scholarly journals A Three-Component Approach to Model and Forecast Age-at-Death Distributions

2020 ◽  
pp. 105-129
Author(s):  
Ugofilippo Basellini ◽  
Carlo Giovanni Camarda
Keyword(s):  
Life Table ◽  
Early Adulthood ◽  
Forecast Accuracy ◽  
Relational Model ◽  
Age At Death ◽  
Mortality Pattern ◽  
Mortality Forecasting ◽  
Component Approach ◽  
Using Data ◽  
Mortality Projections

Abstract Mortality forecasting has recently received growing interest, as accurate projections of future lifespans are needed to ensure the solvency of insurance and pension providers. Several innovative stochastic methodologies have been proposed in most recent decades, the majority of them being based on age-specific mortality rates or on summary measures of the life table. The age-at-death distribution is an informative life-table function that provides readily available information on the mortality pattern of a population, yet it has been mostly overlooked for mortality projections. In this chapter, we propose to analyse and forecast mortality developments over age and time by introducing a novel methodology based on age-at-death distributions. Our approach starts from a nonparametric decomposition of the mortality pattern into three independent components corresponding to Childhood, Early-Adulthood and Senescence, respectively. We then model the evolution of each component-specific death density with a relational model that associates a time-invariant standard to a series of observed distributions by means of a transformation of the age axis. Our approach allows us to capture mortality developments over age and time, and forecasts can be derived from parameters’ extrapolation using standard time series models. We illustrate our methods by estimating and forecasting the mortality pattern of females and males in two high-longevity countries using data of the Human Mortality Database. We compare the forecast accuracy of our model and its projections until 2050 with three other forecasting methodologies.

scholarly journals Mathematical Development and Evaluation of Forecasting Models for Accuracy of Inflation in Developing Countries: A Case of Vietnam

2015 ◽  
Vol 2015 ◽  
pp. 1-14 ◽  
Author(s):  
Nhu-Ty Nguyen ◽  
Thanh-Tuyen Tran
Keyword(s):  
Raw Materials ◽  
Arima Model ◽  
Consumer Price Index ◽  
Forecast Accuracy ◽  
Complex Problem ◽  
Forecast Performance ◽  
Forecasting Models ◽  
Out Of Sample ◽  
Forecasting Inflation ◽  
Using Data

Inflation is a key element of a national economy, and it is also a prominent and important issue influencing the whole economy in terms of marketing. This is a complex problem requiring a large investment of time and wisdom to attain positive results. Thus, appropriate tools for forecasting inflation variables are crucial significant for policy making. In this study, both clarified value calculation and use of a genetic algorithm to find the optimal parameters are adopted simultaneously to construct improved models: ARIMA, GM(1,1), Verhulst, DGM(1,1), and DGM(2,1) by using data of Vietnamese inflation output from January 2005 to November 2013. The MAPE, MSE, RMSE, and MAD are four criteria with which the various forecasting models results are compared. Moreover, to see whether differences exist, Friedman and Wilcoxon tests are applied. Both in-sample and out-of-sample forecast performance results show that the ARIMA model has highly accurate forecasting in Raw Materials Price (RMP) and Gold Price (GP), whereas, the calculated results of GM(1,1) and DGM(1,1) are suitable to forecast Consumer Price Index (CPI). Therefore, the ARIMA, GM(1,1), and DGM(1,1) can handle the forecast accuracy of the issue, and they are suitable in modeling and forecasting of inflation in the case of Vietnam.

scholarly journals Data assimilation for volcanic ash plumes using a satellite observational operator: a case study on the 2010 Eyjafjallajökull volcanic eruption

2017 ◽  
Vol 17 (2) ◽  
pp. 1187-1205 ◽  
Author(s):  
Guangliang Fu ◽  
Fred Prata ◽  
Hai Xiang Lin ◽  
Arnold Heemink ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Volcanic Ash ◽  
Three Dimensional ◽  
Forecast Accuracy ◽  
Improve Model ◽  
Powerful Approach ◽  
Using Data ◽  
D Model

