The　mixed　continuous-discrete　density　model　plays　an　important　role　in　reliability,　finance,　biostatistics,　and　economics.　Using　wavelets　methods,　Chesneau,　Dewan,　and　Doosti　provide　upper　bounds　of　wavelet　estimations　on　L-2　risk　for　a　two-dimensional　continuous-discrete　density　function　over　Besov　spaces　B-r,q(s).　This　paper　deals　with　L-p　(1　＜=　p　＜　infinity)　risk　estimations　over　Besov　space,　which　generalizes　Chesneau-Dewan-Doosti＇s　theorems.　In　addition,　we　firstly　provide　a　lower　bound　of　Lp　risk.　It　turns　out　that　the　linear　wavelet　estimator　attains　the　optimal　convergence　rate　for　r　＞=　p,　and　the　nonlinear　one　offers　optimal　estimation　up　to　a　logarithmic　factor.