Upper　records　are　important　statistics　in　environmental　science　and　many　other　fields.　Because　upper　records　are　crucial　for　policy　making,　precise　modeling　and　inference　techniques　are　in　high　demand.　The　generalized　Pareto　distribution　(GPD)　is　commonly　adopted　by　researchers　for　modeling　heavy　tail　phenomena　in　many　applications.　The　statistical　inference　of　the　GPD　upper　records　is　a　critical　issue　in　record　analysis.　Based　on　upper　record　data,　the　current　parameter　estimation　methods　of　the　GPD　depend　on　preassumed　shape　parameter　and　only　estimate　the　location　and　scale　parameters.　However,　the　shape　parameter　is　typically　unknown　in　real　applications.　In　this　manuscript,　we　propose　a　new　approach　that　can　estimate　all　three　parameters　of　the　GPD.　The　proposed　estimator　is　used　in　conjunction　with　a　moment　method　and　nonlinear　weighted　least　squares　theory　that　minimizes　the　sum　of　squared　deviations　between　the　upper　records　and　their　expectations.　In　simulation　studies,　we　compare　alternative　estimators　and　demonstrate　that　the　new　estimator　is　competitive　in　terms　of　the　bias　and　means　square　error　in　estimating　the　shape　and　scale　parameters.　In　addition,　we　investigate　the　performance　of　different　threshold　selection　procedures　by　estimating　the　Value-at-Risk　(VaR)　of　the　GPD.　Finally,　we　illustrate　the　utilization　of　the　proposed　methods　by　analyzing　an　air　pollution　data.　In　this　analysis,　we　provide　a　detailed　guide　for　selecting　the　threshold　and　upper　records.