print.adlp_component {ADLP}

Accident and Development period Adjusted Linear Pools Component Models

Description

Class to store component models and related functions required for ADLP estimation, prediction and goodness of fit.

Usage

## S3 method for class 'adlp_component'
print(x, ...)

adlp_component(
  model_train,
  model_full,
  calc_dens,
  calc_mu,
  calc_cdf,
  sim_fun,
  ...
)

Arguments

x

Object of class adlp_component

...

Other named parameters required for the model or any of its related functions to run.

model_train

Model trained on training data

model_full

Model trained on all in-sample data

calc_dens

function to calculate the pdf of each point

calc_mu

function to calculate the estimated mean of each point

calc_cdf

function to calculate the cdf of each point

sim_fun

function to simulate new from

Details

Component models model_train and model_full are designed to be objects of class glm, lm, or similar. The models would desirably have a S3 method for 'formula. For models that do not fit under this umbrella, see custom_model. For a potential list of candidate models, one might refer to Avanzi, Li, Wong and Xian (2022).

Functions as assumed to have the following parameter naming convention:

• y as the response variable

• model as the modeling object model_train or model_full

• newdata to designate new data

Other inputs not in this list will need to be intialised with the adlp_component

Value

Object of class adlp_component

References

Avanzi, B., Li, Y., Wong, B., & Xian, A. (2022). Ensemble distributional forecasting for insurance loss reserving. arXiv preprint arXiv:2206.08541.

Examples

data("test_claims_dataset")

train_val <- train_val_split_method1(
    df = test_claims_dataset,
    tri.size = 40,
    val_ratio = 0.3,
    test = TRUE
)
train_data <- train_val$train
valid_data <- train_val$valid
insample_data <- rbind(train_data, valid_data)

base_model1 <- glm(formula = claims~factor(dev),
                   family=gaussian(link = "identity"), data=train_data)

base_model1_full <- update(base_model1, data = insample_data)

dens_normal <- function(y, model, newdata){
    pred_model <- predict(model, newdata=newdata, type="response", se.fit=TRUE)
    mu <- pred_model$fit
    sigma <- pred_model$residual.scale
    return(dnorm(x=y, mean=mu, sd=sigma))
}

cdf_normal<-function(y, model, newdata){
    pred_model <- predict(model, newdata=newdata, type="response", se.fit=TRUE)
    mu <- pred_model$fit
    sigma <- pred_model$residual.scale
    return(pnorm(q=y, mean=mu, sd=sigma))
}

mu_normal<-function(model, newdata){
    mu <- predict(model, newdata=newdata, type="response")
    mu <- pmax(mu, 0)
    return(mu)
}

sim_normal<-function(model, newdata){
    pred_model <- predict(model, newdata=newdata, type="response", se.fit=TRUE)
    mu <- pred_model$fit
    sigma <- pred_model$residual.scale

    sim <- rnorm(length(mu), mean=mu, sd=sigma)
    sim <- pmax(sim, 0)
    return(sim)
}

base_component1 = adlp_component(
    model_train = base_model1,
    model_full = base_model1_full,
    calc_dens = dens_normal,
    calc_cdf = cdf_normal,
    calc_mu = mu_normal,
    sim_fun = sim_normal
)