Evidence for Persistence and Long Memory Features in Mortality Data

2019 ◽  
Author(s):  
Hongxuan Yan ◽  
Gareth Peters ◽  
Jennifer Chan
Keyword(s):  
Long Memory ◽  
Mortality Data

Mortality models incorporating long memory for life table estimation: a comprehensive analysis

2021 ◽  
pp. 1-38
Author(s):  
Hongxuan Yan ◽  
Gareth W. Peters ◽  
Jennifer Chan
Keyword(s):  
Time Series ◽  
Life Expectancy ◽  
Long Memory ◽  
Age Groups ◽  
Deviance Information Criterion ◽  
Information Criterion ◽  
Life Tables ◽  
Mortality Data ◽  
Model Parameters ◽  
Mortality Models

Abstract Mortality projection and forecasting of life expectancy are two important aspects of the study of demography and life insurance modelling. We demonstrate in this work the existence of long memory in mortality data. Furthermore, models incorporating long memory structure provide a new approach to enhance mortality forecasts in terms of accuracy and reliability, which can improve the understanding of mortality. Novel mortality models are developed by extending the Lee–Carter (LC) model for death counts to incorporate a long memory time series structure. To link our extensions to existing actuarial work, we detail the relationship between the classical models of death counts developed under a Generalised Linear Model (GLM) formulation and the extensions we propose that are developed under an extension to the GLM framework known in time series literature as the Generalised Linear Autoregressive Moving Average (GLARMA) regression models. Bayesian inference is applied to estimate the model parameters. The Deviance Information Criterion (DIC) is evaluated to select between different LC model extensions of our proposed models in terms of both in-sample fits and out-of-sample forecasts performance. Furthermore, we compare our new models against existing models structures proposed in the literature when applied to the analysis of death count data sets from 16 countries divided according to genders and age groups. Estimates of mortality rates are applied to calculate life expectancies when constructing life tables. By comparing different life expectancy estimates, results show the LC model without the long memory component may provide underestimates of life expectancy, while the long memory model structure extensions reduce this effect. In summary, it is valuable to investigate how the long memory feature in mortality influences life expectancies in the construction of life tables.

scholarly journals Statistical features of persistence and long memory in mortality data

2021 ◽  
pp. 1-27
Author(s):  
Gareth W. Peters ◽  
Hongxuan Yan ◽  
Jennifer Chan
Keyword(s):  
Long Memory ◽  
Data Science ◽  
National Level ◽  
Age Groups ◽  
Mortality Data ◽  
Statistical Features ◽  
Statistical Feature ◽  
Mortality Projection ◽  
Functional Features ◽  
Memory Structures

Abstract Understanding core statistical properties and data features in mortality data are fundamental to the development of machine learning methods for demographic and actuarial applications of mortality projection. The study of statistical features in such data forms the basis for classification, regression and forecasting tasks. In particular, the understanding of key statistical structure in such data can aid in improving accuracy in undertaking mortality projection and forecasting when constructing life tables. The ability to accurately forecast mortality is a critical aspect for the study of demography, life insurance product design and pricing, pension planning and insurance-based decision risk management. Though many stylised facts of mortality data have been discussed in the literature, we provide evidence for a novel statistical feature that is pervasive in mortality data at a national level that is as yet unexplored. In this regard, we demonstrate in this work a strong evidence for the existence of long memory features in mortality data, and second that such long memory structures display multifractality as a statistical feature that can act as a discriminator of mortality dynamics by age, gender and country. To achieve this, we first outline the way in which we choose to represent the persistence of long memory from an estimator perspective. We make a natural link between a class of long memory features and an attribute of stochastic processes based on fractional Brownian motion. This allows us to use well established estimators for the Hurst exponent to then robustly and accurately study the long memory features of mortality data. We then introduce to mortality analysis the notion from data science known as multifractality. This allows us to study the long memory persistence features of mortality data on different timescales. We demonstrate its accuracy for sample sizes commensurate with national-level age term structure historical mortality records. A series of synthetic studies as well a comprehensive analysis of real mortality death count data are studied in order to demonstrate the pervasiveness of long memory structures in mortality data, both mono-fractal and multifractal functional features are verified to be present as stylised facts of national-level mortality data for most countries and most age groups by gender. We conclude by demonstrating how such features can be used in kernel clustering and mortality model forecasting to improve these actuarial applications.

