J. S. Hunter

Bio: J. S. Hunter is an academic researcher from University of Wisconsin-Madison. The author has contributed to research in topics: Fractional factorial design & Yates analysis. The author has an hindex of 2, co-authored 2 publications receiving 906 citations.

The 2 k-p fractional factorial designs part I

Abstract: (2000). The 2 k—p Fractional Factorial Designs Part I. Technometrics: Vol. 42, No. 1, pp. 28-47.

Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices via Stated Preference Experiments

Abstract: Survey experiments are a core tool for causal inference. Yet, the design of classical survey experiments prevents them from identifying which components of a multidimensional treatment are influential. Here, we show how conjoint analysis, an experimental design yet to be widely applied in political science, enables researchers to estimate the causal effects of multiple treatment components and assess several causal hypotheses simultaneously. In conjoint analysis, respondents score a set of alternatives, where each has randomly varied attributes. Here, we undertake a formal identification analysis to integrate conjoint analysis with the potential outcomes framework for causal inference. We propose a new causal estimand and show that it can be nonparametrically identified and easily estimated from conjoint data using a fully randomized design. The analysis enables us to propose diagnostic checks for the identification assumptions. We then demonstrate the value of these techniques through empirical applications to voter decision making and attitudes toward immigrants.

Orthogonal Main-Effect Plans for Asymmetrical Factorial Experiments

Abstract: Plans for asymmetrical factorial experiments which permit uncorrelated estimates of all main effects when the interactions are negligible are described. The construction of these plans is based upon the principle of proportional frequencies of the factor levels. The possibilities of blocking these main-effect plans, the randomization procedure and the method of analysis are presented.

Taguchi's parameter design: a panel discussion

Abstract: It is more than a decade since Genichi Taguchi's ideas on quality improvement were inrroduced in the United States. His parameter-design approach for reducing variation in products and processes has generated a great deal of interest among both quality practitioners and statisticians. The statistical techniques used by Taguchi to implement parameter design have been the subject of much debate, however, and there has been considerable research aimed at integrating the parameter-design principles with well-established statistical techniques. On the other hand, Taguchi and his colleagues feel that these research efforts by statisticians are misguided and reflect a lack of understanding of the engineering principles underlying Taguchi's methodology. This panel discussion provides a forum for a technical discussion of these diverse views. A group of practitioners and researchers discuss the role of parameter design and Taguchi's methodology for implementing it. The topics covered include the importance of vari...

Response surface methodology: 1966–1988

Abstract: Response sarfxe methodology (RSM) is a collection of tools developed in the 1950s for the purpose of determining optimum operating conditions in applications in the chemical industry. This article reviews the progrrss of RSM in the general areas of experimental design and analysis and indicates how its role has been affected by advanccs in other fields of applied statistics. Current areas of research in RSM are highlighted. and areas for future research are discussed.

An Analysis for Unreplicated Fractional Factorials

Abstract: Loss of markets to Japan has recently caused attention to return to the enormous potential that experimental design possesses for the improvement of product design, for the improvement of the manufacturing process, and hence for improvement of overall product quality. In the screening stage of industrial experimentation it is frequently true that the “Pareto Principle” applies; that is, a large proportion of process variation is associated with a small proportion of the process variables. In such circumstances of “factor sparsity,” unreplicated fractional designs and other orthogonal arrays have frequently been effective when used as a screen for isolating preponderant factors. A useful graphical analysis due to Daniel (1959) employs normal probability plotting. A more formal analysis is presented here, which may be used to supplement such plots and hence to facilitate the use of these unreplicated experimental arrangements.