Single-index　varying　coefficient　model　(SIVCM)　is　a　powerful　tool　for　modeling　nonlinearity　in　multivariate　estimation,　and　has　been　widely　used　in　the　literature　due　to　the　fact　that　it　can　overcome　the　well-known　phenomenon　of　＂curse-of-dimensionality＂.　In　this　paper,　we　consider　the　problem　of　model　detection　and　estimation　for　SIVCM.　Based　on　the　proposed　combined　penalization　procedure,　we　can　identify　the　true　model　structure　consistently,　and　obtain　a　new　semiparametric　model　partially　linear　single-index　varying　coefficient　model　(PLSIVCM).　Under　the　appropriate　conditions,　we　demonstrate　that　the　proposed　penalized　estimators　of　parametric　and　nonparametric　components　of　PLSIVCM　are　consistent,　but　their　asymptotic　distributions　are　not　available.　Hence,　we　extend　the　minimum　average　variance　estimation　method　to　PLSIVCM,　and　establish　the　asymptotic　normality　for　the　refined　estimators　of　index　parameters,　constant　coefficients　and　varying　coefficient　functions,　respectively.　The　finite　sample　performances　of　the　proposed　methods　are　illustrated　by　a　Monte　Carlo　simulation　study　and　the　real　data　analysis.　(C)　2015　Elsevier　Inc.　All　rights　reserved.