Abstract. Using data assimilation (DA) to improve model forecast accuracy is a powerful approach that requires available observations. Infrared satellite measurements of volcanic ash mass loadings are often used as input observations for the assimilation scheme. However, because these primary satellite-retrieved data are often two-dimensional (2-D) and the ash plume is usually vertically located in a narrow band, directly assimilating the 2-D ash mass loadings in a three-dimensional (3-D) volcanic ash model (with an integral observational operator) can usually introduce large artificial/spurious vertical correlations.In this study, we look at an approach to avoid the artificial vertical correlations by not involving the integral operator. By integrating available data of ash mass loadings and cloud top heights, as well as data-based assumptions on thickness, we propose a satellite observational operator (SOO) that translates satellite-retrieved 2-D volcanic ash mass loadings to 3-D concentrations. The 3-D SOO makes the analysis step of assimilation comparable in the 3-D model space.Ensemble-based DA is used to assimilate the extracted measurements of ash concentrations. The results show that satellite DA with SOO can improve the estimate of volcanic ash state and the forecast. Comparison with both satellite-retrieved data and aircraft in situ measurements shows that the effective duration of the improved volcanic ash forecasts for the distal part of the Eyjafjallajökull volcano is about 6 h.

scholarly journals FITTING OF AGE-SPECIFIC MORTALITY

2019 ◽  
Vol 7 (7) ◽  
pp. 405-415
Author(s):  
A.S. Talawar ◽  
Rajani P. Agadi
Keyword(s):  
Parameter Estimation ◽  
Life Table ◽  
Total Population ◽  
Mortality Rates ◽  
Demographic Characteristics ◽  
Parametric Models ◽  
Model Fit ◽  
Survival Functions ◽  
Mortality Pattern ◽  
Specific Mortality

The age-pattern of mortality can be represented by various parametric models. In the present paper we consider a mixture of Weibull, Inverse-Weibull, and Gompertz-Makeham (GoMa) survival functions and Heligman–Pollard model to fit U.S. life table 2014.  We use loss criterion for parameter estimation and demonstrate fitting of model. Both mixture and Heligman–Pollard model fit the mortality pattern reasonably well up to age 90.  We notice that the estimated mortality rates fit the actual pattern fairly well, although the fit at the earlier ages could be better. We have obtained the plots using our estimated values. The plots for mortality pattern of total population and other demographic characteristics (sex and race) are also considered.

scholarly journals Statistical Methods for Predicting Malaria Incidences Using Data from Sudan

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Hamid H. Hussien ◽  
Fathy H. Eissa ◽  
Khidir E. Awadalla
Keyword(s):  
Time Series ◽  
Malaria Incidence ◽  
Moving Average ◽  
Forecast Accuracy ◽  
Transformation Method ◽  
Transformation Model ◽  
Future Research ◽  
Incidence Data ◽  
Moving Average Model ◽  
Using Data

Malaria is the leading cause of illness and death in Sudan. The entire population is at risk of malaria epidemics with a very high burden on government and population. The usefulness of forecasting methods in predicting the number of future incidences is needed to motivate the development of a system that can predict future incidences. The objective of this paper is to develop applicable and understood time series models and to find out what method can provide better performance to predict future incidences level. We used monthly incidence data collected from five states in Sudan with unstable malaria transmission. We test four methods of the forecast: (1) autoregressive integrated moving average (ARIMA); (2) exponential smoothing; (3) transformation model; and (4) moving average. The result showed that transformation method performed significantly better than the other methods for Gadaref, Gazira, North Kordofan, and Northern, while the moving average model performed significantly better for Khartoum. Future research should combine a number of different and dissimilar methods of time series to improve forecast accuracy with the ultimate aim of developing a simple and useful model for producing reasonably reliable forecasts of the malaria incidence in the study area.

The Moderating Effect of Industry Concentration on the Relations Between External Attributes and the Properties of Analyst Earnings Forecast

2013 ◽  
Vol 16 (03) ◽  
pp. 1350019 ◽  
Author(s):  
Yu-Cheng Chen ◽  
Chiung-Yao Huang ◽  
Pei-I Chou
Keyword(s):  
Forecast Accuracy ◽  
Earnings Forecasts ◽  
Moderating Effect ◽  
Industry Concentration ◽  
Earnings Forecast ◽  
Moderating Effects ◽  
Forecast Dispersion ◽  
Earnings Surprise ◽  
Using Data ◽  
Analyst Earnings

