GPRMortality - Gaussian Process Regression for Mortality Rates
A Bayesian statistical model for estimating child
(under-five age group) and adult (15-60 age group) mortality.
The main challenge is how to combine and integrate these
different time series and how to produce unified estimates of
mortality rates during a specified time span. GPR is a Bayesian
statistical model for estimating child and adult mortality
rates which its data likelihood is mortality rates from
different data sources such as: Death Registration System,
Censuses or surveys. There are also various hyper-parameters
for completeness of DRS, mean, covariance functions and
variances as priors. This function produces estimations and
uncertainty (95% or any desirable percentiles) based on
sampling and non-sampling errors due to variation in data
sources. The GP model utilizes Bayesian inference to update
predicted mortality rates as a posterior in Bayes rule by
combining data and a prior probability distribution over
parameters in mean, covariance function, and the regression
model. This package uses Markov Chain Monte Carlo (MCMC) to
sample from posterior probability distribution by 'rstan'
package in R. Details are given in Wang H, Dwyer-Lindgren L,
Lofgren KT, et al. (2012) <doi:10.1016/S0140-6736(12)61719-X>,
Wang H, Liddell CA, Coates MM, et al. (2014)
<doi:10.1016/S0140-6736(14)60497-9> and Mohammadi, Parsaeian,
Mehdipour et al. (2017) <doi:10.1016/S2214-109X(17)30105-5>.
