Title: Confidence Intervals for Quantiles from Histograms and Other Grouped Data,
Abstract: Interval estimation of quantiles has been treated by many in the literature.
However, to the best of our knowledge there has been no consideration for
interval estimation when the data are available in grouped format. Motivated by
this, we introduce several methods to obtain confidence intervals for quantiles
when only grouped data is available. Our preferred method for interval
estimation is to approximate the underlying density using the Generalized
Lambda Distribution (GLD) to both estimate the quantiles and variance of the
quantile estimators. We compare the GLD method with some other methods that we
also introduce which are based on a frequency approximation approach and a
linear interpolation approximation of the density. Our methods are strongly
supported by simulations showing that excellent coverage can be achieved for a
wide number of distributions. These distributions include highly-skewed
distributions such as the log-normal, Dagum and Singh-Maddala distributions. We
also apply our methods to real data and show that inference can be carried out
on published outcomes that have been summarized only by a histogram. Our
methods are therefore useful for a broad range of applications. We have also
created a web application that can be used to conveniently calculate the
estimators.