  In reliability and life testing studies, the topic of estimating hazard rate
has received great attention in recent years since an estimate of hazard rate
is a quite useful tool for making decisions. Some works have included
nonparametric approaches while some have considered parametric structural
models for complete as well as censored data sets; see Meeker et al. (1992),
Antoniadis and Gr\'egoire (1999), Rai and Singh (2003), Bezandry et al. (2005),
Brunel and Comte (2008), and Mahapatra et al. (2012). Depending on the shapes
of the hazard rate, efficiencies differ markedly across proposed estimators.
This situation is remarkable especially when different estimation techniques
are utilized for unknown parameters of underlying distributions in parametric
approaches. That is, estimated hazard rate (and also reliability) at a specific
time point t as functions of these estimators leads to inconsistent coverage
probabilities as distributional convergence of hazard rate estimator may not be
at the desired rate for certain sample sizes.
  This manuscript focuses on the estimation of monotone hazard rate when the
distribution of concern is of location-scale type. A very simple and efficient
approximation of hazard rate for a complete sample is introduced as a function
of order statistics, which allows a fast convergence to the asymptotic
distribution while achieving highly accurate coverage probabilities for
confidence intervals (CI). Efficiency of the method is demonstrated on
simulation studies by considering a number of location-scale distributions
having various hazard shapes. We also consider the case of censored samples by
incorporating the predicted future order statistics into the picture.
