blite: Bayesian threshold-based inference for time series extremes

In lite: Likelihood-Based Inference for Time Series Extremes

Bayesian threshold-based inference for time series extremes

Description

Performs threshold-based Bayesian inference for 3 aspects of stationary time series extremes: the probability that the threshold is exceeded, the marginal distribution of threshold excesses and the extent of clustering of extremes, as summarised by the extremal index.

Usage

blite(
  data,
  u,
  cluster,
  k = 1,
  inc_cens = TRUE,
  ny,
  gp_prior = revdbayes::set_prior(prior = "mdi", model = "gp"),
  b_prior = revdbayes::set_bin_prior(prior = "jeffreys"),
  theta_prior_pars = c(1, 1),
  n = 1000,
  type = c("vertical", "none"),
  ...
)

Arguments

data	A numeric vector or numeric matrix of raw data. If data is a matrix then the log-likelihood is constructed as the sum of (independent) contributions from different columns. A common situation is where each column relates to a different year. If data contains missing values then split_by_NAs isvused to divide the data further into sequences of non-missing values, stored in different columns in a matrix. Again, the log-likelihood is constructed as a sum of contributions from different columns.
u	A numeric scalar. The extreme value threshold applied to the data. See Details for information about choosing u.
cluster	This argument is used to set the argument cluster to meatCL, which calculates the matrix V passed as the argument V to adjust_loglik. If data is a matrix and cluster is missing then cluster is set so that data in different columns are in different clusters. If data is a vector and cluster is missing then cluster is set so that each observation forms its own cluster. If cluster is supplied then it must have the same structure as data: if data is a matrix then cluster must be a matrix with the same dimensions as data and if data is a vector then cluster must be a vector of the same length as data. Each entry in cluster sets the cluster of the corresponding component of data.
k, inc_cens	Arguments passed to kgaps. k sets the value of the run parameter K in the K-gaps model for the extremal index. inc_cens determines whether contributions from right-censored inter-exceedance times are used. See Details for information about choosing k.
ny	A numeric scalar. The (mean) number of observations per year. Setting this appropriately is important when making predictive inferences using predict.blite, but ny is not used by blite so it need not be supplied now. If ny is supplied to blite then it is stored for use by predict.blite. Alternatively, ny can be supplied in a later call to predict.blite. If ny is supplied to both blite and predict.blite then the value supplied to predict.blite will take precedence, with no warning given.
gp_prior	A list to specify a prior distribution for the GP parameters (\sigmau, \xi), set using set_prior.
b_prior	A list to specify a prior distribution for the Bernoulli parameter pu, set using set_bin_prior.
theta_prior_pars	A numerical vector of length 2 containing the respective values of the parameters \alpha and \beta of a Beta(\alpha, \beta) prior for the extremal index \theta.
n	An integer scalar. The size of posterior sample required.
type	A character scalar. Either "vertical" to adjust the independence log-likelihood vertically, or "none" for no adjustment. Horizontal adjustment is not offered because it does not preserve the correct support of the posterior distribution.
...	Further arguments to be passed to the function meatCL in the sandwich package. In particular, the clustering adjustment argument cadjust may make a difference if the number of clusters is not large.

Details

See flite for details of the (adjusted) likelihoods on which these Bayesian inferences are based.

The likelihood is based on a model for 3 independent aspects.

1. A Bernoulli(p_u) model for whether a given observation exceeds the threshold u.

2. A generalised Pareto, GP(\sigma_u, \xi), model for the marginal distribution of threshold excesses.

3. The K-gaps model for the extremal index \theta.

The general approach follows Fawcett and Walshaw (2012).

The contributions to the likelihood for p_u and (\sigma_u, \xi) are based on the vertically-adjusted likelihoods described in flite. This is an example of Bayesian inference using a composite likelihood Ribatet et al (2012). Priors for p_u (\sigma_u, \xi) and \theta are set using the arguments gp_prior, b_prior and theta_prior_pars. Currently, only priors where p_u (\sigma_u, \xi) and \theta are independent a priori are allowed.

Two tuning parameters need to be chosen: a threshold u and the K-gaps run parameter K. The exdex package has a function choose_uk to inform this choice.

Random samples are simulated from the posteriors for p_u and (\sigma_u, \xi) (using ru) and \theta (using kgaps_post).

Value

An object of class c("blite", "lite", "chandwich"). This object is an n \times 4 matrix containing the posterior samples, with column names c("p[u]", "sigma[u]", "xi", "theta").

The object also has the attributes "Bernoulli", "gp", "theta", which provide the fitted model objects returned from adjust_loglik (for "Bernoulli" and "gp") and kgaps (for "theta"). The named input arguments are returned in a list as the attribute inputs. If ny was not supplied then its value is NA. The call to blite is provided in the attribute "call". A call to flite is used to create adjusted log-likelihoods for p_u and (\sigma_u, \xi). The object returned from the call is provided as the attribute "flite_object".

Objects inheriting from class "blite" have coef, nobs, plot, summary, vcov and confint methods. See bliteMethods.

predict.blite can be used to make predictive inferences about the largest value to be observed in N years.

References

Fawcett, L. and Walshaw, D. (2012), Estimating return levels from serially dependent extremes. Environmetrics, 23, 272-283. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/env.2133")}

Ribatet, M., Cooley, D., & Davison, A. C. (2012). Bayesian inference from composite likelihoods, with an application to spatial extremes. Statistica Sinica, 22(2), 813-845.

See Also

bliteMethods, including plotting the posterior samples.

predict.blite to make predictive inferences about future extreme values.

flite for frequentist threshold-based inference for time series extremes.

choose_uk to inform the choice of the threshold u and run parameter K.

Examples

### Cheeseboro wind gusts

cdata <- exdex::cheeseboro
# Each column of the matrix cdata corresponds to data from a different year
# blite() sets cluster automatically to correspond to column (year)
cpost <- blite(cdata, u = 45, k = 3)
summary(cpost)

## Plots of posterior samples
plot(cpost)

## Credible intervals
confint(cpost)

lite documentation built on Sept. 11, 2024, 6:27 p.m.