Wilhelm Lexis: The Normal Length of Life as an Expression of the "Nature of Things"

Véron, Jacques; Rohrbasser, Jean-Marc

doi:10.2307/3246676

Cited by 5 publications

(4 citation statements)

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“…these ideas influenced the nascent discipline of demography through the work of wilhelm lexis, a German statistician who studied mortality through the lens of distributional regularities (véron and Rohrbasser 2003). lexis's theory of normal life emphasized a "normal age"-corresponding to the mode of old-age mortality-which represented the length of life that each individual could expect in the absence of premature mortality (lexis 1877, 1878).…”

Section: From Error To Variationmentioning

confidence: 99%

“…lexis's theory of normal life emphasized a "normal age"-corresponding to the mode of old-age mortality-which represented the length of life that each individual could expect in the absence of premature mortality (lexis 1877, 1878). like Quetelet, lexis attached a normative as well as descriptive value to the center of the mortality distribution (véron and Rohrbasser 2003). He calculated the probability of deviation from the normal age for various european populations (1878) and interpreted the consistency of this variability measure across populations as a mathematical regularity representing the randomness inherent in natural phenomena.…”

Section: From Error To Variationmentioning

confidence: 99%

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The Implications of Increased Survivorship for Mortality Variation in Aging Populations

Engelman

Canudas-Romo

Agree

2010

Population & Development Rev

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The remarkable growth in life expectancy during the twentieth century inspired predictions of a future in which all people, not just a fortunate few, will live long lives ending at or near the maximum human life span. We show that increased longevity has been accompanied by less variation in ages at death, but survivors to the oldest ages have grown increasingly heterogeneous in their mortality risks. These trends are consistent across countries, and apply even to populations with record-low variability in the length of life. We argue that as a result of continuing improvements in survival, delayed mortality selection has shifted health disparities from early to later life, where they manifest in the growing inequalities in late-life mortality.

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Section: From Error To Variationmentioning

confidence: 99%

Section: From Error To Variationmentioning

confidence: 99%

The Implications of Increased Survivorship for Mortality Variation in Aging Populations

Engelman

Canudas-Romo

Agree

2010

Population & Development Rev

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“…Here, we focus on Gaussian Graphical Models (GGMs). Gaussianity is proposed to be a reasonable assumption according to its mathematical simplicity and its dominance in nature (Véron and Rohrbasser (2003); Uhler (2017)).…”

Section: Graphical Modelsmentioning

confidence: 99%

Multivariate Analysis and Visualization using R Package muvis

Heidari¹,

Balazadeh-Meresht²,

Sharifi‐Zarchi³

2018

Preprint

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Increased application of multivariate data in many scientific areas has considerably raised the complexity of analysis and interpretation. Although quite a few approaches have been suggested to address this issue, there is still a gap between the most efficient proposed methods and available software. muvis is an R package (core team ( 2017)) which is a toolkit for analyzing multivariate datasets. Several tools are implemented for common analyses of multivariate datasets, including preprocessing, dimensionality reduction, statistical analysis, Probabilistic Graphical Modeling, hypothesis testing, and visualization. Furthermore, we have implemented two novel methods-Variable-wise Kullback-Leibler Divergence (VKL) and Violating Variable-wise Kullback-Leibler Divergence (VVKL)-in muvis, which are proposed to find the features with most different probability distributions between two groups of samples. The main aim of the package is to provide a wide range of users with different levels of expertise in R with a set of tools for comprehensive analysis of multivariate datasets. We exploited the NHANES dataset to declare the functionality of muvis in practice.

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“…Adult life times were often assumed to follow a normal distribution (Véron and Rohrbasser (2003) citing Wilhelm Lexis) with standard deviation 9.3 (Ediev (2012) citing Wilhelm Lexis) and modal age at death 80 for modern populations. However, in the case a positively skewed Gompertz distribution as the support of the normal distribution is on (−∞, ∞), a significant portion of a likely alternative normal distribution's probability density would be on the negative axis.…”

Section: Power Of the Testsmentioning

confidence: 99%

Goodness-of-fit tests for the Gompertz distribution

Lénárt

Missov

2014

Communications in Statistics - Theory and Methods

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While the Gompertz distribution is often fitted to lifespan data, testing whether the fit satisfies theoretical criteria is being neglected. Here four goodness-of-fit measures-the Anderson-Darling statistic, the correlation coefficient test, a statistic using moments, and a nested test against the generalized extreme value distributions-are discussed. Along with an application to laboratory rat data, critical values calculated by the empirical distribution of the test statistics are also presented.

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Wilhelm Lexis: The Normal Length of Life as an Expression of the "Nature of Things"

Cited by 5 publications

References 0 publications

The Implications of Increased Survivorship for Mortality Variation in Aging Populations

The Implications of Increased Survivorship for Mortality Variation in Aging Populations

Multivariate Analysis and Visualization using R Package muvis

Goodness-of-fit tests for the Gompertz distribution

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