We introduce a new method for generating optimal split-plot designs. These designs are optimal in the sense that they are efficient for estimating the fixed effects of the statistical model that is appropriate given the split-plot design structure. One advantage of the method is that it does not...

In many industrial experiments, some of the factors are not independently reset for each run. This is due to time and/or cost constraints and to the hard-to-change nature of these factors. Most of the literature restricts the attention to split-plot designs in which all the hard-to-change...

Stated choice experiments are conducted to identify the attributes that drive people's preferences when choosing between competing options of products or services. They are widely used in transportation in order to support the decision making of companies and governmental authorities. A large...

Designed experiments allow researchers to explore and understand the relation between sets of experimental factors and one or more responses. However, there are many cases where the experimenter cannot determine the factor levels freely. In this article, we study a baking experiment where the...

Many factorial experiments yield categorical response data. Moreover, the experiments are often run under a restricted randomization for logistical reasons and/or because of time and cost constraints. The combination of categorical data and restricted randomization necessitates the use of...

Generalized Aberration (GA) is one of the most frequently used criteria to quantify the suitability of an orthogonal array (OA) to be used as an experimental design. We demonstrate that this criterion is less suitable than other criteria to classify multi-level OAs of strength 3. For this...

In industrial experiments, there are often restrictions in randomization caused by equipment and resource constraints, as well as budget and time restrictions. Next to the split-plot and the split-split-plot design, the staggered-level design is an interesting design option for experiments...

This paper presents a general method for nding optimal blocking arrangements of pure-level and mixed-level orthogonal designs of resolution 3. The method requires enumeration of all non-isomorphic `parent designs' that include the treatment factors as well as the future blocking factor. For all...

Using maximum likelihood estimation for discrete choice modeling of small datasets causes two problems. The first problem is that the data often exhibit separation, in which case the maximum likelihood estimates do not exist. Also, provided they exist, the maximum likelihood estimates are...

In mixture experiments, the factors under study are proportions of the ingredients of a mixture. The special nature of the factors in a mixture experiment necessitates specific types of regression models, and specific types of experimental designs. Although mixture experiments usually are...