Quantile　information　is　useful　in　business　and　engineering　applications,　but　the　exact　sampling　distribution　of　sample　quantile　is　often　unknown.　In　this　paper,　we　study　the　performance　of　four　nonparametric　methods,　the　kernel　density　estimation　(KDE),　bootstrap　percentile　(BP),　bootstrap-t　(BT)　and　accelerated　bias-correction　bootstrap　(BCa)　methods,　through　Monte　Carlo　simulations　for　conducting　interval　inference　on　the　quantiles　of　normal　and　generalized　Pareto　distributions.　Simulation　results　show　that　the　BCa　and　BP　methods　outperform　the　BT　and　KDE　methods.　Sample　sizes　to　implement　the　recommended　nonparametric　methods　for　inferring　a　range　of　upper　quantiles　are　also　studied　based　on　the　coverage　probability.　©　2017　ISSN　1881-803X.