Markov (Set) chains application to predict mortality rates using extended Milevsky–Promislov generalized mortality models

2021 ◽  
pp. 1-21
Author(s):  
Piotr Sliwka
Keyword(s):  
Mortality Rates ◽  
Mortality Models

scholarly journals Identifiability in age/period/cohort mortality models

2020 ◽  
Vol 14 (2) ◽  
pp. 500-536 ◽  
Author(s):  
Andrew Hunt ◽  
David Blake
Keyword(s):  
Mortality Rates ◽  
Comprehensive Analysis ◽  
Mortality Model ◽  
Mortality Models ◽  
The Future

AbstractThe addition of a set of cohort parameters to a mortality model can generate complex identifiability issues due to the collinearity between the dimensions of age, period and cohort. These issues can lead to robustness problems and difficulties making projections of future mortality rates. Since many modern mortality models incorporate cohort parameters, we believe that a comprehensive analysis of the identifiability issues in age/period/cohort mortality models is needed. In this paper, we discuss the origin of identifiability issues in general models before applying these insights to simple but commonly used mortality models. We then discuss how to project mortality models so that our forecasts of the future are independent of any arbitrary choices we make when fitting a model to data in order to identify the historical parameters.

scholarly journals Mortality Forecasting: How Far Back Should We Look in Time?

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 22 ◽  
Author(s):  
Han Li ◽  
Colin O’Hare
Keyword(s):  
Predictive Power ◽  
Historical Data ◽  
Mortality Rates ◽  
Point Of View ◽  
Mortality Data ◽  
Male Mortality ◽  
Historical Information ◽  
Mortality Forecasting ◽  
Mortality Models ◽  
Forecasting Performance

Extrapolative methods are one of the most commonly-adopted forecasting approaches in the literature on projecting future mortality rates. It can be argued that there are two types of mortality models using this approach. The first extracts patterns in age, time and cohort dimensions either in a deterministic fashion or a stochastic fashion. The second uses non-parametric smoothing techniques to model mortality and thus has no explicit constraints placed on the model. We argue that from a forecasting point of view, the main difference between the two types of models is whether they treat recent and historical information equally in the projection process. In this paper, we compare the forecasting performance of the two types of models using Great Britain male mortality data from 1950–2016. We also conduct a robustness test to see how sensitive the forecasts are to the changes in the length of historical data used to calibrate the models. The main conclusion from the study is that more recent information should be given more weight in the forecasting process as it has greater predictive power over historical information.

scholarly journals Mortality Projections for Small Populations: An Application to the Maltese Elderly

Risks ◽  
2019 ◽  
Vol 7 (2) ◽  
pp. 35
Author(s):  
Massimiliano Menzietti ◽  
Maria Morabito ◽  
Manuela Stranges
Keyword(s):  
Missing Data ◽  
Older Adult ◽  
Reference Population ◽  
Mortality Rates ◽  
Adult Mortality ◽  
Small Population ◽  
Small Populations ◽  
Small Country ◽  
Mortality Models ◽  
Mortality Projections

In small populations, mortality rates are characterized by a great volatility, the datasets are often available for a few years and suffer from missing data. Therefore, standard mortality models may produce high uncertain and biologically improbable projections. In this paper, we deal with the mortality projections of the Maltese population, a small country with less than 500,000 inhabitants, whose data on exposures and observed deaths suffers from all the typical problems of small populations. We concentrate our analysis on older adult mortality. Starting from some recent suggestions in the literature, we assume that the mortality of a small population can be modeled starting from the mortality of a bigger one (the reference population) adding a spread. The first part of the paper is dedicated to the choice of the reference population, then we test alternative mortality models. Finally, we verify the capacity of the proposed approach to reduce the volatility of the mortality projections. The results obtained show that the model is able to significantly reduce the uncertainty of projected mortality rates and to ensure their coherent and biologically reasonable evolution.

scholarly journals Identifying Structural Breaks in Stochastic Mortality Models

2015 ◽  
Vol 1 (2) ◽  
Author(s):  
Colin O’Hare ◽  
Youwei Li
Keyword(s):  
Life Expectancy ◽  
Structural Breaks ◽  
Structural Break ◽  
Mortality Rates ◽  
Insurance Companies ◽  
Mortality Data ◽  
Stochastic Mortality ◽  
Stochastic Mortality Models ◽  
Mortality Models ◽  
Mortality Forecasts

