Bühlmann credibility approach to systematic mortality risk modeling for sub-Saharan Africa populations (Kenya)

The fluctuation of microgrid power flow leads to serious voltage problems at the point of common coupling (PCC). The quantitative representation of the disturbance parameters of the voltage waveform at the PCC is necessary for evaluating and controlling the impact of distributed generation in the microgrid on the power system. An improved atomic decomposition (IAD) method is proposed to represent the disturbance parameters quantitatively and efficiently. Based on the disturbance characteristics of the PCC voltage, a coherent atom dictionary composed of four subdictionaries is constructed to improve the decomposition efficiency. To further improve the computational efficiency, an improved matching pursuits algorithm is proposed by alternating the search way to extract the disturbance components in the atomic decomposition. Meanwhile, simulation results show that the proposed IAD method has better antinoise and disturbance parameters quantization ability than wavelet transform.

Section: Introductionmentioning

confidence: 99%

Quantitative Representation of Disturbance Waveform for Microgrid Connected PCC Voltages Using Improved Atomic Decomposition

Hao,

Liu,

Yan

et al. 2024

“…More interestingly, some mortality modeling papers have misunderstood the model in its operations, for instance, denoting m(x, t) which is commonly defned as central death rates for a given life aged exactly x for a given time period of t years shown by [2][3][4][5] as…”

Section: Introductionmentioning

confidence: 99%

“…Today, several extensions and applications have been made, making actuarial science literature rich in terms of mortality modeling techniques. Also, the freeware statistical R package has included many packages, namely, "StMoMo," "demography," "acturyr," and "gnm," thus helping in forecasting the rates of future mortality and at the same time ftting a model of time series especially to the predicted rates of the mortality index, see [2,4,[13][14][15][16][17]. Deep learning mortality modeling has been conducted in [18], thus making it one of novel methodologies to deal with data paucity in developing countries.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Incorporated Bühlmann Credibility in the Modified Lee–Carter Mortality Model

Odhiambo

2023

Self Cite

This research study proposes the inclusion of randomness or an error term in the modified Lee–Carter model, which improves the traditional Lee–Carter model for modeling and forecasting mortality risk for years in the actuarial science field. While the modified Lee–Carter model points out some of its common shortcomings , it has no distributional assumption that has been placed on the error/disturbance term. Incorporating a Gaussian distributional assumption on the error term is proposed, and then, the deep learning technique is used to obtain the parameter estimates. It is a departure from the traditional singular value decomposition estimation technique for estimating the parameters in the model. Finally, the Bühlmann credibility approach is incorporated into the model to determine its forecasting precision compared to the classical Lee–Carter model before applied in actuarial valuations.

“…Finally, Odhiambo et al [23] proposed using Bühlmann's credibility approach to model systematic mortality risk, especially for sub-Saharan African populations with limited mortality data, such as Kenya. In addition, Odhiambo et al [24] proposed incorporating the deep learning technique to the Cairns-Blake-Dowd (CBD) model while modelling systematic mortality risk to enhance the accuracy of the models used in cases with limited data for systematic mortality risk modelling and forecasting.…”

Section: Introductionmentioning

confidence: 99%

Poisson Incorporated Credibility Regression Modelling of Systematic Mortality Risk for Populations with Finite Data

Odhiambo

Weke

Ngare

et al. 2022

Self Cite

This study considered the modeling of systematic mortality risk for populations with finite data using the Poisson incorporated Credibility regression model. For novelty, we have included the credibility regression approach to modelling mortality by assuming the number of annual deaths follow a Poisson distribution. Our model shows improvement in precision levels when estimating mortality risk compared to classical models used in European countries. We have illustrated that our model works optimally when using Kenyan mortality data, comparing male and female lives under the different strategies, thus making better predictions than the classical Lee–Carter (LC) and Cairns–Blake–Dowd (CBD) models. The mean absolute forecast error (MAFE), mean absolute percentage forecast error (MAPFE), root mean square error (RMSE), and root mean square forecast error (RMSFE) under the incorporated credibility regression model are much lower than the values obtained without incorporation of the Buhlmann credibility approach. The findings of this research will help insurance companies, pension firms, and government agencies in sub-Saharan countries model and forecast systematic mortality risks accurately. Finally, the results are essential in actuarial modelling and pricing, thus making life assurance products affordable for most people in low-income African countries.