Let (X, Y) be a bivariate random variable having a joint density function f(x, y). If EY is finite, then the regression function m (of Y on X) may be defined as m(x)=E(Y|X=x). Suppose that (X1,Y1),…,(Xn,...Let (X, Y) be a bivariate random variable having a joint density function f(x, y). If EY is finite, then the regression function m (of Y on X) may be defined as m(x)=E(Y|X=x). Suppose that (X1,Y1),…,(Xn, Yn) is a random sample from (X, Y). The kernel regression function estimate can be obtained by展开更多
文摘Let (X, Y) be a bivariate random variable having a joint density function f(x, y). If EY is finite, then the regression function m (of Y on X) may be defined as m(x)=E(Y|X=x). Suppose that (X1,Y1),…,(Xn, Yn) is a random sample from (X, Y). The kernel regression function estimate can be obtained by