This　paper　provides　upper　bounds　of　wavelet　estimations　on　L-p　(1　＜=　p　＜　infinity)　risk　for　a　density　function　in　Besov　spaces　based　on　negatively　associated　stratified　size-biased　random　samples.　It　turns　out　that　the　classical　theorem　of　Donoho,　Johnstone,　Kerkyacharian　and　Picard　is　completely　extended　to　more　general　cases.　More　precisely,　we　consider　the　model　with　multiplication　noise　and　allow　the　sample　negatively　associated.　Our　theory　is　illustrated　with　a　simulation　study.