Graduating the age-specific fertility pattern using Support Vector Machines

Kostaki, Anastasia; Moguerza, Javier M.; Olivares, Alberto; Psarakis, Stelios

doi:10.4054/demres.2009.20.25

Cited by 10 publications

(6 citation statements)

References 22 publications

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“…The above findings have stimulated new research questions and the development of more flexible statistical models which are able to adequately describe these non-standard shapes and characterize their dynamic evolution. Recent approaches include models relying on mixtures of symmetric distributions (Peristera and Kostaki 2007;Bermúdez et al 2012), smoothing splines (Schmertmann 2003) and skewed distributions (Mazzuco and Scarpa 2015), with some parametric assumptions sometimes relaxed via nonparametric alternatives (Kostaki et al 2009;Canale and Scarpa 2015). Clearly, the improved fit of these models comes at a price in terms of interpretability.…”

Section: Introductionmentioning

confidence: 99%

Projecting Proportionate Age–Specific Fertility Rates via Bayesian Skewed Processes

Aliverti

Durante

Scarpa

2020

The Springer Series on Demographic Methods and Population Analysis

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Fertility rates show dynamically–varying shapes when modeled as a function of the age at delivery. We incorporate this behavior under a novel Bayesian approach for dynamic modeling of proportionate age–specific fertility rates via skewed processes. The model assumes a skew–normal distribution for the age at the moment of childbirth, while allowing the location and the skewness parameters to evolve in time via Gaussian processes priors. Posterior inference is performed via Monte Carlo methods, leveraging results on unified skew–normal distributions. The proposed approach is illustrated on Italian age–specific fertility rates from 1991 to 2014, providing forecasts until 2030.

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Section: Introductionmentioning

confidence: 99%

Projecting Proportionate Age–Specific Fertility Rates via Bayesian Skewed Processes

Aliverti

Durante

Scarpa

2020

The Springer Series on Demographic Methods and Population Analysis

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“…However, even though from the point of view of formal demography much is known about fertility trends and the demography of marriages during the 20th century (see for example Balourdos et al, 2007;Peristera & Kostaki, 2007Kotzamanis & Sofianopoulou, 2009;Kostaki et al, 2009), comparative studies of anthropological populations living on mainland Greece are rare. Hatzisavva and Xirotiris (2003) and Zafeiris et al (2015) studied Metsovo (Epirus, Greece) and Zafeiris and Xirotiris (2007) studied the demographic and genealogical structure of the Vlachic population of Karitsa (Pieria) and of the Christian Roma of Aratos (Zafeiris & Xirotiris, 2002).…”

Section: Introductionmentioning

confidence: 99%

Completed Fertility During the Twentieth Century: An Example From Six Settlements in Northern Greece

Zafeiris

Kaklamani

2018

J. Biosoc. Sci.

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This study aimed to delineate temporal trends and differentials of completed fertility and their relationship with some characteristics of the marriage system in specific anthropological populations of northern Greece. The analysis was based on the life history of quinquennial and decennial birth cohorts of married women born in the 20th century who reproduced solely within the settlements studied. The variables studied were: children ever born, mean age of mother at first marriage, mean age of mother at first child (live birth), mean age of mother at last child and reproductive span. The results indicated that there were significant differences in the demographic characteristics of marriage and that there was an ongoing fertility transition in the 20th century in the populations studied. The mechanism of fertility decline was connected with the gradual reduction of the mean age of the mother at last child, the parallel decrease in the mean age at childbearing and a shortening of the reproductive span. Fertility levels at all times maintained a dynamic character imposed by local cultural, economic and social structures, which, in turn, were part of broader national and international structures, in all the populations studied. A strong trend of convergence of fertility levels was observed among the populations studied.

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“…Since SVM techniques have shown very successful results in smoothing noisy data, such as neighbourhood curves (Muñoz and Moguerza 2005) or nonlinear profiles (Moguerza et al 2007), they can probably serve as an equally useful tool for mortality graduation purposes. Regarding demographic data, SVM have shown an interesting performance when applied to the graduation of age-specific fertility patterns (Kostaki et al 2009). These techniques are easy to adjust, which implies they can be easily applied by demographers who may lack a thorough background on Statistical Learning Theory or pattern recognition.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Machines as tools for mortality graduation

Kostaki

Moguerza

Olivares³

et al. 2012

CSP

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A topic of interest in demographic and biostatistical analysis as well as in actuarial practice,is the graduation of the age-specific mortality pattern. A classical graduation technique is to fit parametric models. Recently, particular emphasis has been given to graduation using nonparametric techniques. Support Vector Machines (SVM) is an innovative methodology that could be utilized for mortality graduation purposes. This paper evaluates SVM techniques as tools for graduating mortality rates. We apply SVM to empirical death rates from a variety of populations and time periods. For comparison, we also apply standard graduation techniques to the same data.

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Graduating the age-specific fertility pattern using Support Vector Machines

Cited by 10 publications

References 22 publications

Projecting Proportionate Age–Specific Fertility Rates via Bayesian Skewed Processes

Projecting Proportionate Age–Specific Fertility Rates via Bayesian Skewed Processes

Completed Fertility During the Twentieth Century: An Example From Six Settlements in Northern Greece

Support Vector Machines as tools for mortality graduation

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