The Experts below are selected from a list of 255897 Experts worldwide ranked by ideXlab platform
Uwe Sunde - One of the best experts on this subject based on the ideXlab platform.
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life expectancy schooling and lifetime labor supply theory and evidence revisited
Econometrica, 2013Co-Authors: Matteo Cervellati, Uwe SundeAbstract:This paper presents a theoretical and empirical analysis of the role of life expectancy for optimal schooling and lifetime labor supply. The results of a simple prototype Ben-Porath model with age-specific Survival rates show that an increase in lifetime labor supply is not a necessary, or a sufficient, condition for greater life expectancy to increase optimal schooling. The observed increase in Survival rates during working ages that follows from the "rectangularization" of the Survival Function is crucial for schooling and labor supply. The empirical results suggest that the relative benefits of schooling have been increasing across cohorts of U.S. men born between 1840 and 1930. A simple quantitative analysis shows that a realistic shift in the Survival Function can lead to an increase in schooling and a reduction in lifetime labor hours. [PUBLICATION ABSTRACT]
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life expectancy schooling and lifetime labor supply theory and evidence revisited
Social Science Research Network, 2013Co-Authors: Matteo Cervellati, Uwe SundeAbstract:This paper presents a theoretical and empirical analysis of the role of life expectancy for optimal schooling and lifetime labor supply. The results of a simple prototype Ben-Porath model with age-specific Survival rates show that an increase in lifetime labor supply is not a necessary, nor a sufficient, condition for greater life expectancy to increase optimal schooling. The observed increase in Survival rates during working ages that follows from the "rectangularization'' of the Survival Function is crucial for schooling and labor supply. The empirical results suggest that the relative benefits of schooling have been increasing across cohorts of US man born 1840-1930. A simple quantitative analysis shows that a realistic shift in the Survival Function can lead to an increase in schooling and a reduction in lifetime labor hours.
Matteo Cervellati - One of the best experts on this subject based on the ideXlab platform.
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life expectancy schooling and lifetime labor supply theory and evidence revisited
Econometrica, 2013Co-Authors: Matteo Cervellati, Uwe SundeAbstract:This paper presents a theoretical and empirical analysis of the role of life expectancy for optimal schooling and lifetime labor supply. The results of a simple prototype Ben-Porath model with age-specific Survival rates show that an increase in lifetime labor supply is not a necessary, or a sufficient, condition for greater life expectancy to increase optimal schooling. The observed increase in Survival rates during working ages that follows from the "rectangularization" of the Survival Function is crucial for schooling and labor supply. The empirical results suggest that the relative benefits of schooling have been increasing across cohorts of U.S. men born between 1840 and 1930. A simple quantitative analysis shows that a realistic shift in the Survival Function can lead to an increase in schooling and a reduction in lifetime labor hours. [PUBLICATION ABSTRACT]
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life expectancy schooling and lifetime labor supply theory and evidence revisited
Social Science Research Network, 2013Co-Authors: Matteo Cervellati, Uwe SundeAbstract:This paper presents a theoretical and empirical analysis of the role of life expectancy for optimal schooling and lifetime labor supply. The results of a simple prototype Ben-Porath model with age-specific Survival rates show that an increase in lifetime labor supply is not a necessary, nor a sufficient, condition for greater life expectancy to increase optimal schooling. The observed increase in Survival rates during working ages that follows from the "rectangularization'' of the Survival Function is crucial for schooling and labor supply. The empirical results suggest that the relative benefits of schooling have been increasing across cohorts of US man born 1840-1930. A simple quantitative analysis shows that a realistic shift in the Survival Function can lead to an increase in schooling and a reduction in lifetime labor hours.
Ioannis D Bassukas - One of the best experts on this subject based on the ideXlab platform.
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parametric estimation of the risk of melanoma related death with the recursion formula of the gompertz Survival Function
Melanoma Research, 1996Co-Authors: Ioannis D Bassukas, A Lippold, M HundeikerAbstract:Parametric modelling of Survival data is the only reliable method for deriving prognostic dimensions from epidemiological population data. This method has, however, a serious limitation, which has made it - in contrast to demographic studies - less popular in clinical epidemiology: its application requires great numbers of individuals at risk. Since malignant melanoma of the skin is becoming more frequent in recent years this disease is also becoming available to corresponding parametric analyses of Survival.
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use of the recursion formula of the gompertz Survival Function to evaluate life table data
Mechanisms of Ageing and Development, 1996Co-Authors: Ioannis D BassukasAbstract:Abstract The recursion formula of the Gompertz Function is an established method for the analysis of growth processes. In the present study the recursion formula of the Gompertz Survival Function ln S ( t + s ) = a + b × ln S ( t ) is introduced for the analysis of Survival data, where S ( t ) is the Survival fraction at age t , s is the constant age increment between two consecutive measurements of the Survival fraction and a and b are parameters. With the help of this method - and provided stroboscopical measurements of rates of Survival are available — the Gompertz Survival Function, instead of the corresponding mortality Function, can be determined directly using linear regression analysis. The application of the present algorithm is demonstrated by analysing two sets of data taken from the literature (Survival of Drosophila imagoes and of female centenarians) using linear regression analysis to fit Survival or mortality rates to the corresponding models. In both cases the quality of fit was superior by using the algorithm presently introduced. Moreover, Survival Functions calculated from the fits to the mortality law only poorly predict the Survival data. On the contrary, the results of the present method not only fit to the measurements, but, for both sets of data the mortality parameters calculated by the present method are essentially identical to those obtained by a corresponding application of a non-linear Marquardt-Levenberg algorithm to fit the same sets of data to the explicit form of the Gompertz Survival Function. Taking into consideration the advantages of using a linear fit (goodness-of-fit test and efficient statistical comparison of Survival patterns) the method of the recursion formula of the Gompertz Survival Functions is the most preferable method to fit Survival data to the Gompertz Function.
David E Matthews - One of the best experts on this subject based on the ideXlab platform.
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maximum likelihood estimation of a Survival Function with a change point for truncated and interval censored data
Statistics in Medicine, 2002Co-Authors: Heejeong Lim, Jianguo Sun, David E MatthewsAbstract:This paper considers estimation of a Survival Function when there exists a change point and the Survival time of interest is defined as elapsed time between two related events. Furthermore, there exists censoring on observations on the occurrences of both events and truncation on observations on the occurrence of the second event and thus the Survival time of interest. To obtain the maximum likelihood estimator of a Survival Function, an EM algorithm is developed when the Survival Function is completely unknown before the change point and known up to a vector of unknown parameters after the change point. The idea is a generalization of that discussed in Moeschberger and Klein. Simulations and an example are used to evaluate and illustrate the algorithm.
John Oquigley - One of the best experts on this subject based on the ideXlab platform.
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proportional hazards estimate of the conditional Survival Function
Journal of The Royal Statistical Society Series B-statistical Methodology, 2000Co-Authors: John OquigleyAbstract:Summary. We introduce a new estimator of the conditional Survival Function given some subset of the covariate values under a proportional hazards regression. The new estimate does not require estimating the base-line cumulative hazard Function. An estimate of the variance is given and is easy to compute, involving only those quantities that are routinely calculated in a Cox model analysis. The asymptotic normality of the new estimate is shown by using a central limit theorem for Kaplan-Meier integrals. We indicate the straightforward extension of the estimation procedure under models with multiplicative relative risks, including non-proportional hazards, and to stratified and frailty models. The estimator is applied to a gastric cancer study where it is of interest to predict patients' Survival based only on measurements obtained before surgery, the time at which the most important prognostic variable, stage, becomes known.
