Bootstrapping the Poisson log-bilinear model for mortality forecasting

To improve the mechanical performances of joints in prefabricated construction, a type of connection structure with long-fiber and metal laminated bolts (referred to as a fiber-metal connector) is proposed and investigated by simulation and theoretical methods. The results include the following: (1) The fiber layer in bolts can form a second stiffness during rotation. This mechanical characteristic improves the bearing capacities and energy dissipation ability of the connector relative to the conventional metal connector, which are expected to effectively limit the elastoplastic rotational displacement of a structure. (2) For the reason, the fiber layer can bear load in the plastic phase due to its high-strength characteristic in the length direction. (3) A bilinear model for the bearing curve of the fiber-metal connector is proposed, and equations for optimization of fiber layer thickness are obtained with a target on bearing capacity and energy dissipation ability which are approximately higher 30% and 13% than that of the conventional metal connector, respectively. This research is expected to provide a theoretical basis for the application of this fiber-metal connector in engineering and improve the safety of prefabricated structures.

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A Multi-Timescale Bilinear Model for Optimization and Control of HVAC Systems with Consistency

Energies ◽

10.3390/en14020400 ◽

2021 ◽

Vol 14 (2) ◽

pp. 400 ◽

Cited By ~ 1

Author(s):

Zelin Nie ◽

Feng Gao ◽

Chao-Bo Yan

Keyword(s):

Optimization Model ◽

Air Conditioning ◽

State Of The Art ◽

Hvac Systems ◽

Practical Significance ◽

Hvac System ◽

Bilinear Model ◽

Optimization And Control ◽

And Control ◽

Slow Timescale

Reducing the energy consumption of the heating, ventilation, and air conditioning (HVAC) systems while ensuring users’ comfort is of both academic and practical significance. However, the-state-of-the-art of the optimization model of the HVAC system is that either the thermal dynamic model is simplified as a linear model, or the optimization model of the HVAC system is single-timescale, which leads to heavy computation burden. To balance the practicality and the overhead of computation, in this paper, a multi-timescale bilinear model of HVAC systems is proposed. To guarantee the consistency of models in different timescales, the fast timescale model is built first with a bilinear form, and then the slow timescale model is induced from the fast one, specifically, with a bilinear-like form. After a simplified replacement made for the bilinear-like part, this problem can be solved by a convexification method. Extensive numerical experiments have been conducted to validate the effectiveness of this model.

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Parsimonious Predictive Mortality Modeling by Regularization and Cross-Validation with and without Covid-Type Effect

Risks ◽

10.3390/risks9010005 ◽

2020 ◽

Vol 9 (1) ◽

pp. 5

Author(s):

Karim Barigou ◽

Stéphane Loisel ◽

Yahia Salhi

Keyword(s):

Life Insurance ◽

Cross Validation ◽

Cohort Effect ◽

Information Criterion ◽

Mortality Forecasting ◽

Hand Model ◽

Regularization Techniques ◽

Bayes Information Criterion ◽

The One ◽

Regularized Model

Predicting the evolution of mortality rates plays a central role for life insurance and pension funds. Standard single population models typically suffer from two major drawbacks: on the one hand, they use a large number of parameters compared to the sample size and, on the other hand, model choice is still often based on in-sample criterion, such as the Bayes information criterion (BIC), and therefore not on the ability to predict. In this paper, we develop a model based on a decomposition of the mortality surface into a polynomial basis. Then, we show how regularization techniques and cross-validation can be used to obtain a parsimonious and coherent predictive model for mortality forecasting. We analyze how COVID-19-type effects can affect predictions in our approach and in the classical one. In particular, death rates forecasts tend to be more robust compared to models with a cohort effect, and the regularized model outperforms the so-called P-spline model in terms of prediction and stability.

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MORTALITY FORECASTING WITH A SPATIALLY PENALIZED SMOOTHED VAR MODEL

Astin Bulletin ◽

10.1017/asb.2020.39 ◽

2020 ◽

pp. 1-29

Author(s):

Le Chang ◽

Yanlin Shi

Keyword(s):

Population Data ◽

Coefficient Matrix ◽

Gradient Algorithm ◽

Var Model ◽

Vector Autoregressive ◽

Mortality Forecasting ◽

Dependency Structure ◽

Svar Model ◽

The Uk ◽

Sparsity Structure

Abstract This paper investigates a high-dimensional vector-autoregressive (VAR) model in mortality modeling and forecasting. We propose an extension of the sparse VAR (SVAR) model fitted on the log-mortality improvements, which we name “spatially penalized smoothed VAR” (SSVAR). By adaptively penalizing the coefficients based on the distances between ages, SSVAR not only allows a flexible data-driven sparsity structure of the coefficient matrix but simultaneously ensures interpretable coefficients including cohort effects. Moreover, by incorporating the smoothness penalties, divergence in forecast mortality rates of neighboring ages is largely reduced, compared with the existing SVAR model. A novel estimation approach that uses the accelerated proximal gradient algorithm is proposed to solve SSVAR efficiently. Similarly, we propose estimating the precision matrix of the residuals using a spatially penalized graphical Lasso to further study the dependency structure of the residuals. Using the UK and France population data, we demonstrate that the SSVAR model consistently outperforms the famous Lee–Carter, Hyndman–Ullah, and two VAR-type models in forecasting accuracy. Finally, we discuss the extension of the SSVAR model to multi-population mortality forecasting with an illustrative example that demonstrates its superiority in forecasting over existing approaches.

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A novel EVT-modified Lee-Carter model for mortality forecasting : An application to extreme mortality events

Journal of Statistics and Management Systems ◽

10.1080/09720510.2021.1878629 ◽

2021 ◽

pp. 1-33

Author(s):

Gavakshi Gungah ◽

Jason Narsoo

Keyword(s):

Mortality Forecasting ◽

Carter Model

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Predictive performance of international COVID-19 mortality forecasting models

Nature Communications ◽

10.1038/s41467-021-22457-w ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Joseph Friedman ◽

Patrick Liu ◽

Christopher E. Troeger ◽

Austin Carter ◽

Robert C. Reiner ◽

...

Keyword(s):

Predictive Performance ◽

Absolute Error ◽

Decision Makers ◽

Government Interventions ◽

World Region ◽

Mortality Forecasting ◽

Forecasting Models ◽

Pandemic Response ◽

Alternative Scenarios ◽

Absolute Percent Error

AbstractForecasts and alternative scenarios of COVID-19 mortality have been critical inputs for pandemic response efforts, and decision-makers need information about predictive performance. We screen n = 386 public COVID-19 forecasting models, identifying n = 7 that are global in scope and provide public, date-versioned forecasts. We examine their predictive performance for mortality by weeks of extrapolation, world region, and estimation month. We additionally assess prediction of the timing of peak daily mortality. Globally, models released in October show a median absolute percent error (MAPE) of 7 to 13% at six weeks, reflecting surprisingly good performance despite the complexities of modelling human behavioural responses and government interventions. Median absolute error for peak timing increased from 8 days at one week of forecasting to 29 days at eight weeks and is similar for first and subsequent peaks. The framework and public codebase (https://github.com/pyliu47/covidcompare) can be used to compare predictions and evaluate predictive performance going forward.

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