Showing papers by "William E. Strawderman published in 1986"
TL;DR: Application of the method in the case where the objective is to estimate volume per acre and prior knowledge is represented by a yield equation demonstrates that this method can reduce the amount of sample information that would be required if the yield equation were to be ignored.
Abstract: A method for determining the appropriate sample size to produce an estimate with a stated allowable percent error when the sample data is to be combined with prior information is presented. Application of the method in the case where the objective is to estimate volume per acre and prior knowledge is represented by a yield equation demonstrates that this method can reduce the amount of sample information that would be required if the yield equation were to be ignored.
12 citations
TL;DR: A Stein-rule estimator, which shrinks least squares estimates of regression parameters toward their weighted average, was employed to estimate the coefficient in the constant form factor volume equation for 18 species simultaneously, revealing that the superiority of Stein- Rule procedures over least squares decreased as the sample size increased and that the Stein- rule procedures were robust to unequal sample sizes.
Abstract: A Stein-rule estimator, which shrinks least squares estimates of regression parameters toward their weighted average, was employed to estimate the coefficient in the constant form factor volume equation for 18 species simultaneously. The Stein-rule procedure was applied to ordinary least squares estimates and weighted least squares estimates. Simulation tests on independent validation data sets revealed that the Stein-rule estimates were biased, but predicted better than the corresponding least squares estimates. The Stein-rule procedures also yielded lower estimated mean square errors for the volume equation coefficient than the corresponding least squares procedure. Different methods of withdrawing sample data from the total sample available for each species revealed that the superiority of Stein-rule procedures over least squares decreased as the sample size increased and that the Stein-rule procedures were robust to unequal sample sizes, at least on the scale studied here.
10 citations
TL;DR: In this paper, the authors assume that the distribution of the third component is of the form of a strong mixture of noramal distribution and use this distribution for the problem of estimating the vector under the loss function.
Abstract: Let X be a vactor of independent components with mean vectoro θ We assume that the distribution of the jth component is of the form ieavarliant mixture of noramal distribution are minimax for the problem of estimating the vector under the loss function
7 citations