In recent years, the issue of life expectancy has become of utmost importance to pension providers, insurance companies, and government bodies in the developed world. Significant and consistent improvements in mortality rates and hence life expectancy have led to unprecedented increases in the cost of providing for older ages. This has resulted in an explosion of stochastic mortality models forecasting trends in mortality data to anticipate future life expectancy and hence quantify the costs of providing for future aging populations. Many stochastic models of mortality rates identify linear trends in mortality rates by time, age, and cohort and forecast these trends into the future by using standard statistical methods. These approaches rely on the assumption that structural breaks in the trend do not exist or do not have a significant impact on the mortality forecasts. Recent literature has started to question this assumption. In this paper, we carry out a comprehensive investigation of the presence or of structural breaks in a selection of leading mortality models. We find that structural breaks are present in the majority of cases. In particular, we find that allowing for structural break, where present, improves the forecast result significantly.

scholarly journals Novel COVID-19 Mortality Rate Prediction (MRP) Model for India Using Regression Model With Optimized Hyperparameter

2021 ◽  
Vol 23 (4) ◽  
pp. 1-12
Author(s):  
Dhamodharavadhani S. ◽  
R. Rathipriya
Keyword(s):  
Mortality Rate ◽  
Regression Model ◽  
Death Rate ◽  
Historical Data ◽  
Gaussian Process Regression ◽  
Mortality Rates ◽  
Regression Method ◽  
Rate Prediction ◽  
Mortality Models ◽  
The U.S

The main objective of this study is to estimate the future COVID-19 mortality rate for India using COVID-19 mortality rate models from different countries. Here, the regression method with the optimal hyperparameter is used to build these models. In the literature, numerous mortality models for infectious diseases have been proposed, most of which predict future mortality by extending one or more disease-related attributes or parameters. But most of these models predict mortality rates from historical data. In this paper, the Gaussian process regression model with the optimal hyperparameter is used to develop the COVID-19 mortality rate prediction (MRP) model. Five different MRP models have been built for the U.S., Italy, Germany, Japan, and India. The results show that Germany has the lowest death rate in 2000 plus COVID-19 confirmed cases. Therefore, if India follows the strategy pursued by Germany, India will control the COVID-19 mortality rate even in the increase of confirmed cases.

scholarly journals Application of Machine Learning to Mortality Modeling and Forecasting

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 26 ◽  
Author(s):  
Susanna Levantesi ◽  
Virginia Pizzorusso
Keyword(s):  
Machine Learning ◽  
Stochastic Models ◽  
Goodness Of Fit ◽  
Mortality Rates ◽  
Machine Learning Algorithms ◽  
Stochastic Mortality ◽  
Life Insurers ◽  
Stochastic Mortality Models ◽  
Mortality Models ◽  
Out Of Sample

Estimation of future mortality rates still plays a central role among life insurers in pricing their products and managing longevity risk. In the literature on mortality modeling, a wide number of stochastic models have been proposed, most of them forecasting future mortality rates by extrapolating one or more latent factors. The abundance of proposed models shows that forecasting future mortality from historical trends is non-trivial. Following the idea proposed in Deprez et al. (2017), we use machine learning algorithms, able to catch patterns that are not commonly identifiable, to calibrate a parameter (the machine learning estimator), improving the goodness of fit of standard stochastic mortality models. The machine learning estimator is then forecasted according to the Lee-Carter framework, allowing one to obtain a higher forecasting quality of the standard stochastic models. Out-of sample forecasts are provided to verify the model accuracy.

On the Problem of Maximum Yield from North Pacific Sockeye Salmon Stocks

1963 ◽  
Vol 20 (6) ◽  
pp. 1371-1396 ◽  
Author(s):  
Robert R. Parker
Keyword(s):  
North Pacific ◽  
Sockeye Salmon ◽  
Maximum Yield ◽  
Mortality Rates ◽  
Natural Mortality ◽  
Instantaneous Growth Rate ◽  
Mortality Models ◽  
Back Calculation ◽  
Diverse Data ◽  
Pelagic Fishing

If it is assumed that rates of exploitation, regardless of where fishing takes place, can be adjusted to the needs for reproduction, maximum yield from stocks of north Pacific sockeye salmon (Oncorhynchus nerka) will then be a function of growth and natural mortality rates. Events during the last decade have demonstrated the vulnerability of this species to pelagic fishing during the penultimate and ultimate years of life. The instantaneous growth rate on a per-month basis (g) is estimated from diverse data by back calculation, tag and recapture, and size-at-time methods. A general consistency of results is apparent regardless of the method utilized. For 2-ocean annuli life history types, penultimate [Formula: see text], ultimate [Formula: see text]. For 3-ocean annuli types, penultimate g = 0.059, ultimate [Formula: see text]. Ocean mortality models and rates derived by several researchers are reviewed and discussed. Using those proposed by the author, it is shown that the stock mass, hence the allowable yield, increases throughout the penultimate and ultimate years in the sea and reaches a maximum when the stocks approach the coast on the spawning migration. A conservative estimate of the potential increment, based on the 1957–1959 pelagic catches, is 14.2%. It is concluded that the yield of these stocks is seriously depressed by pelagic fishing.

