Results 221 to 230 of about 29,512 (260)

Modeling and Forecasting U.S. Mortality

Journal of the American Statistical Association, 1992
Abstract Time series methods are used to make long-run forecasts, with confidence intervals, of age-specific mortality in the United States from 1990 to 2065. First, the logs of the age-specific death rates are modeled as a linear function of an unobserved period-specific intensity index, with parameters depending on age.
Ronald D. Lee, Lawrence R. Carter
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Forecasting Changes in Mortality

North American Actuarial Journal, 1998
In this article, we express a concern that certain commonly accepted methods of predicting mortality will likely prove to be inadequate in the future. Specifically, the Lee-Carter method, which overall has been empirically successful, uses auto-regressive moving average (ARMA) technology and contains no structural mortality equation.
Sam Gutterman, Irwin T. Vanderhoof
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Error Models for Official Mortality Forecasts

Journal of the American Statistical Association, 1990
"The Office of the Actuary, U.S. Social Security Administration, produces alternative forecasts of mortality to reflect uncertainty about the future.... In this article we identify the components and assumptions of the official forecasts and approximate them by stochastic parametric models.
J M, Alho, B D, Spencer
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Forecasting Mortality with Autoencoders: An Application to Italian Mortality Data

2023
Predictions of human survival probabilities are an extremely relevant topic in many fields of human activities and interests, including in particular the insurance field. The model considered the most reliable, and, for this reason, most widely used both in the literature and in practical applications, is the Lee–Carter model. In this paper, we propose
Michele La Rocca, Cira Perna, Marilena Sibillo, Antonio Vignes   +3 more
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Forecasting Mortality for Small Populations by Mixing Mortality Data

SSRN Electronic Journal, 2013
In this paper we address the problem of projecting mortality when data are severely affected by random fluctuations, due in particular to a small sample size, or when data are scanty. Such situations may emerge when dealing with small populations, such as small countries (possibly previously part of a larger country), a specific geographic area of a ...
A. Ahcan, D. Medved, A. Olivieri, PITACCO, ERMANNO   +3 more
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Forecasting mortality: An introduction

2009
Abstract This chapter aims at describing various methods proposed by actuaries and demographers for projecting mortality. Many of these have been actually used in the actuarial context, in particular for pricing and reserving in relation to life annuity products and pensions, and in the demographic field, mainly for population ...
Ermanno Pitacco, Michel Denuit, Steven Haberman, Annamaria Olivieri   +3 more
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Mortality forecasting: Neural network approach

2020 13th International Conference "Management of large-scale system development" (MLSD), 2020
Application of an artificial neural network (NN) for predicting human mortality and life expectancy has been studied. Obtained NN reflects current demographic theories, according to which mortality trends in countries with high life expectancy converge to a common trend, and the difference in the life expectancy of men and women is decreasing.
Vasily Gorlishchev, Anatoly Michalsky
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Smoothing and forecasting mortality rates

Statistical Modelling, 2004
The prediction of future mortality rates is a problem of fundamental importance for the insurance and pensions industry. We show how the method of P-splines can be extended to the smoothing and forecasting of two-dimensional mortality tables. We use a penalized generalized linear model with Poisson errors and show how to construct regression and ...
Currie, Iain D., Durban, Maria, Eilers, Paul H. C.   +2 more
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Statistical methods for forecasting mortality

2012
Thesis - Athens University of Economics and Business.
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Stochastic Forecast of Mortality Effects

SSRN Electronic Journal, 2011
In this study we propose a stochastic mortality forecast model that may be viewed as a Levy process. First, age, period and cohort effects are objectively identified in a given matrix of historic mortality data. Next, these patterns are removed from the matrix of mortality improvement rates.
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