Based on the work of earlier studies, the main objective of this study is to determine whether the properties of analyst earnings forecast are related to the interaction effects of external attributes and industry concentration that were not the focus of previous research. Specifically, this study examines the relations between external attributions and the properties of analyst earnings forecasts. Furthermore, we explore the moderating effect of industry concentration on the relations between external attributions and the properties of analyst earnings forecasts. Using data from Compustat and I/B/E/S, we provide evidence that analysts' earnings forecast accuracy is lower and the forecast dispersion is larger for firms with more earnings surprise. Firms with more analysts' forecasts covering are associated with higher forecast accuracy, but not necessarily higher forecast dispersion. The moderating effects of industry concentration on the relationships between earnings surprise, the number of estimates covering the company and forecast accuracy are particularly strong. In addition, the moderating effects of industry concentration on the relationship between earnings surprise, the number of estimates covering the company and the forecast dispersion are partially supported. Overall, the industrial concentration factor either magnifies or alleviates the effect of external attributions on analyst's forecast accuracy and forecast dispersion.

scholarly journals Multiple Sclerosis Mortality Rates in Canada, 1975-2009

2015 ◽  
Vol 43 (1) ◽  
pp. 134-141 ◽  
Author(s):  
Sharon A. Warren ◽  
Wonita Janzen ◽  
Kenneth G. Warren ◽  
Lawrence W. Svenson ◽  
Donald P. Schopflocher
Keyword(s):  
Multiple Sclerosis ◽  
General Population ◽  
Age Distribution ◽  
Mortality Rates ◽  
Age Group ◽  
Age At Death ◽  
Annual Percent Change ◽  
All Cause Mortality ◽  
Statistics Canada ◽  
Using Data

ABSTRACTBackground: This study examined mortality due to multiple sclerosis (MS) in Canada, 1975-2009 to determine whether there has been a change in age at death relative to the general population and decrease in MS mortality rates. Methods: Mortality rates/100,000 population for MS and all causes were calculated using data derived from Statistics Canada, age-standardized to the 2006 population. Results: The average annual Canadian MS mortality rate, 1975-2009 was 1.23/100,000. Five-year rates for 1975-79, 1980-84, 1985-89, 1990-94, 1995-99, 2000-04, 2005-09 were: 1.16, 0.94, 1.01, 1.16, 1.30, 1.43, 1.33. Trend analysis showed mortality rates over the entire 35 years were stable (average annual percent change of less than one percent). The average annual 1975-2009 rates for females and males were 1.45 and 0.99. Five-year female rates were always higher than males. Regardless of gender, there was a decrease in MS mortality rates in the 0-39 age group and increases in the 60-69, 70-79, and 80+ groups over time. In contrast, there were decreases in all-cause mortality rates across each age group. The highest MS mortality rates for 1975-2009 were consistently in the 50-59 and 60-69 groups for both genders, while the highest all-cause mortality rates were in the 80+ group. Conclusions: Changes in the age distribution of MS mortality rates indicate a shift to later age at death, possibly due to improved health care. However MS patients remain disadvantaged relative to the general population and changes in age at death are not reflected in decreased mortality rates.

scholarly journals Dimensions of social capital and life adjustment in the transition to early adulthood

2011 ◽  
Vol 35 (6) ◽  
pp. 482-489 ◽  
Author(s):  
Gregory S. Pettit ◽  
Stephen A. Erath ◽  
Jennifer E. Lansford ◽  
Kenneth A. Dodge ◽  
John E. Bates
Keyword(s):  
Social Capital ◽  
Family Income ◽  
Early Adulthood ◽  
Adult Life ◽  
Romantic Partners ◽  
Behavioral Adjustment ◽  
Life Transition ◽  
Prospective Longitudinal Study ◽  
Using Data ◽  
Prospective Longitudinal

The predictive relations between social capital depth (high-quality relationships across contexts) and breadth (friendship network extensivity) and early-adult life adjustment outcomes were examined using data from a prospective longitudinal study. Interviews at age 22 yielded (a) psychometrically sound indexes of relationship quality with parents, peers, and romantic partners that served as indicators of a latent construct of social capital depth, and (b) a measure of number of close friends. In follow-up interviews at age 24, participants reported on their behavioral adjustment, educational attainment, and arrests and illicit substance use. Early-adolescent assessments of behavioral adjustment and academic performance served as controls; data on what were construed as interpersonal assets (teacher-rated social skills) and opportunities (family income) were also collected at this time. Results showed that depth was associated with overall better young-adult adjustment, net of prior adjustment, and assets and opportunities. Breadth was only modestly associated with later outcomes, and when its overlap with depth was taken into account, breadth predicted higher levels of subsequent externalizing problems. These findings are consistent with the notion that social capital is multidimensional and that elements of it confer distinct benefits during an important life transition.

