We are interested in E [N], the
mean time until the most recent k values of a sequence
of independent and identically distributed random variables
exceeds a specified constant. Using recent results, we present
a simulation procedure for determining E [N]. These results are also used to obtain upper and lower bounds for
E [N]. These bounds, however, are in
terms of a quantity ω that is not easily calculated.
A recursive procedure for evaluating ω when the data
distribution is Bernoulli is given. Efficient simulation
procedures for estimating ω in the cases of normal
and exponential population distributions are also presented,
as is a Markov chain monte carlo procedure when the distribution
is general.
On moving average control charts and their conditional average run lengths
