The authors report results of a survey conducted to update a previous one on the commercial use of conjoint analysis. They document an extensive number of applications and show systematic changes in their characteristics consistent with research results reported in the literature. Issues relevant to the options available to analysts involved in the conduct of conjoint analysis are identified and discussed.

This study had institutional review board approval; written informed consent was obtained. The purpose was to prospectively determine the heart rate (HR) dependency of three-dimensional (3D) coronary artery motion by incorporating into analysis the durations of systole and diastole. Thirty patients (seven women, 23 men; mean age, 56.6 years +/- 12.7 [standard deviation]; HR: 45-100 beats per minute) underwent electrocardiographically gated 64-section computed tomographic (CT) coronary angiography to determine coronary motion velocities at bifurcation points. Significance of velocity differences (P < .05) was determined by using analysis of variance for repeated measures and Bonferroni post hoc tests. HR dependency was determined by using linear regression analysis. HR significantly affected 3D coronary motion (r = 0.47, P < .009) through nonproportional shortening of systole and diastole (r = -0.82, P < .001), leading to percentage reconstruction interval shifts of coronary velocity troughs and peaks (P < .01). Results suggest that image reconstruction algorithms at CT coronary angiography be adapted to the individual patient's HR.

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There are two ways to estimate the predictive power of a regression model: a cross-validation procedure and a formula. A number of formulas have been derived. A review of the literature leads to four (unbiased or least biased) formulas, each one appropriate depending on whether the predictor variables are fixed or random and on the measure needed, that is, a measure of the absolute error (the mean squared error of prediction) or of the relative error (the cross-validated multiple correlation). The advantages of these formulas over cross-validation are that they are less cumbersome to use and that they produce more precise estimates. The conditions under which it is appropriate to use these formulas are discussed as well as their use for comparing the predictive power of regression, subjective and equal weights.

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Objects are usually embedded into context. Visual context has been successfully used in object detection tasks, however, it is often ignored in object tracking. We propose a method to learn supporters which are, be it only temporally, useful for determining the position of the object of interest. Our approach exploits the General Hough Transform strategy. It couples the supporters with the target and naturally distinguishes between strongly and weakly coupled motions. By this, the position of an object can be estimated even when it is not seen directly (e.g., fully occluded or outside of the image region) or when it changes its appearance quickly and significantly. Experiments show substantial improvements in model-free tracking as well as in the tracking of "virtual" points, e.g., in medical applications.

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Conjoint analysis has been used extensively in marketing research to estimate the impact of selected product (service) characteristics on customer preferences for products (services). In this paper we discuss findings obtained from a survey of commercial users of the methodology. We project that around 1,000 commercial applications have been carried out during the last decade. We discuss the manner in which the methodology is used commercially, remaining issues that deserve further exploration, and recent advances or insights obtained by researchers working in this area.