1,721,086 research outputs found
A random forest algorithm to improve the Lee–Carter mortality forecasting: impact on q-forward
Full text linkIncreased life expectancy in developed countries has led researchers to pay more attention to mortality projection to anticipate changes in mortality rates. Following the scheme proposed in Deprez et al. (Eur Actuar J 7(2):337–352, 2017) and extended by Levantesi and Pizzorusso (Risks 7(1):26, 2019), we propose a novel approach based on the combination of random forest and two-dimensional P-spline, allowing for accurate mortality forecasting. This approach firstly provides a diagnosis of the limits of the Lee–Carter mortality model through the application of the random forest estimator to the ratio between the observed deaths and their estimated values given by a certain model, while the two-dimensional P-spline are used to smooth and project the random forest estimator in the forecasting phase. Further considerations are devoted to assessing the demographic consistency of the results. The model accuracy is evaluated by an out-of-sample test. Finally, we analyze the impact of our model on the pricing of q-forward contracts. All the analyses have been carried out on several countries by using data from the Human Mortality Database and considering the Lee–Carter model
Petrus Nigri: a Dominican Hebraist
No full textThis book chapter introduces a well-known 15th century Dominican Hebraist and preacher, Petrus Nigri (Peter Schwarz), and discusses his work in the context of late medieval Jewish-Christian relations
Life expectancy and lifespan disparity forecasting: a long short-term memory approach
Full text linkAfter the World War II, developed countries experienced a constant decline in mortality. As a result, life expectancy has never stopped increasing, despite an evident deceleration in developed countries, e.g. England, USA and Denmark. In this paper, we propose a new approach for forecasting life expectancy and lifespan disparity based on the recurrent neural networks with a long short-term memory. This type of neural network leads to predicting future values of longevity indexes while maintaining the significant influence of the past trend, but at the same time adequately reproducing the recent trend into forecasting. The model was applied to five countries for two fitting periods focusing on the forecasting life expectancy and lifespan disparity, both independently and simultaneously at birth and age 65. The results were compared to the projections obtained by four different models, namely, the Double Gap, ARIMA, CoDa and Lee-Carter in the independent case and the first-order Vector Autoregression model in the simultaneous case. Our predictions seem to be coherent with historical trends and biologically reasonable, providing a more accurate portrait of the future life expectancy and lifespan disparity
Predicting the Second Wave of COVID-19 Pandemic Through the Dynamic Evolving Neuro Fuzzy Inference System
No full text
Life expectancy and lifespan disparity forecasting: a long short-term memory approach
No full textAfter the World War II, developed countries experienced a constant decline in mortality. As a result, life expectancy has never stopped increasing, despite an evident deceleration in developed countries, e.g. England, USA and Denmark. In this paper, we propose a new approach for forecasting life expectancy and lifespan disparity based on the recurrent neural networks with a long short-term memory. This type of neural network leads to predicting future values of longevity indexes while maintaining the significant influence of the past trend, but at the same time adequately reproducing the recent trend into forecasting. The model was applied to five countries for two fitting periods focusing on the forecasting life expectancy and lifespan disparity, both independently and simultaneously at birth and age 65. The results were compared to the projections obtained by four different models, namely, the Double Gap, ARIMA, CoDa and Lee-Carter in the independent case and the first-order Vector Autoregression model in the simultaneous case. Our predictions seem to be coherent with historical trends and biologically reasonable, providing a more accurate portrait of the future life expectancy and lifespan disparity
Correlation between angiographic success and functional improvement assessed by exercise test following percutaneous transluminal coronary angioplasty.
No full textCorrelation between angiographic success and functional improvement assessed by exercise test following percutaneous transluminal coronary angioplasty.
Barillà F, Romeo F, Tomai F, Pace V, Valente A, Martuscelli E, Nigri A, Reale A.
Source
2nd Department of Cardiovascular Diseases, University La Sapienza, Rome, Italy.
Abstract
Sixty-one consecutive patients with stable effort angina and single vessel disease underwent successful (reduction of coronary stenoses by greater than or equal to 20%) percutaneous transluminal coronary angioplasty (PTCA). Anatomical results were analysed on the basis of functional evaluation obtained by exercise test (ET) 1 week before (pre-PTCA) and within 1 month after (post-PTCA) PTCA. Total exercise duration and maximal double product significantly increased after PTCA (4.5 +/- 1 min vs 6.9 +/- 1.5 min, p less than 0.001 and 14.1 +/- 3.6 x 1000 mmHg x bpm vs 18 +/- 4.2 x 1000 mmHg x bpm, p less than 0.001). Pre-PTCA ET was positive in 43 patients (70%) and post-PTCA ET in 15 (24%). In patients with post-PTCA positive ET, mean stenosis diameter reduction was significantly lower than that obtained in patients with negative post-PTCA ET (29.6 +/- 8.9% vs 61.1 +/- 18.8%, p less than 0.001). In conclusion, PTCA improved exercise tolerance in the majority of patients with myocardial ischemia, however the definition of anatomical success used in this study appears to be poorly correlated with functional improvement as assessed by ET
Predicting the second wave of COVID-19 pandemic through the Dynamic Evolving Neuro Fuzzy Inference System
No full textIn this paper, we make a prediction of the second wave of COVID-19 using a dynamic evolving neuro-fuzzy inference system (DENFIS). The model choice is motivated by the fact that the spread of the pandemic must be read in its dynamism and every prediction cannot ignore the daily updating of available data and new information. We provide results of the prediction of the second wave of COVID-19 across Europe, soliciting to update the model day by day as new information occurs. The study offers to public health stakeholders and Governments a useful tool to analyze the effectiveness of the virus containment measures in the short run and for controlling the COVID-19 spread
Modelling Life Expectancy Gender Gap in a Multi-population Framework
No full textThis paper aims at investigating whether the life expectancy gender gap follows any long-run common tendency across different countries through a model-based analysis. If these tendencies are found to exist, then a model which takes them into account should perform better than a basic and unrestricted one. Once the gap is modeled as a multivariate non-stationary stochastic process, the goal is to find any long-run equilibrium among single series via cointegration analysis, which ultimately allows estimating some stationary linear combinations of nonstationary variables referred to as the error correction terms. To achieve such a result it is preferable to work with homogeneous samples. Therefore, the first step of this analysis consists in partitioning the initial data set into five clusters. Since the input data set includes countries with different gender gap dynamics, this diversity is clearly reflected by the difference among the models employed to fit single clusters. All series result to be non-stationary. Given the model, we check the stationarity of the error correction term and apply simple backtesting to ten-years forecasting. Evidence suggests that the fifth cluster is a cointegrated series leading to postulate that an underlying long period equilibrium does exist for this cluster
- …
