优先排序和决策:方法的简要回顾

Q4 Mathematics
S. Lipovetsky
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引用次数: 0

摘要

描述了用于解决优先级问题的统计和决策技术。这些方法包括多属性决策的层次分析法(AHP)及其对统计建模和测试的扩展、优先级估计的缩放技术、最大差分模型、回归中关键驱动因素的识别以及其他方法。所描述的技术已被广泛应用,并被证明有助于识别和订购解决各种营销研究和决策问题中最重要的项目。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prioritization and decision-making: A brief review of methods
Statistical and decision-making techniques for solving prioritization problems are described. These approaches include the analytic hierarchy process (AHP) of the multi-attribute decision-making and its extension to the statistical modeling and testing, scaling techniques of priority estimation, maximum difference models, identification of key drivers in regression, and other methods. The described techniques have been widely applied and proved to be helpful for identification and ordering the most important items in solving various marketing research and decision-making problems.
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来源期刊
Model Assisted Statistics and Applications
Model Assisted Statistics and Applications Mathematics-Applied Mathematics
CiteScore
1.00
自引率
0.00%
发文量
26
期刊介绍: Model Assisted Statistics and Applications is a peer reviewed international journal. Model Assisted Statistics means an improvement of inference and analysis by use of correlated information, or an underlying theoretical or design model. This might be the design, adjustment, estimation, or analytical phase of statistical project. This information may be survey generated or coming from an independent source. Original papers in the field of sampling theory, econometrics, time-series, design of experiments, and multivariate analysis will be preferred. Papers of both applied and theoretical topics are acceptable.
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