Assessing Fit and Dimensionality in Least Squares Metric Multidimensional Scaling Using Akaike's Information Criterion

Ding CS; Davison ML

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Assessing Fit and Dimensionality in Least Squares Metric Multidimensional Scaling Using Akaike's Information Criterion

机译：使用Akaike的信息准则评估最小二乘指标多维缩放的适合度和维数

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Akaike's information criterion is suggested as a tool for evaluating fit and dimensionality in metric multidimensional scaling that uses least squares methods of estimation. This criterion combines the least squares loss function with the number of estimated parameters. Numerical examples are presented. The results from analyses of both simulation data and real data demonstrate the utility of the Akaike's information criterion in identifying the best approximating models in multidimensional scaling analyses.

机译：建议使用Akaike的信息标准作为一种评估工具，该工具使用最小二乘估计法来评估度量多维标度中的契合度和维数。该准则将最小二乘损失函数与估计参数的数量结合在一起。给出了数值示例。对模拟数据和实际数据的分析结果表明，Akaike的信息标准在识别多维比例分析中的最佳近似模型方面具有实用性。

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《Educational and Psychological Measurement》 |2010年第2期|共16页
作者
Ding CS; Davison ML;
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正文语种 eng
中图分类教育心理学;
关键词
MDS; assessing dimensionality; AIC;

机译：MDS;评估维度;AIC;

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1. Assessing Fit and Dimensionality in Least Squares Metric Multidimensional Scaling Using Akaike's Information Criterion [J] . Ding CS, Davison ML Educational and Psychological Measurement . 2010,第2期

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8. Post-Training Performance Criterion Development and Application. The Development of Unidimensional Scales for the Dimensions Derived from a Multidimensional Scale Analysis of the Job of the Naval Aviation Electronics Technician [R] . Schultz, D. G., Siegel, A. I. 1964

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Assessing Fit and Dimensionality in Least Squares Metric Multidimensional Scaling Using Akaike's Information Criterion

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