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What are the models that extend lee carter model using multipopulation data? 


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The Lee-Carter model has been extended using multipopulation data in various ways. One approach involves introducing a neural network (NN) architecture designed to calibrate individual models simultaneously across all populations, improving parameter estimates and forecasting performance . Another extension incorporates macroeconomic variables into the model, utilizing a new LC-WT-ANFIS model that combines adaptive network-based fuzzy inference systems with wavelet filters to enhance forecasting accuracy . Furthermore, a nonlinear Bayesian extension of the Lee-Carter model has been proposed, integrating a neural network with variational autoencoder for single-stage estimation, providing interpretability and confidence intervals in forecasting without Markov chain Monte Carlo methods . These extensions demonstrate the versatility and effectiveness of utilizing multipopulation data in enhancing mortality rate forecasts.

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The Lee-Carter model is extended using multipopulation data through the development of neural networks that fit both the Lee-Carter and Poisson Lee-Carter models simultaneously.
The paper extends the Lee-Carter model using a variational autoencoder, focusing on single populations. It does not specifically address extensions using multipopulation data.
The LC-V model and its multipopulation counterpart extend the Lee-Carter model using multipopulation data, improving forecasting accuracy and maintaining age coherence in mortality rate predictions.
The paper introduces the LC-WT-ANFIS model, extending the Lee-Carter model with multipopulation data using wavelet functions and adaptive network-based fuzzy inference system for mortality rate forecasting.

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