MULTIVARIATE LONG-MEMORY COHORT MORTALITY MODELS

2019 ◽  
Vol 50 (1) ◽  
pp. 223-263 ◽  
Author(s):  
Hongxuan Yan ◽  
Gareth W. Peters ◽  
Jennifer S.K. Chan
Keyword(s):  
Long Memory ◽  
Cohort Effect ◽  
Pension Plans ◽  
Insurance Companies ◽  
Mortality Data ◽  
Data Sets ◽  
New Approach ◽  
Effect Model ◽  
Mortality Models ◽  
Proposed Model

AbstractThe existence of long memory in mortality data improves the understandings of features of mortality data and provides a new approach for establishing mortality models. The findings of long-memory phenomena in mortality data motivate us to develop new mortality models by extending the Lee–Carter (LC) model to death counts and incorporating long-memory model structure. Furthermore, there are no identification issues arising in the proposed model class. Hence, the constraints which cause many computational issues in LC models are removed. The models are applied to analyse mortality death count data sets from three different countries divided according to genders. Bayesian inference with various selection criteria is applied to perform the model parameter estimation and mortality rate forecasting. Results show that multivariate long-memory mortality model with long-memory cohort effect model outperforms multivariate extended LC cohort model in both in-sample fitting and out-sample forecast. Increasing the accuracy of forecasting of mortality rates and improving the projection of life expectancy is an important consideration for insurance companies and governments since misleading predictions may result in insufficient funds for retirement and pension plans.

Inequalities and Impact of Socioeconomic-Cultural Factors in Suicide Rates Across Italy

Crisis ◽  
2011 ◽  
Vol 32 (4) ◽  
pp. 178-185 ◽  
Author(s):  
Maurizio Pompili ◽  
Marco Innamorati ◽  
Monica Vichi ◽  
Maria Masocco ◽  
Nicola Vanacore ◽  
...  
Keyword(s):  
Suicide Rate ◽  
Age Groups ◽  
Premature Death ◽  
Mortality Rates ◽  
Mortality Data ◽  
Italian Population ◽  
Comprehensive Overview ◽  
Suicide Rates ◽  
Younger Age ◽  
Age And Sex

Background: Suicide is a major cause of premature death in Italy and occurs at different rates in the various regions. Aims: The aim of the present study was to provide a comprehensive overview of suicide in the Italian population aged 15 years and older for the years 1980–2006. Methods: Mortality data were extracted from the Italian Mortality Database. Results: Mortality rates for suicide in Italy reached a peak in 1985 and declined thereafter. The different patterns observed by age and sex indicated that the decrease in the suicide rate in Italy was initially the result of declining rates in those aged 45+ while, from 1997 on, the decrease was attributable principally to a reduction in suicide rates among the younger age groups. It was found that socioeconomic factors underlined major differences in the suicide rate across regions. Conclusions: The present study confirmed that suicide is a multifaceted phenomenon that may be determined by an array of factors. Suicide prevention should, therefore, be targeted to identifiable high-risk sociocultural groups in each country.

Term-Long Memory

1984 ◽  
Vol 29 (7) ◽  
pp. 576-577
Author(s):  
Leonard D. Stern
Keyword(s):  
Long Memory

Perbandingan Linieritas Dalam Pendeteksian Faktor-Faktor Yang Mempengaruhi Angka Kematian Bayi Di Provinsi Aceh

2019 ◽  
Vol 2 (2) ◽  
Author(s):  
Desfira Ahya ◽  
Inas Salsabila ◽  
Miftahuddin
Keyword(s):  
Mortality Rate ◽  
Infant Mortality ◽  
Linear Model ◽  
Health Workers ◽  
Additive Model ◽  
Infant Mortality Rate ◽  
Mortality Data ◽  
Important Indicator ◽  
Penalized Spline ◽  
Aceh Province

