??23?2?2?P??S??
2??2?1S23??P??2??
2??2???19S2?P?9.5?2?28.5????
其中?=219S2?2~?2(19),则
?2???2?19S22P?S????P9.5??28.5???22????? ?P?9.5??2?28.5??0.95?0.05?0.9 3)
?X???X??1??S?20?P??c??P??P???X??SccS/20??????
其中,T=X??S/20~t(19),则
??S?20?P??c??P?T0.9 ???X??c????所以:
20?t0.9(19)=1.328,计算得:c?3.3676. c2 27 设总体X的均值?与方差?存在,若X1,X2,?,Xn为它的一个样本,X是样本均值,试证明对i?j,相关系数r(Xi?X,Xj?X)??1. n?1? 证明 r(Xi?X,Xj?X)covX(i?XX,j?Xn?12? n)D(Xi?X)DX(j?X)
D(Xi?X)?D(Xj?X)?1Cov(Xi?X,Xj?X)?E(XiXj?XiX?XjX?XX)???2
n1所以:r(Xi?X,Xj?X)??.
n?128. 设总体X~N(?,?2),从该总体中抽取简单随机样本X1,X2,?,X2n(n?1),X是
它的样本均值,求统计量T??(Xi?1ni?Xn?i?2X)2的数学期望.
解 因X~N(?,?2),X1,X2,?,X2n(n?1)为该总体的简单随机样本,令
Yi?Xi?Xn?i,则有Yi~N(2?,2?2)
1n 可得:Y??Yi?2X
ni?1T??(Xi?Xn?i?2X)???Yi?Y??(n?1)SY2
22i?1i?1nnET?(n?1)ESY2?2(n?1)?2
习题二
1 设总体的分布密度为:
?(??1)x?,0?x?1f(x;?)??
其它?0,(X1,,Xn)为其样本,求参数?的矩估计量??1和极大似然估计量??2 .现测得样本观测值
为:0.1,0.2,0.9,0.8,0.7,0.7,求参数?的估计值 .
解 计算其最大似然估计:
L(?,x1lnL(?,x1xn)???????1?xi??????1??i?1nn?x?ii?1nxn)?nln(??1)???lnxii?1n
ndn?lnL(?,x1xn)????lnxi?0d???1i?1n
?2??1?n??0.2112?lnxii?1 其矩估计为:
X?13.4?0.1?0.2?0.9?0.8?0.7?0.7?? 6611x??2??11?2X??1?1?EX??(??1)xdx?(??1)??X,??0.3077??20??2X?10
??1?2Xn?????,???1?所以:12nX?1???lnXii?1????, ????1?0.3077,??2?0.2112 ?.
2 设总体X服从区间[0, ?]上的均匀分布,即X~U[0,?],(X1,,Xn)为其样本,
1)求参数?的矩估计量??1和极大似然估计量??2;
2)现测得一组样本观测值:1.3,0.6,1.7,2.2,0.3,1.1,试分别用矩法和极大似然法求总体均值、总体方差的估计值. 解 1)矩估计量:
EX???12??2X?2. 4?X,?1最大似然估计量:
L(?,x1lnL(?,x1xn)??i?1n1?n?1?n
xn)????0?无解 .此时,依定义可得:?22)矩法:EX??maxXi
1?i?n??12?1.2,DX???22?2?112?0.472
?2?212 极大似然估计:EX??1.1,DX??0.4033
.
3 设X1,...,Xn是来自总体X的样本,试分别求总体未知参数的矩估计量与极大似然估计量 .已知总体X的分布密度为:
?e1)f(x;?)????0,??x,x?0x?0,??0未知
2)f(x;?)??xx!e,??x?0,1,2,,??0未知
?1,?3)f(x;a,b)??b?a??0a?x?b其它,a?b未知
??x4) f(x;?)???0?2,0???x???其它,?未知
?1e?5)f(x;?,?)?????0,?(x??)/?,x??x??,??0,其中参数?,?未知
???6)f(x;?,?)??????0,?4x? 7)f(x;?)?????0,?23x??1,0?x??x??,?,??0,其中参数?,?未知
x?2e?2,x?0x?0,??0未知
8)
解 1)
f(x;?)?(x?1)?(1??),x?2,3,2x?2,0???1
矩法估计:EX?最大似然估计:
??1 ?X,?1?X?xii?1n1L(?,x1xn)???ei?1n??xi??enn??,lnL(?,x1xn)?nln????xi
i?1ndnn??lnL???xi?0,?2d??i?12)
X~P(?) 矩估计:
?xi?1n?i1 X.
??X EX???X,?1n最大似然估计:
L(?,x1xn)??i?1?xxiie???e?n??nx?x .
i,lnL??n??nxln???xi
dnx??XlnL??n??0,?2d??3)
b?a??a?b矩估计:EX? ,DX?212 联立方程:
2?a?b?X?a??2???X????2??X?b??b?a??M*??2??12 最大似然估计: L(?,x1n*3M23M*2
xn)??f(xi;?)?i?11,lnL??nln(b?a) n(b?a)dlnLn??minXi时,使得似然函数最大, ??0,无解,当a1?i?ndab?a?依照定义,a4)
矩估计:
???minXi,同理可得a??maxXi1?i?n1?i?n
.
EX??x0?dx??lnx??0,不存在
最大似然估计:
L(?,x11xn)??2???2,lnL?nln??2?lnxii?1xii?1xinn?n
?n??X lnL??0,无解;依照定义,?(1).
???5)
矩估计:
??EX?
?????x??e?(x??)/?dx??t(???t)edt???(1)???(2) ?0?????X
2?t22(???t)edt???(1)?2???(2)???(3) ?0EX2???2?2???2?2?(???)2??2?M2?12X?i n1??M2?X??Xi2?X2?M*2n
*?*?1?X?M2?,?1?M222