Akaike's information criterion and Kullback-Leibler loss for histogram density estimation

1990 ◽  
Vol 85 (4) ◽  
pp. 449-467 ◽  
Author(s):  
Peter Hall
Keyword(s):  
Density Estimation ◽  
Information Criterion ◽  
Akaike's Information Criterion ◽  
Akaike’S Information Criterion

Cluster Analysis of Antigenic Profiles of Tumors: Selection of Number of Clusters Using Akaike’s Information Criterion

1990 ◽  
Vol 29 (03) ◽  
pp. 200-204 ◽  
Author(s):  
J. A. Koziol
Keyword(s):  
Cluster Analysis ◽  
Basic Problem ◽  
Information Criterion ◽  
Akaike's Information Criterion ◽  
Cell Surface Antigens ◽  
Number Of Clusters ◽  
Akaike’S Information Criterion ◽  
Multinomial Data ◽  
Tumor Types ◽  
Selection Of

AbstractA basic problem of cluster analysis is the determination or selection of the number of clusters evinced in any set of data. We address this issue with multinomial data using Akaike’s information criterion and demonstrate its utility in identifying an appropriate number of clusters of tumor types with similar profiles of cell surface antigens.

A Comparison of Segment Retention Criteria for Finite Mixture Logit Models

2003 ◽  
Vol 40 (2) ◽  
pp. 235-243 ◽  
Author(s):  
Rick L. Andrews ◽  
Imran S. Currim
Keyword(s):  
Information Criterion ◽  
Finite Mixture ◽  
Choice Data ◽  
Akaike's Information Criterion ◽  
Experimental Conditions ◽  
Widespread Application ◽  
Akaike’S Information Criterion ◽  
True Number ◽  
Penalty Factor ◽  
Number Of Segments

Despite the widespread application of finite mixture models in marketing research, the decision of how many segments to retain in the models is an important unresolved issue. Almost all applications of the models in marketing rely on segment retention criteria such as Akaike's information criterion, Bayesian information criterion, consistent Akaike's information criterion, and information complexity to determine the number of latent segments to retain. Because these applications employ real-world data in which the true number of segments is unknown, it is not clear whether these criteria are effective. Retaining the true number of segments is crucial because many product design and marketing decisions depend on it. The purpose of this extensive simulation study is to determine how well commonly used segment retention criteria perform in the context of simulated multinomial choice data, as obtained from supermarket scanner panels, in which the true number of segments is known. The authors find that an Akaike's information criterion with a penalty factor of three rather than the traditional value of two has the highest segment retention success rate across nearly all experimental conditions. Currently, this criterion is rarely, if ever, applied in the marketing literature. Experimental factors of particular interest in marketing contexts, such as the number of choices per household, the number of choice alternatives, the error variance of the choices, and the minimum segment size, have not been considered in the statistics literature. The authors show that they, among other factors, affect the performance of segment retention criteria.

Sign in / Sign up

Export Citation Format

Share Document