ELYP (version 0.7-1)

BJfindL2: Find the Wilks Confidence Interval Lower Bound for Betafun from the 2 dimensional Buckley-James Empirical Likelihood Ratio Function

Description

This function uses simple search to find the lower level (default 95%) 1 parameter Wilks confidence limits based on the Buckley-James empirical likelihood test function for two dimensional beta's. Betafun determines which 1 parameter we are finding the lower bound.

Usage

BJfindL2(NPmle, ConfInt, LLRfn, Betafun, dataMat, level=3.84)

Arguments

NPmle

a 2-d vector: the NPMLEs: beta1 hat and beta2 hat.

ConfInt

a vector of length 2. Approx. length of the 2 conf. intervals for beta1 and beta2.

LLRfn

a function that returns -2LLR value.

Betafun

a function that takes the input of 2 parameter values (beta1,beta2) and returns a parameter that we wish to find the confidence Interval lower Value.

dataMat

matrix of covariates

level

confidence level. Use chi-square(df=1), but calibration possible.

Value

• A list with the following components:
• Lower
  the lower confidence bound.
• minParameterNloglik
  Final values of the 2 parameters, and the log likelihood.

Details

Basically we repeatedly testing the value of the 2 parameters, finding the -2LLR values, until we find those Betafun which the -2 log likelihood Ratio value is equal to 3.84 (or other level, if set differently).

References

Zhou, M. and Li, G. (2006). Computing censored empirical likelihood ratio by EM algorithm. JCGS

Examples

## See the Rd file of BJfindU2 for example.