Background and objective: Binary response models are used in many real applications. For these models the Fisher information matrix (FIM) is proportional to the FIM of a weighted simple linear regression model. The same is also true when the weight function has a finite integral. Thus, optimal designs for one binary model are also optimal for the corresponding weighted linear regression model. The main objective of this paper is to provide a tool for the construction of MV-optimal designs, minimizing the maximum of the variances of the estimates, for a general design space. Methods: MV-optimality is a potentially difficult criterion because of its nondifferentiability at equal variance designs. A methodology for obtaining MV-opti...
For the binary response model, we determine optimal designs based on the D-optimal criterion which a...
Available from Bibliothek des Instituts fuer Weltwirtschaft, ZBW, D-21400 Kiel C 205043 / FIZ - Fach...
We investigate the problem of designing for linear regression models, when the assumed model form is...
Background and objective: Binary response models are used in many real applications. For these m...
Maximum variance (MV) and Standarized maximum variance (SMV) optimum designs for binary response mod...
[[abstract]]In this thesis, we are interested in finding optimal minimax designs for heteroscedastic...
Recently, Dette and Sahm (1998) have put forward a procedure to construct MV-and SMV-optimurn design...
We establish an extension, to the case of multiple regression, of a result on minimax simple regress...
For many problems of statistical inference in regression modelling, the Fisher informa-tion matrix d...
For the binary response model, we determine optimal designs based on the D-optimal criterion which a...
AbstractWe establish an extension, to the case of multiple regression, of a result on minimax simple...
In the common nonparametric regression model y_i = g(t_i) + \sigma (t_i)\, \varepsilon_i,\,\, i = 1...
International audienceBackground and objectives: To optimize designs for longitudinal studies analyz...
Some procedures to construct local MV- and SMV-optimumdesigns for binaryresponsemodels are already a...
Chapter 1 provides an introduction to the area of optimum experimental design for the linear regress...
For the binary response model, we determine optimal designs based on the D-optimal criterion which a...
Available from Bibliothek des Instituts fuer Weltwirtschaft, ZBW, D-21400 Kiel C 205043 / FIZ - Fach...
We investigate the problem of designing for linear regression models, when the assumed model form is...
Background and objective: Binary response models are used in many real applications. For these m...
Maximum variance (MV) and Standarized maximum variance (SMV) optimum designs for binary response mod...
[[abstract]]In this thesis, we are interested in finding optimal minimax designs for heteroscedastic...
Recently, Dette and Sahm (1998) have put forward a procedure to construct MV-and SMV-optimurn design...
We establish an extension, to the case of multiple regression, of a result on minimax simple regress...
For many problems of statistical inference in regression modelling, the Fisher informa-tion matrix d...
For the binary response model, we determine optimal designs based on the D-optimal criterion which a...
AbstractWe establish an extension, to the case of multiple regression, of a result on minimax simple...
In the common nonparametric regression model y_i = g(t_i) + \sigma (t_i)\, \varepsilon_i,\,\, i = 1...
International audienceBackground and objectives: To optimize designs for longitudinal studies analyz...
Some procedures to construct local MV- and SMV-optimumdesigns for binaryresponsemodels are already a...
Chapter 1 provides an introduction to the area of optimum experimental design for the linear regress...
For the binary response model, we determine optimal designs based on the D-optimal criterion which a...
Available from Bibliothek des Instituts fuer Weltwirtschaft, ZBW, D-21400 Kiel C 205043 / FIZ - Fach...
We investigate the problem of designing for linear regression models, when the assumed model form is...