before 1960, the older the cohort, the higher the risk of HL; for those born after 1960, the relative risk remained stable. Among the cases of NHL, 51.6% were men and 77.4% were over 40 years old. The age-standardized incidence rate of NHL per 100,000 population increased from 22.6 in 1997 to 17.0 in 2012. A trend of a decrease in the incidence of NHL of 1.7% per year was observed in all ranges in both sexes (p <0.001). The risk of developing NHL increased steadily with advancing age in both sexes. The APC model did not detect period effects. For men, older cohorts are at higher risk, and younger cohorts are at lower risk. For women born before 1960 the behavior was similar to that of men, for those born after 1960, the relative risk remained stable.
Books on the topic "Generalized Age-Period-Cohort"
Ferraro, Kenneth F. Causality. Oxford University Press, 2017. http://dx.doi.org/10.1093/med/9780190665340.003.0002.
Full textBook chapters on the topic "Generalized Age-Period-Cohort"
Yang, Yang, and Kenneth C. Land. "Formalities of the Age-Period-Cohort Analysis Conundrum and a Generalized Linear Mixed Models (GLMM) Framework." In Age-Period-Cohort Analysis, 55–73. Chapman and Hall/CRC, 2016. http://dx.doi.org/10.1201/b13902-4.
Full textD'Amato Valeria, Piscopo Gabriella, and Russolillo Maria. "Iterative Algorithms for detecting mortality trends in the family of Lee Carter Models." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2011. https://doi.org/10.3233/978-1-60750-692-8-69.
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