**Date & Time:** Monday, April 07, 2014, 11:00-12:00.

**Venue:** Room 113

**Title:** Search Designs for Model Selection

**Speaker:** Kashinath Chatterjee, Visva Bharati University

**Abstract:** The major objective of factorial experiments is to provide information on interaction
eﬀects, apart from the general mean and all the main eﬀects. When the number of factors
is large and little prior knowledge is available on the various factorial eﬀects, conventional
fractional factorial experiments that are capable of estimating interactions require too
many observations to be economically viable. The eﬀect hierarchy principle is one of the
most important principles in experimental design. The principle states that (i) lower-order
factorial eﬀects are more likely to be important than higher-order ones, and (ii) eﬀects of
the same order are equally likely to be important. To overcome this problem, interaction
eﬀects of higher orders are frequently dropped from consideration in an experiment and
are assumed to be negligible, often without substantive justiﬁcation. Also, a fractional
factorial design is often run in which only main eﬀects can be investigated (a main eﬀects
design). These practices can result in an inadequate understanding of the joint action of
the factors on the response and in poor predictive models. In industrial experimentation,
it is noted through many research papers that the loss of information on interactions
is a serious problem, because a key tool for product improvement is the exploitation of
interactions between design (control) factors which can be set in the product speciﬁcation
and noise factors which cannot.

It is to be remarked that before performing an experiment, it is generally very difficult to predict the interaction effects that are possibly present in the model. In such a case the designs capable of just estimating the possibly present interaction eﬀects may not be good enough to serve the objective of the experimenter. Moreover, in such a case if one considers only a main eﬀect model, the outcome of the analysis will mislead the experimenter. To cope with this problem, Srivastava (1975) introduced a design criterion that seeks to maximize the ability to discriminate between models. The main objective of this presentation is to introduce the notion of Search Designs, pioneered by Srivatava (1975), and their applications to model selections in fractional factorial experiments.