Mortality among the Widowed in Sweden

1982 ◽  
Vol 10 (2) ◽  
pp. 33-41 ◽  
Author(s):  
Dan Mellström ◽  
Åke Nilsson ◽  
Anders Odén ◽  
Åke Rundgren ◽  
Alvar Svanborg
Keyword(s):  
Marital Status ◽  
Population Study ◽  
Excess Mortality ◽  
Time Interval ◽  
Age Group ◽  
Mortality Pattern ◽  
Risk Of Death ◽  
Married Men ◽  
The Difference ◽  
Using Data

This study consists of three parts. In the first part the risk of death for widowed persons is studied as a function of time interval since the day of bereavement. The effects of bereavement on mortality are investigated in all widowed people in Sweden (about 360000) from 1968 to 1978. Among widowers above 65 years of age there are nine deaths per 1000 in excess compared with married men during the first 3 months after bereavement. In comparison with married people in the age group 70–74 it is found that among widows there is an increased mortality by 22% and among widowers by 48% during the first 3 months after bereavement. Further observation, during a period of altogether 11 years, showed that excess mortality continues, though at a lower level. In the second part, causes of death in the age group 70–74, divided according to marital status, are studied on the basis of data from the National Central Bureau of Statistics. The excess in mortality is due mainly to cancer and cardiovascular deaths, but also accidents, suicides and cirrhosis of the liver. The third part deals with differences between marital status groups with respect to tobacco smoking and alcohol abuse by using data from the population study of “70-year-olds in Göteborg” (H 70). By using data from central registers together with data from the population study it is possible to show that life style factors have an impact on the difference in mortality pattern between married and widowed people in Sweden.

scholarly journals Early roots of adult competence

2011 ◽  
Vol 35 (6) ◽  
pp. 490-496 ◽  
Author(s):  
Michelle M. Englund ◽  
Sally I-Chun Kuo ◽  
Jennifer Puig ◽  
W. Andrew Collins
Keyword(s):  
Social Capital ◽  
Interpersonal Relationships ◽  
Adaptive Functioning ◽  
Early Adulthood ◽  
Well Being ◽  
Path Modeling ◽  
Global Functioning ◽  
Developmental Models ◽  
Using Data

Social capital has traditionally been defined in terms of the amount of resources that one derives as a result of a diversity of interpersonal relationships. However, the quality of these relationships across development has not been examined as a contributor to social capital and few studies have examined the significance of various age-salient relationships in predicting adaptive functioning, especially testing for cumulative effects over time. Using data from the Minnesota Longitudinal Study of Risk and Adaptation, developmental models spanning from infancy to adulthood were tested via path modeling, linking quality of various age-salient relationships (e.g., infant–caregiver attachment, peer competence, friendship security, and effectiveness in romantic relationships) to global adaptive functioning at age 28. As hypothesized, quality of age-salient relationships during different developmental periods predicted the quality of subsequent relationships, but also showed links with adaptive functioning in early adulthood. Results also showed that the quality of infant attachment relationships not only was linked with more proximal relationships, but also had direct effects on global functioning, suggesting the potential significance of early relationship quality in adaption and well-being in adulthood.

scholarly journals COVID-19 Outbreaks and Age Mortality Patterns

2020 ◽  
Author(s):  
Vladimir Shapiro
Keyword(s):  
Elderly Population ◽  
Regression Line ◽  
Logarithmic Scale ◽  
Age At Death ◽  
Large Magnitude ◽  
Mortality Pattern ◽  
Death Risk ◽  
Lower Point

Abstract As of the Fall of 2020, many countries are still fighting the COVID-19 pandemic. After the painful first massive wave in the Spring, more and more of them are facing the new outbreaks of varying impact. Understanding the mortality pattern associated with such subsequent outbreaks would help governments better prepare and save lives. These secondary outbreaks are still quite new to the scientists as the data have not been sufficient to identify robust trends. By now, US is dealing with the second outbreak of large magnitude and statistically significant analyses are finally possible. We have analyzed the weekly mortality death counts for various ages in US for the entire COVID-19 pandemic duration. Three somewhat related features involving age at death have been extracted: a) COVID-19 average age at death, b) fraction of deaths at ages 65+, and c) slope of age gradient regression line on the logarithmic scale. It turns out that during the outbreak the mortality age gradient is undergoing the following changes: a) average age at death at the peak is 4-5 years higher than at the lower point; b) fraction of deaths of 65+ is by approximately 10% higher, and c) the higher slope of the age gradient translates into an extra death risk of 5.8% every year. In other words, risks, to which an elderly population is exposed during all phases of the pandemic, rise sharply during and right after the outbreak peaks.

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