- method
"LDF"
- growthFunction
name of the growth function
- Origin
names of the rows of the triangle
- CurrentValue
the most mature value for each row
- CurrentAge
the most mature "age" for each row
- CurrentAge.used
the most mature age used; differs from "CurrentAge"
when adol=TRUE
- MAXAGE
same as 'maxage' argument
- MAXAGE.USED
the maximum age for development
from the average date of loss;
differs from MAXAGE when adol=TRUE
- FutureValue
the projected loss amounts ("Reserves" in Clark's paper)
- ProcessSE
the process standard error of the FutureValue
- ParameterSE
the parameter standard error of the FutureValue
- StdError
the total standard error (process + parameter)
of the FutureValue
- Total
a list
with amounts that appear on the "Total" row
for components "Origin" (="Total"), "CurrentValue", "FutureValue",
"ProcessSE", "ParameterSE", and "StdError"
- PAR
the estimated parameters
- THETAU
the estimated parameters for the "ultimate loss" by
origin year ("U" in Clark's notation)
- THETAG
the estimated parameters of the growth function
- GrowthFunction
value of the growth function as of the
CurrentAge.used
- GrowthFunctionMAXAGE
value of the growth function as of the
MAXAGE.used
- SIGMA2
the estimate of the sigma^2 parameter
- Ldf
the "to-ultimate" loss development factor
(sometimes called the "cumulative development factor")
as defined in Clark's paper for each origin year
- LdfMAXAGE
the "to-ultimate" loss development factor as of
the maximum age used in the model
- TruncatedLdf
the "truncated" loss development factor for developing
the current diagonal to
the maximum age used in the model
- FutureValueGradient
the gradient of the FutureValue function
- origin
the origin year corresponding to each observed value of incremental loss
- age
the age of each observed value of incremental loss
- fitted
the expected value of each observed value of incremental loss
(the "mu's" of Clark's paper)
- residuals
the actual minus fitted value for
each observed incremental loss
- stdresid
the standardized residuals for
each observed incremental loss
(= residuals/sqrt(sigma2*fitted),
referred to as "normalized residuals" in Clark's paper; see p. 62)
- FI
the "Fisher Information" matrix as defined in Clark's paper
(i.e., without the sigma^2 value)
- value
the value of the loglikelihood function at the solution point
- counts
the number of calls to the loglikelihood function
and its gradient function when numerical convergence was achieved