Projecting Dynamic Life Tables Using Data Cloning

2017 ◽  
pp. 43-57
Author(s):  
Andrés Benchimol ◽  
Irene Albarrán ◽  
Juan Miguel Marín ◽  
Pablo Alonso-González
Keyword(s):  
Life Tables ◽  
Data Cloning ◽  
Dynamic Life Tables ◽  
Using Data

Constructing dynamic life tables with a single-factor model

2020 ◽  
Vol 43 (2) ◽  
pp. 787-825
Author(s):  
David Atance ◽  
Alejandro Balbás ◽  
Eliseo Navarro
Keyword(s):  
Factor Model ◽  
Life Tables ◽  
Single Factor ◽  
Dynamic Life Tables ◽  
Single Factor Model

scholarly journals Adult mortality in preindustrial Quebec

2013 ◽  
Vol 39 (3-4) ◽  
pp. 23 ◽  
Author(s):  
Claudine Lacroix ◽  
Bertrand Desjardins
Keyword(s):  
Adult Mortality ◽  
Life Tables ◽  
New France ◽  
Differential Mortality ◽  
French Canadians ◽  
Rank Tests ◽  
First Marriage ◽  
Age At First Marriage ◽  
Using Data ◽  
Gender Type

This paper presents the main results of a detailed study on adult mortality in French Canadians born before 1750 and having married inthe colony of New France. Using data from parish registers, mortality is studied using abridged life tables, with staggered entries according to age at first marriage. Survival tables and log-Rank tests are used to support the results. Three features were selected for the study of differential mortality: gender, type of residence area (urban or rural), and cohort. The mortality of French Canadians is compared to that of their French contemporaries.

scholarly journals Executive branch federal civil servant mortality by sex and educational level - 1993/2014

2017 ◽  
Vol 28 (75) ◽  
pp. 445-464 ◽  
Author(s):  
Kaizo Iwakami Beltrão ◽  
Sonoe Sugahara
Keyword(s):  
Educational Level ◽  
Goodness Of Fit ◽  
Executive Branch ◽  
Life Tables ◽  
Public Servants ◽  
External Causes ◽  
Actuarial Studies ◽  
Using Data ◽  
Different Cultures ◽  
The City

ABSTRACT Life tables have been elaborated throughout much of human history. However, the first life table to use actuarial concepts was only constructed in 1815 by Milne for the city of Carlisle in England. Since then, numerous tables have been elaborated for different regions and countries, due to their crucial importance for analyzing various types of problems covering a vast range of possibilities, from actuarial studies to forecasting and evaluating demands in order to define public policies. The most common problem nowadays in an actuarial calculation is choosing a suitable table for a given population. Brazil has few specific tables for the pensions market and has been using imported tables that refer to other countries, with different cultures and different mortality experiences. Using data from the Integrated Human Resource Administration System, this table constructs life tables for Executive branch federal civil servants for the period from 1993 to 2014, disaggregated for sex, age, and educational level (high school and university). The international literature has recognized differences in mortality due to sex, socioeconomic differences, and occupation. The creation of the Complementary Pension Foundation for Federal Public Servants in 2013 requires specific mortality tables for this population to support actuarial studies, healthcare, and personnel policies. A mathematical equation is fitted to the data. This equation can be broken down into infant mortality (not present in the data), mortality from external causes, and mortality from senescence. Recent results acknowledging an upper limit for old age mortality are incorporated into the adjusted probabilities of death. Assuming a binomial distribution for deaths, the deviance was used as a figure of merit to evaluate the goodness of fit of the observed data both to a set of tables used by the insurance/pensions market and to the adjusted tables.

scholarly journals A Comparison of Forecasting Mortality Models Using Resampling Methods

Mathematics ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. 1550
Author(s):  
David Atance ◽  
Ana Debón ◽  
Eliseo Navarro
Keyword(s):  
Insurance Industry ◽  
Geographical Area ◽  
Real Data ◽  
Life Tables ◽  
Insurance Companies ◽  
Prediction Ability ◽  
Resampling Methods ◽  
Proactive Approach ◽  
Mortality Models ◽  
Dynamic Life Tables

