mean.BLC

Calculates the means based on the resulting chains from a fitted BLC model.

Fit Bayesian graduation mortality using the Heligman-Pollard model, as seen in Heligman, L., & Pollard, J. H. (1980) <doi:10.1017/S0020268100040257> and Dellaportas, Petros, et al. (2001) <doi:10.1111/1467-985X.00202>, and dynamic linear model (Campagnoli, P., Petris, G., and Petrone, S. (2009) <doi:10.1007/b135794_2>). While Heligman-Pollard has parameters with a straightforward interpretation yielding some rich analysis, the dynamic linear model provides a very flexible adjustment of the mortality curves by controlling the discount factor value. Closing methods for both Heligman-Pollard and dynamic linear model were also implemented according to Dodd, Erengul, et al. (2018) <https://www.jstor.org/stable/48547511>. The Bayesian Lee-Carter model is also implemented to fit historical mortality tables time series to predict the mortality in the following years and to do improvement analysis, as seen in Lee, R. D., & Carter, L. R. (1992) <doi:10.1080/01621459.1992.10475265> and Pedroza, C. (2006) <doi:10.1093/biostatistics/kxj024>.

Luiz Figueiredo

BayesMortalityPlus

Bayesian Mortality Modelling

mean.BLC function

<dl><dt>x</dt>
<dd>A <code>BLC</code> object, result of a call to blc() function.</dd>
<dt>name</dt>
<dd>A character with a parameter name of the BLC model that should be returned. It can be one of these: "alpha", "beta", "kappa", "phiv", "theta", "phiw".</dd>
<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd></dl>

Arguments

BLC: Arithmetic mean — mean.BLC

<dl>

<dt>x</dt>
<dd>A <code>BLC</code> object, result of a call to blc() function.</dd>


<dt>name</dt>
<dd>A character with a parameter name of the BLC model that should be returned. It can be one of these: "alpha", "beta", "kappa", "phiv", "theta", "phiw".</dd>


<dt>...</dt>
<dd>Further arguments passed to or from other methods.</dd>

</dl>

mean.BLC: BLC: Arithmetic mean

Description

Usage

Value

Arguments

See Also

Examples