In　this　paper,　we　propose　a　new　method,　namely　the　level-value　estimation　method,　for　finding　global　minimizer　of　continuous　optimization　problem.　For　this　purpose,　we　define　the　variance　function　and　the　mean　deviation　function,　both　depend　on　a　level　value　of　the　objective　function　to　be　minimized.　These　functions　have　some　good　properties　when　Newton＇s　method　is　used　to　solve　a　variance　equation　resulting　by　setting　the　variance　function　to　zero.　We　prove　that　the　largest　root　of　the　variance　equation　equals　the　global　minimal　value　of　the　corresponding　optimization　problem.　We　also　propose　an　implementable　algorithm　of　the　level-value　estimation　method　where　importance　sampling　is　used　to　calculate　integrals　of　the　variance　function　and　the　mean　deviation　function.　The　main　idea　of　the　cross-entropy　method　is　used　to　update　the　parameters　of　sample　distribution　at　each　iteration.　The　implementable　level-value　estimation　method　has　been　verified　to　satisfy　the　convergent　conditions　of　the　inexact　Newton　method　for　solving　a　single　variable　nonlinear　equation.　Thus,　convergence　is　guaranteed.　The　numerical　results　indicate　that　the　proposed　method　is　applicable　and　efficient　in　solving　global　optimization　problems.