The accuracy of the predictions of age-specific probabilities of death is an essential objective for the insurance industry since it dramatically affects the proper valuation of their products. Currently, it is crucial to be able to accurately calculate the age-specific probabilities of death over time since insurance companies’ profits and the social security of citizens depend on human survival; therefore, forecasting dynamic life tables could have significant economic and social implications. Quantitative tools such as resampling methods are required to assess the current and future states of mortality behavior. The insurance companies that manage these life tables are attempting to establish models for evaluating the risk of insurance products to develop a proactive approach instead of using traditional reactive schemes. The main objective of this paper is to compare three mortality models to predict dynamic life tables. By using the real data of European countries from the Human Mortality Database, this study has identified the best model in terms of the prediction ability for each sex and each European country. A comparison that uses cobweb graphs leads us to the conclusion that the best model is, in general, the Lee–Carter model. Additionally, we propose a procedure that can be applied to a life table database that allows us to choose the most appropriate model for any geographical area.

Social class differences in mortality in Great Britain around 1981

1988 ◽  
Vol 115 (3) ◽  
pp. 495-517 ◽  
Author(s):  
S. Haberman ◽  
D. S. F. Bloomfield
Keyword(s):  
Longitudinal Study ◽  
Great Britain ◽  
Social Class ◽  
Life Tables ◽  
Social Classes ◽  
Class Differences ◽  
The Social ◽  
Occupational Mortality ◽  
Using Data ◽  
The Individual

The Decennial Supplement on Occupational Mortality published in 1978 commented on mortality differences between the social classes (Chapter 8) using data from the 1971 Census and the deaths in the period 1970–72. The analysis was based on life tables prepared for the individual social classes from which derived indices, for example expectations of life, were calculated. It is proposed here to repeat this exercise using the data for males recently published in microfiche form by the Office of Population Censuses and Surveys—OPCS. This time, the Decennial Supplement has omitted to provide an analysis and commentary and we propose to make some attempt to remedy this deficiency. In our analysis, the Decennial Supplement data have been supplemented by data from the OPCS Longitudinal Study.

scholarly journals Assessing Parameter Identifiability in Phylogenetic Models Using Data Cloning

2012 ◽  
Vol 61 (6) ◽  
pp. 955-972 ◽  
Author(s):  
José Miguel Ponciano ◽  
J. Gordon Burleigh ◽  
Edward L. Braun ◽  
Mark L. Taper
Keyword(s):  
Parameter Identifiability ◽  
Data Cloning ◽  
Using Data ◽  
Phylogenetic Models

Hierarchical models in ecology: confidence intervals, hypothesis testing, and model selection using data cloning

Ecology ◽  
2009 ◽  
Vol 90 (2) ◽  
pp. 356-362 ◽  
Author(s):  
José Miguel Ponciano ◽  
Mark L. Taper ◽  
Brian Dennis ◽  
Subhash R. Lele
Keyword(s):  
Model Selection ◽  
Hypothesis Testing ◽  
Confidence Intervals ◽  
Hierarchical Models ◽  
Data Cloning ◽  
Using Data

Life tables: construction and interpretation

2021 ◽  
pp. 151-162
Author(s):  
Owen R. Jones
Keyword(s):  
Life Histories ◽  
Worked Examples ◽  
Time Frame ◽  
Life Tables ◽  
Type I ◽  
Type Ii ◽  
Risk Of Death ◽  
Nonhuman Animals ◽  
Using Data ◽  
Diversity Of Life

Life tables, which describe how the risk of death (and sometimes fertility) changes with age, are a fundamental tool for describing and exploring the diversity of life histories. Numerous important life history metrics can be derived from them. This chapter provides a broad coverage of life table construction and use and use with a particular focus on nonhuman animals. The calculation of life tables can be divided into approaches: cohort-based, where the data are obtained from individuals born at (approximately) the same time that are followed until death; and period-based, where the data are obtained from a population of mixed ages followed for a particular time-frame (e.g. a year). Worked examples of both approaches are provided using data from published sources. Emphasis is placed on understanding concepts such as rates vs. probability, life expectancy, and generation time. Links are drawn between the survivorship curve (type I, type II, and type III survivorship) and entropy. The chapter also covers the concept of the Lexis diagram which is used to represent births and deaths for individuals in different cohorts. Finally, the assumptions and limitations of life tables are discussed, with pointers to further reading. Code and data are provided.

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