Recent　research　and　substantive　studies　have　shown　growing　interest　in　expectile　regression　(ER)　procedures.　Similar　to　quantile　regression,　ER　with　respect　to　different　expectile　levels　can　provide　a　comprehensive　picture　of　the　conditional　distribution　of　a　response　variable　given　predictors.　This　study　proposes　three　composite　-type　ER　estimators　to　improve　estimation　accuracy.　The　proposed　ER　estimators　include　the　composite　estimator,　which　minimizes　the　composite　expectile　objective　function　across　expectiles;　the　weighted　expectile　average　estimator,　which　takes　the　weighted　average　of　expectile-specific　estimators;　and　the　weighted　composite　estimator,　which　minimizes　the　weighted　composite　expectile　objective　function　across　expectiles.　Under　certain　regularity　conditions,　we　derive　the　convergence　rate　of　the　slope　function,　obtain　the　mean　squared　prediction　error,　and　establish　the　asymptotic　normality　of　the　slope　vector.　Simulations　are　conducted　to　assess　the　empirical　performances　of　various　estimators.　An　application　to　the　analysis　of　capital　bike　share　data　is　presented.　The　numerical　evidence　endorses　our　theoretical　results　and　confirm　the　superiority　of　the　composite　-type　ER　estimators　to　the　conventional　least　squares　and　single　ER　estimators.