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Latent GOLD Choice 把诺贝「尔的技术应用到了下一阶段。它提供了两种方法去分析选择信息。
从1970年，一个有力的方法学被提议到分析被告人选择并且使用结果效用值实用参数去计算在不同竞争情景中的份额估算。被提议的随机效用模型，现在被称为条件逻辑，多项的逻辑或者集合选择模型，使创始人获得诺贝尔奖。
最 近，集合模型得到了改进，,考虑到不同在选择中有不同的偏好的事实。结果就是一个模型产生更好的份额估计，同时识别重要的片段和每个片段估计的分享。 Latent GOLD Choice代表为发展先进选择模型的GOLD的标准。选择数据从调查或者受访者评价/排名/选择产品/服务/备选方案//选择时的实际行为。选择模型不 同于∴传统的回归模型，因为选择被预测作为一个选择替代的特有的功能。每一■个供替代的选择/产品/服务/选项都有特性。什么是估算是这些特性的价值/实用 性。Latent级别代表给予各种属性微分重视的片段。
Latent GOLD® Choice takes the Nobel prize-winning methodology to the next level
During the 1970 a powerful methodology was proposed for analyzing respondent choices and using the resulting part-worth utility parameters to calculate share estimates under different competitive scenarios. The proposed random utility model, now referred to as the conditional logit, multinomial logit or aggregate choice model, earned the author a Nobel prize. (See http://elsa.berkeley.edu/~mcfadden/iatbr00.html)
Recently, this aggregate model has been improved to allow for the fact that different consumer segments utilize different preferences in making their choices. The result is a model that produces better share estimates by simultaneously identifying the important segments and the estimated share for each segment. Latent GOLD Choice represents the GOLD standard for developing advanced choice models. Choice data is obtained from surveys or actual behavior where respondents rate/rank/choose products/services/alternatives/options. Choice models differ from traditional regression models in that choices are predicted as a function of characteristics of the choice alternatives. Each alternative/product/service/option has attributes. What is estimated is the importances/utilities of these attributes. Latent classes represent segments that give differential importance to the various attributes.
Hear from the Experts
See what the experts have to say about the future of conjoint and choice modeling. "Wish List for Conjoint Analysis" by Eric Bradlow and comments by Jordan Louviere, Bryan Orme, Joffre Swait, Jeroen Vermunt and Jay Magidson. Download a zip file (42K) or a pdf file of all of the articles (65K), or read them individually in our Articles section.
Latent Class models provide the best way to analyze choice data.
The two most popular ways to take into account differences in respondent preferences are Hierarchical Bayes (HB) models and Latent Class (LC) models, also know as finite mixture models. A recent extensive comparison of the two was made by Andrews, Ainslie and Currim, (2002), 擜n empirical comparison of logit choice models with discrete vs. continuous representations of heterogeneity? Journal of Marketing Research, Vol. XXXIX (November), 479-487. In a followup publication by Andrews and Currim, (May 2003, JMR), the authors refer to their earlier work as 搮showing that finite mixture [LC] models are at least as effective as more recent methods [HB] for recovering heterogeneity 厰. Added to the fact that the Latent GOLD Choice program can estimate models in a fraction of the time that it takes to estimate HB models, plus provides many additional capabilities, we believe that Latent GOLD Choice is the GOLD standard for advanced choice modeling.
Specifically, The LC models as implemented in Latent GOLD® Choice provide the following advantages over HB models:
Much faster estimation -- Typical models are estimated in seconds or minutes as opposed to the hours required to estimate HB models.
Simultaneous segmentation - In addition to individual level part-worth utility estimates, segments are identified simultaneously with the estimation of their utilities.
Inclusion of covariates to describe/ predict segments. In addition to differing in preferences, covariates can be included in the model to see how the segments differ with respect to demographics and other respects.
Justified statistically as part of the maximum likelihood (ML) framework. The ML framework allows numerous hypotheses to be tested.
