PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 12287090

Count data models for demographic data.

R Winkelmann·K F Zimmermann
View original source ↗Export metadata ↓

Abstract

"This paper deals with the estimation of single equation models in which the counts are regressed on a set of observed individual characteristics such as age, gender, or nationality.... We propose a generalized event count model to simultaneously allow for a wide class of count data models and account for over- and underdispersion. This model is successfully applied to German data on fertility, divorces and mobility." (SUMMARY IN FRE)

Explore related subjects

DemographyDeveloped CountriesDivorceEmigration and ImmigrationEuropeFertilityGermanyMarriageModels, TheoreticalPopulationPopulation DynamicsResearchStatistics as TopicDemographic FactorsEstimation TechnicsMathematical ModelMigrationNuptialityResearch MethodologyWestern EuropeJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

R Winkelmann, K F Zimmermann. 1994. Count data models for demographic data.. https://doi.org/10.1080/08898489409525374

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

PubMed2006

Psychological distress among American Red Cross disaster workers responding to the terrorist attacks of September 11, 2001.

This study investigated American Red Cross disaster workers' symptoms of distress and posttraumatic stress resulting from exposure to disaster stimuli during their response to the September 11, 2001 terrorist attacks. A sample of 3055 Red Cross disaster workers was surveyed 1 year after the terrorist attacks regarding demographic characteristics, function during the response, and exposure to disaster stimuli. Participants were grouped by function and self-reported exposure, with the hypothesis that workers in Direct Services and/or those reporting to be directly exposed to disaster stimuli would experience greater levels of posttraumatic stress symptoms and distress than workers in indirect services or reporting no exposure. Findings revealed that while there were significant differences between both Function and Exposure groups on dependent measures, the multivariate eta2 was very small for both and did not meet medium effect size criteria. The results indicated that workers directly exposed to disaster stimuli reported no more distress than those who were not directly exposed.

Demography↗
PubMed2006

Controlling the false-positive rate in multilocus genome scans for selection.

Rapid typing of genetic variation at many regions of the genome is an efficient way to survey variability in natural populations in an effort to identify segments of the genome that have experienced recent natural selection. Following such a genome scan, individual regions may be chosen for further sequencing and a more detailed analysis of patterns of variability, often to perform a parametric test for selection and to estimate the strength of a recent selective sweep. We show here that not accounting for the ascertainment of loci in such analyses leads to false inference of natural selection when the true model is selective neutrality, because the procedure of choosing unusual loci (in comparison to the rest of the genome-scan data) selects regions of the genome with genealogies similar to those expected under models of recent directional selection. We describe a simple and efficient correction for this ascertainment bias, which restores the false-positive rate to near-nominal levels. For the parameters considered here, we find that obtaining a test with the expected distribution of P-values depends on accurately accounting both for ascertainment of regions and for demography. Finally, we use simulations to explore the utility of relying on outlier loci to detect recent selective sweeps. We find that measures of diversity and of population differentiation are more effective than summaries of the site-frequency spectrum and that sequencing larger regions (2.5 kbp) in genome-scan studies leads to more power to detect recent selective sweeps.

Demography↗
PubMed2006

Social sexual inequality and sex difference in cancer incidence.

Socioeconomic factors play many roles in influencing health including overall health status, lifestyle and occupational exposures, and access to preventive, diagnostic and treatment services. This paper reviews evidence on the geographical distribution of the sex differences in cancer incidence and life expectancy. The analyses reported are at the regional (Italy), continental (Europe), and world-wide scales. In agreement with other contributions on the social epidemiology of cancer, these results indicate that there is a close link between the health of the populations, and socioeconomic and cultural factors, and support the notion that environment contributes strongly to total cancer incidence. Thus, the emphasis for reducing cancer incidence needs to focus more on reducing environmental contributions. In order to improve the health status of the populations, not only applications of the present etiologic knowledge are necessary (for example, it is estimated that around up to 50% of cancers are nowadays technically preventable), but also further research on environmental topics should be stimulated. Within this perspective the indicators of health differences between genders-which are demonstrated to be very sensitive to socioeconomic and cultural factors--can play a very useful role for monitoring environmental factors, and for health planning by agencies and governments.

Demography↗