A COMPARISON OF PARAMETRIC MORTALITY MODELS TO GRADUATE URBAN AND RURAL MORTALITY

2021 ◽  
Vol 37 (04) ◽  
pp. 485-497
Author(s):  
Mushtaq Ahmad Khan Barakzai ◽  
Aqil Burney
Keyword(s):  
Loss Function ◽  
Life Insurance ◽  
Rural Areas ◽  
Rural Population ◽  
Urban Areas ◽  
Mortality Rates ◽  
Mortality Data ◽  
Rural And Urban ◽  
Mortality Models ◽  
Urban And Rural Areas

This study examine twenty-nine parametric mortality models and assess their suitability for graduating mortality rates of urban and rural areas in Pakistan. Grouped age specific mortality rates of rural and urban populations for the year 2019 are used. The data is collected from the website of National Institute of Population Studies which conduct Maternal Mortality Survey in Pakistan on regular basis. The parametric mortality models were applied to rural and urban mortality data. We used R software to estimate the model’s parameters and assess their suitability for urban and rural populations. The suitability of these models was assessed by using 3 different loss functions. Our analyses found that the fourth type of Heligman-Polard’s model with loss function 3 provides reliable results for graduating the mortality of rural population while second type of Carriere model with loss function 3 produce best results for graduating the urban mortality of Pakistan. Based on two models, mortality rates of urban and rural population have been graduated over age range 0-85. We suggest the use the graduated mortality rates of urban and rural areas for pricing life insurance products in rural and urban areas respectively. In addition, graduated mortality rates are also suggested for use in calculation of life insurance liabilities.

scholarly journals A multi-country comparison of stochastic models of breast cancer mortality with P-splines smoothing approach

2020 ◽  
Vol 20 (1) ◽  
Author(s):  
Sumaira Mubarik ◽  
Ying Hu ◽  
Chuanhua Yu
Keyword(s):  
Breast Cancer ◽  
Stochastic Models ◽  
Forecast Accuracy ◽  
Mortality Rates ◽  
The Other ◽  
Mortality Data ◽  
Demographic Model ◽  
Single Model ◽  
Life Expectancy At Birth ◽  
Mortality Models

Abstract Background Precise predictions of incidence and mortality rates due to breast cancer (BC) are required for planning of public health programs as well as for clinical services. A number of approaches has been established for prediction of mortality using stochastic models. The performance of these models intensely depends on different patterns shown by mortality data in different countries. Methods The BC mortality data is retrieved from the Global burden of disease (GBD) study 2017 database. This study include BC mortality rates from 1990 to 2017, with ages 20 to 80+ years old women, for different Asian countries. Our study extend the current literature on Asian BC mortality data, on both the number of considered stochastic mortality models and their rigorous evaluation using multivariate Diebold-Marino test and by range of graphical analysis for multiple countries. Results Study findings reveal that stochastic smoothed mortality models based on functional data analysis generally outperform on quadratic structure of BC mortality rates than the other lee-carter models, both in term of goodness of fit and on forecast accuracy. Besides, smoothed lee carter (SLC) model outperform the functional demographic model (FDM) in case of symmetric structure of BC mortality rates, and provides almost comparable results to FDM in within and outside data forecast accuracy for heterogeneous set of BC mortality rates. Conclusion Considering the SLC model in comparison to the other can be obliging to forecast BC mortality and life expectancy at birth, since it provides even better results in some cases. In the current situation, we can assume that there is no single model, which can truly outperform all the others on every population. Therefore, we also suggest generating BC mortality forecasts using multiple models rather than relying upon any single model.

scholarly journals Modelling Nigeria Male Mortality Using Functional Data Time Series Analysis Approach.

2021 ◽  
Author(s):  
Jean Bosco NDIKUBWIMANA ◽  
LAWAL F.K ◽  
James KARAMUZI ◽  
Angelique DUKUNDE ◽  
Evariste GATABAZI ◽  
...  
Keyword(s):  
Functional Data ◽  
Research Work ◽  
Mortality Rates ◽  
Male Mortality ◽  
Analysis Techniques ◽  
Mortality Models ◽  
Incidence And Mortality ◽  
Public Health Strategies ◽  
Carter Model ◽  
Better Than

Abstract Incidence and mortality rates are considered as a guideline for planning public health strategies and allocating resources. Several methods have been proposed and used for modeling mortalities of various countries. Among the leading mortality, models are the Lee-Carter model which has been used in various countries and adjudged to fit the mortality of these countries well. But it came with its own limitations as the model was used in a more developed nation. In this research work, we propose functional data analysis techniques to model Nigerian Male mortality using the data obtained from the Nigeria Bureau of Statistics from 1998-2010. We compared the results obtained using some parameters such as MAPE and MSE. From the results, we discovered that the improvement of the parameters of our model shows that it is better than the Lee-Carter model in analyzing Nigerian Male Mortality.

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