Sentence examples for preference estimates from inspiring English sources

Preference estimates based on market data and hypothetical market situations are called "revealed preferences" (RP) and "stated preferences" (SP), where the notations show whether the actual or hypothetical choice is used.

Once having taken into account potential differences in scale (inversely proportional to the variance of the random term in random utility models), preference estimates for beef attributes are found to be sensitive to the design generation process.

The DM, through experience, can use the robustness index and other information generated in calculating the index to determine what action to take: make a final selection from the present set of non-eliminated designs; improve the precision of the preference estimates; or otherwise cope with the imprecision.

Thus, reliability of pictures may represent a possible methodological pitfall that potentially decreases precision of human preference estimates.

The class-specific preference estimates are presented in Table  4.

Country or region-specific preference estimates may benefit local economic evaluations.

Path preference estimated using the simple spatial reconstruction algorithm also showed an upcoming path to a memory-guided goal, similarly to the Bayesian decoder.

Higher physician preference, estimated using their prescribing habits, was associated with decreased discontinuation risk in patients with RA treated with TNF antagonists.

In addition to information about effects of interventions, health care decisions require consideration of issues around the clinical state and circumstances including baseline risks for a certain outcome, the populations and societal values and preferences, estimates of effect and expertise to bring all of that together.

These are business-to-business companies that collect thousands of details — like the shopping habits, vacation preferences, estimated income, ethnicity, hobbies, predilections for gambling or smoking and health concerns — about millions of consumers, the better to help marketers identify potential new customers as well as maintain their already loyal clients.

Moreover, to categorize customer requirements on the basis of the aggregate consumer preferences estimated by the MOCM model, we extend the Kano model and propose a marginal effect-based Kano model (MEKM).