Angka Kematian Bayi/ Infant Mortality Rate (IMR) merupakan indikator penting dalam mengukur keberhasilan pengembangan kesehatan. Nilai IMR juga dapat digunakan untuk mengetahui tingkat kesehatan ibu, kondisi kesehatan lingkungan dan secara umum, tingkat pengembangan sosio-ekonomi masyarakat. Penelitian ini bertujuan untuk memperoleh model IMR terbaik menggunakan tiga pendekatan: Model Linear, Model Linear Tergeneralisir dan Model Aditif Tergeneralisir dengan basis P-spline. Sebagai tambahan, berdasarkan model tersebut akan terlihat variabel yang mempengaruhi tingkat kematian bayi di provinsi Aceh. Penelitian ini menggunakan data jumlah kematian bayi di tahun 2013-2015. Data dalam penelitian ini diperoleh dari Profil Kesehatan Aceh. Hasil menunjukkan bahwa model terbaik dalam menjelaskan angka kematian bayi di provinsi Aceh tahun 2013-2015 ialah Model Linear Tergeneralisir dengan basis P-spline menggunakan parameter penghalusan 100 dan titik knots 8. Faktor yang sangat mempengaruhi angka kematian ialah jumlah pekerja yang sehat.   Infant mortality rate (IMR) is an important indicator in measuring the success of health development. IMR also can be used to knowing the level of maternal health, environmental health conditions and generally the level of socio-economic development in community. This research aims to get the best model of infant mortality data using three approaches: Linear Model, Generalized Linear Model and Generalized Additive Model with Penalized Spline (P-spline) base. In addition, based on the model can be seen the variables that affect to infant mortality in Aceh Province. This research uses data number of infant mortality in Aceh Province period 2013-2015. The data in this research were obtained from Aceh’s Health Profile. The results show that the best model can be explain infant mortality rate in Aceh Province period 2013-2015 is GAM model with P-spline base using smoothing parameter 100 and knots 8. Factor that high effect to infant mortality is number of health workers.

Antiplatelet Therapy in Acute Coronary Syndromes

2010 ◽  
Vol 6 (1) ◽  
pp. 58
Author(s):  
Sasha Koul ◽  
David Erlinge ◽  
◽  
Keyword(s):  
Platelet Function ◽  
Acute Coronary Syndromes ◽  
New Drugs ◽  
Antiplatelet Drugs ◽  
Bleeding Complications ◽  
Great Promise ◽  
Mortality Data ◽  
Poor Responders ◽  
Coronary Syndromes

Drugs inhibiting platelet function play a major role in the treatment of acute coronary syndromes (ACS). The first drug used, which is still considered the cornerstone of therapy today, is aspirin. Although very impressive in acutely decreasing rates of myocardial infarction as well as death, long-term data are scarce, despite our current recommendation for lifelong aspirin. The thienopyridines, most notably clopidogrel, are the next line of antiplatelet drugs. Well-documented data support the usage of clopidogrel for non-STEMI-ACS (NSTE-ACS). Although positive mortality data exist regarding clopidogrel and STEMI patients in a medically treated population, including thrombolysis, no larger amounts of randomised data exist in a primary PCI setting. Poor responders to aspirin and/or clopidogrel are a clinical problem, with these individuals constituting a higherrisk group for recurrent ischaemic events. Whereas very little can be done regarding aspirin resistance, clopidogrel resistance might be diminished by increasing the dosage or changing to more potent and newer-generation antiplatelet drugs. The role of glycoprotein IIb/IIIa inhibitors has diminished drastically and instead paved the way for thrombin antagonists (bivalirudin), which have fewer bleeding complications with resulting better long-term mortality. Novel adenosine diphosphate (ADP)-receptor blockers such as prasugrel and ticagrelor have shown increased efficacy over clopidogrel and hold great promise for the future. However, not all patients may benefit from these new drugs and economic constraints may also limit their use. Platelet function tests could possibly help in identifying risk groups in need of stronger platelet inhibition.

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