eld: Empirical likelihood displacement

In melt: Multiple Empirical Likelihood Tests

Empirical likelihood displacement

Description

Computes empirical likelihood displacement for model diagnostics and outlier detection.

Usage

## S4 method for signature 'EL'
eld(object, control = NULL)

## S4 method for signature 'GLM'
eld(object, control = NULL)

Arguments

object	An object that inherits from EL.
control	An object of class ControlEL constructed by el_control(). Defaults to NULL and inherits the control slot in object.

Details

Let L(\theta) be the empirical log-likelihood function based on the full sample with n observations. The maximum empirical likelihood estimate is denoted by \hat{\theta}. Consider a reduced sample with the ith observation deleted and the corresponding estimate \hat{\theta}_{(i)}. The empirical likelihood displacement is defined by

\textrm{ELD}_i = 2\{L(\hat{\theta}) - L(\hat{\theta}_{(i)})\}.

If \textrm{ELD}_i is large, then the ith observation is an influential point and can be inspected as a possible outlier. eld computes \textrm{ELD}_i for i = 1, \dots, n .

Value

An object of class ELD.

References

Lazar NA (2005). “Assessing the Effect of Individual Data Points on Inference From Empirical Likelihood.” Journal of Computational and Graphical Statistics, 14(3), 626–642. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1198/106186005X59568")}.

Zhu H, Ibrahim JG, Tang N, Zhang H (2008). “Diagnostic Measures for Empirical Likelihood of General Estimating Equations.” Biometrika, 95(2), 489–507. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/biomet/asm094")}.

See Also

EL, ELD, el_control(), plot()

Examples

data("precip")
fit <- el_mean(precip, par = 30)
eld <- eld(fit)
plot(eld)

melt documentation built on May 31, 2023, 7:12 p.m.