This　paper　focuses　on　the　variable　selections　for　semiparametric　varying　coefficient　partially　linear　models　when　the　covariates　in　the　parametric　and　nonparametric　components　are　all　measured　with　errors.　A　bias-corrected　variable　selection　procedure　is　proposed　by　combining　basis　function　approximations　with　shrinkage　estimations.　With　appropriate　selection　of　the　tuning　parameters,　the　consistency　of　the　variable　selection　procedure　and　the　oracle　property　of　the　regularized　estimators　are　established.　A　simulation　study　and　a　real　data　application　are　undertaken　to　evaluate　the　finite　sample　performance　of　the　proposed　method.　(C)　2010　Elsevier　Inc.　All　rights　reserved.