2.(2021,I)设(X 1,Y1),(X2,Y2 ),···,(xn,Yn)为来自总体N(μ1,-|||-μ2;σ1^2,σ2^2;ρ)的简单随机样本,令 theta =(mu )_(1)-(mu )_(2), overline (X)=dfrac (1)(n)sum _(i=1)^n(X)_(i), overline (Y)=dfrac (1)(n)sum _(i=1)^n(Y)_(i),-|||-hat (theta )=overline (X)-overline (Y), 则 () .-|||-(A)θ是θ的无偏估计, (hat (theta ))=dfrac ({{sigma )_(1)}^2+({sigma )_(2)}^2}(n)-|||-(B)θ不是θ的无偏估计, (hat (theta ))=dfrac ({{sigma )_(1)}^2+({sigma )_(2)}^2}(n)-|||-(C)θ是θ的无偏估计, (hat (theta ))=dfrac ({{sigma )_(1)}^2+({sigma )_(2)}^2-2rho (sigma )_(1)(sigma )_(2)}(n)-|||-(D)θ不是θ的无偏估计, (hat (theta ))=dfrac ({{sigma )_(1)}^2+({sigma )_(2)}^2-2rho ({sigma )_(1)(sigma )_(2)}}(n)

3.(2021,Ⅲ)设(X1 Y1),(X2,Y2),···,(Xn,Yn)为来自总体N(μ1,-|||-μ2;σ1^2,σ2^2,ρ)的简单随机样本,令 th

例 4-9 (2021年是,数三)设(X1,Y1 ) (X2,Y2),...(Xn,Yn)为来自总-|||-N(μ1,μ2;σ1^2,σ2;ρ)的简单随机样本,

设 X_1, X_2, ..., X_n 为来自总体 N(mu_1, sigma^2) 的简单随机样本，Y_1, Y_2, ..., Y_m 为来自总体 N(m

-|||-设X1,X2,···,xn为来自均值为θ的指数分布总体的简单随机样本,Y1,Y2,···,yn=来自均值为-|||-2θ的指数分布总体的简单随机样本,

3.设总体 approx N(mu ,(sigma )^2), X1,X2···Xn是来自该总体的简单随机样本,则-|||-dfrac (1)({sigma )

2.(2018,Ⅲ)设X1,X2,···, _(n)(ngeqslant 2) 为来自总体 (mu ,(sigma )^2)(sigma gt 0) 的-|||

574 设总体X服从正态分布N(μ,σ^2),X1,X2,···,Xn是来自总体X的简单随机样本,据-|||-此样本检验假设: _(0):mu =(mu )_(

574 设总体X服从正态分布N(μ,σ^2),X1,X2,···,xn是来自总体X的简单随机样本,据-|||-此样本检验假设: _(0):mu =(mu )_(

设(X1,X2,···,Xn )为来自正态总体 (mu ,(sigma )^2)设(X1,X2,···,Xn )为来自正态总体 (mu ,(sigma )^2)

[问答题]设随机变量X1，X2，…，Xn相互独立，且均在区间[0，θ]上服从于均匀分布，设Y1=max{X1，X2，…Xn}，Y2=min{X1，X2，…Xn}