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Showing 4 results for Optimal Design
Mahboobeh Doosti Irani, Saeid Pooladsaz,
Volume 5, Issue 2 (2-2012)
Abstract
Consider an experimental situation where it is desired to compare more than one test treatments with a control treatment. In this paper a method is presented for achieving E-optimal incomplete block design for this situation under the assumption that the observations within each block are correlated. Then an algorithm is provided for making optimal design based on above-mentioned method. This algorithm for any correlation structure with negative non-diagonal elements is applicable.
Habib Jafari, Shima Pirmohamadi,
Volume 10, Issue 2 (2-2017)
Abstract
The optimal criteria are used to find the optimal design in the studied model. These kinds of models are included the paired comparison models. In these models, the optimal criteria (D-optimality) determine the optimal paired comparison. In this paper, in addition to introducing the quadratic regression model with random effects, the paired comparison models were presented and the optimal design has been calculated for them.
Habib Jafari, Samira Amibigi, Parisa Parsamaram,
Volume 11, Issue 1 (9-2017)
Abstract
Most of the research of design optimality is conducted on linear and generalized linear models. In applicable studies, in agriculture, social sciences, etc, usually in addition to fixed effects, there is also at least one random effect in the model. These models are known as mixed models. In this article, Beta regression model with a random intercept is considered as a mixed model and locally D-optimal design is calculated for simple and quadratic forms of the model and the trend of changes of optimal design points for different parameter values will be studied. For the simple model, a two point locally D-optimal design has been obtained for different parameter values and in the quadratic model, a three point locally D-optimal design has been acquired. Also, according to the efficiency criterion, these locally D-optimal designs are compared with the same designs. It was observed that the efficiency of optimal design, when the random intercept is not considered in the model is lower than the case in which the random effect is considered.
Mehdi Kiani,
Volume 17, Issue 1 (9-2023)
Abstract
In the 1980s, Genichi Taguchi, a Japanese quality advisor, claimed that most of the variability affiliated with the response could be attributed to the company of unmanageable (noise) factors. In some practical cases, his modeling proposition evidence leads the quality improvement to many runs in a crossed array. Hence, several researchers have em-braced noteworthy attitudes of response surface methodology along with the robust parameter design action as alternatives to Taguchi's plan. These alternatives model the response's mean and variance corresponding to the combination of control and noise factors in a combined array to accomplish a robust process or production. Indeed, using response surface methods to the robust parameter design minimises the impression of noise factors on assembling processes or productions. This paper intends to develop further modeling of the predicted response and variance in the presence of noise factors based on unbiased and robust estimators. Another goal is to design the experiments according to the optimal designs to improve these estimators' accuracy and precision simultaneously.
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