Markon Kristian E, Krueger Robert F
Department of Psychology, University of Minnesota, Twin Cities Campus, Minneapolis, MN 55455, USA.
Psychol Methods. 2006 Sep;11(3):228-43. doi: 10.1037/1082-989X.11.3.228.
Distinguishing between discrete and continuous latent variable distributions has become increasingly important in numerous domains of behavioral science. Here, the authors explore an information-theoretic approach to latent distribution modeling, in which the ability of latent distribution models to represent statistical information in observed data is emphasized. The authors conclude that loss of statistical information with a decrease in the number of latent values provides an attractive basis for comparing discrete and continuous latent variable models. Theoretical considerations as well as the results of 2 Monte Carlo simulations indicate that information theory provides a sound basis for modeling latent distributions and distinguishing between discrete and continuous latent variable models in particular.
区分离散型和连续型潜在变量分布在行为科学的众多领域中变得越来越重要。在此，作者探索了一种用于潜在分布建模的信息论方法，该方法强调潜在分布模型在观测数据中表示统计信息的能力。作者得出结论，随着潜在值数量的减少，统计信息的损失为比较离散型和连续型潜在变量模型提供了一个有吸引力的基础。理论考量以及两个蒙特卡洛模拟的结果表明，信息论为潜在分布建模，特别是区分离散型和连续型潜在变量模型，提供了坚实的基础。
