Limiting the possible influence of time order effects may be an important consideration in designing a sequential experiment. We consider the problem of finding trend robust run orders of two-level factorials when the time effects are modelled via a first order autoregressive error model or via a time series model. In both cases, run orders are trend robust if the factors change level many times. Other run orders can be quite inefficient, even if they are free of low-degree polynomial trends. We present a simple algorithm for constructing run orders of two-level factorial designs with many level changes
Saunders & Eccleston (1992) presented an approach to the design of 2‐level factorial experiments for...
Random execution of run sequences is employed in response surface designs to avoid bias in the respo...
Gaining competitive advantage requires today’s businesses to innovate at an increasing speed. New pr...
The problem of constructing trend-resistant factorial designs is discussed. Suppose a factorial expe...
When a multifactor experiment is carried out over a period of time, the responses may depend on a ti...
The response from a factorial experiment carried out in a time sequence may be affected by uncontrol...
When experiments are to be performed in a time sequence, the observed responses are affected by a ti...
When performing an experiment in a time sequence, the experimenter has reason to believe that the ob...
When experiments are carried out over a period of time, the response may be subject to time trends. ...
Common experimental practices suggest randomizing the order in which runs are performed. However, th...
In numerous experimental settings, engineers and scientists might be obliged to run their experiment...
When performing an experiment, the observed responses are often influenced by a temporal trend due t...
When performing an experiment, the observed responses are often influenced by a temporal trend due t...
Using a systematic run order can be the proper way to conduct an experiment when a temporal trend is...
In this paper we propose tests for the null hypothesis that a time series process displays a constan...
Saunders & Eccleston (1992) presented an approach to the design of 2‐level factorial experiments for...
Random execution of run sequences is employed in response surface designs to avoid bias in the respo...
Gaining competitive advantage requires today’s businesses to innovate at an increasing speed. New pr...
The problem of constructing trend-resistant factorial designs is discussed. Suppose a factorial expe...
When a multifactor experiment is carried out over a period of time, the responses may depend on a ti...
The response from a factorial experiment carried out in a time sequence may be affected by uncontrol...
When experiments are to be performed in a time sequence, the observed responses are affected by a ti...
When performing an experiment in a time sequence, the experimenter has reason to believe that the ob...
When experiments are carried out over a period of time, the response may be subject to time trends. ...
Common experimental practices suggest randomizing the order in which runs are performed. However, th...
In numerous experimental settings, engineers and scientists might be obliged to run their experiment...
When performing an experiment, the observed responses are often influenced by a temporal trend due t...
When performing an experiment, the observed responses are often influenced by a temporal trend due t...
Using a systematic run order can be the proper way to conduct an experiment when a temporal trend is...
In this paper we propose tests for the null hypothesis that a time series process displays a constan...
Saunders & Eccleston (1992) presented an approach to the design of 2‐level factorial experiments for...
Random execution of run sequences is employed in response surface designs to avoid bias in the respo...
Gaining competitive advantage requires today’s businesses to innovate at an increasing speed. New pr...