论文信息 - Sequential Designs for Binary Data with the Purpose to Maximize the Probability of Response

Sequential Designs for Binary Data with the Purpose to Maximize the Probability of Response

Two kinds of sequential designs are proposed for finding the point that maximizes the probability of response assuming a binary response variable and a quadratic logistic regression model. One is a parametric optimal design approach, and the other one is a nonparametric stochastic approximation approach. The suggested sequential designs are evaluated and compared in a simulation study. In summary, the parametric approach performed very well whereas its competitor failed in some cases.

Ellinor Fackle Fornius

[1] H. Robbins. A Stochastic Approximation Method , 1951 .

[2] C. F. Wu,et al. Efficient Sequential Designs with Binary Data , 1985 .

[3] P. McCullagh,et al. Generalized Linear Models , 1972, Predictive Analytics.

[4] J. Kiefer,et al. Optimum Designs in Regression Problems , 1959 .

[5] H. Robbins,et al. Adaptive Design and Stochastic Approximation , 1979 .

[6] W. Näther. Optimum experimental designs , 1994 .

[7] A. Albert,et al. On the existence of maximum likelihood estimates in logistic regression models , 1984 .

[8] J. Sacks. Asymptotic Distribution of Stochastic Approximation Procedures , 1958 .

[9] George E. P. Box,et al. Empirical Model‐Building and Response Surfaces , 1988 .

[10] D. Anbar. A stochastic Newton-Raphson method , 1978 .

[11] Changbao Wu. Maximum likelihood recursion and stochastic approximation in sequential designs , 1986 .

[12] J. Kiefer,et al. Stochastic Estimation of the Maximum of a Regression Function , 1952 .

[13] Eric R. Ziegel,et al. An Introduction to Generalized Linear Models , 2002, Technometrics.