In　this　paper,　we　present　a　variable　selection　procedure　by　combining　basis　function　approximations　with　penalized　estimating　equations　for　semiparametric　varying-coefficient　partially　linear　models　with　missing　response　at　random.　The　proposed　procedure　simultaneously　selects　significant　variables　in　parametric　components　and　nonparametric　components.　With　appropriate　selection　of　the　tuning　parameters,　we　establish　the　consistency　of　the　variable　selection　procedure　and　the　convergence　rate　of　the　regularized　estimators.　A　simulation　study　is　undertaken　to　assess　the　finite　sample　performance　of　the　proposed　variable　selection　procedure.