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**Classification in postural style.**
*(English)*
Zbl 1454.62193

Summary: This article contributes to the search for a notion of postural style, focusing on the issue of classifying subjects in terms of how they maintain posture. Longer term, the hope is to make it possible to determine on a case by case basis which sensorial information is prevalent in postural control, and to improve/adapt protocols for functional rehabilitation among those who show deficits in maintaining posture, typically seniors. Here, we specifically tackle the statistical problem of classifying subjects sampled from a two-class population. Each subject (enrolled in a cohort of 54 participants) undergoes four experimental protocols which are designed to evaluate potential deficits in maintaining posture. These protocols result in four complex trajectories, from which we can extract four small-dimensional summary measures. Because undergoing several protocols can be unpleasant, and sometimes painful, we try to limit the number of protocols needed for the classification. Therefore, we first rank the protocols by decreasing order of relevance, then we derive four plug-in classifiers which involve the best (i.e., more informative), the two best, the three best and all four protocols. This two-step procedure relies on the cutting-edge methodologies of targeted maximum likelihood learning (a methodology for robust and efficient inference) and super-learning (a machine learning procedure for aggregating various estimation procedures into a single better estimation procedure). A simulation study is carried out. The performances of the procedure applied to the real data set (and evaluated by the leave-one-out rule) go as high as an 87% rate of correct classification (47 out of 54 subjects correctly classified), using only the best protocol.

### MSC:

62H30 | Classification and discrimination; cluster analysis (statistical aspects) |

### Keywords:

aggregation; classification; cross-validation; postural style; targeted minimum loss learning (TMLE); top-scoring pairs classifier; super-learning### Software:

SuperLearner
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\textit{A. Chambaz} and \textit{C. Denis}, Ann. Appl. Stat. 6, No. 3, 977--993 (2012; Zbl 1454.62193)

### References:

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[4] | Chambaz, A. and Denis, C. (2012). Supplement to “Classification in postural style.” . · Zbl 1454.62193 |

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[7] | Newell, K. M., Slobounov, S. M., Slobounova, E. S. and Molenaar, P. C. M. (1997). Stochastic processes in postural center-of-pressure profiles. Experimental Brain Research 113 158-164. |

[8] | van der Laan, M. J., Polley, E. C. and Hubbard, A. E. (2007). Super learner. Stat. Appl. Genet. Mol. Biol. 6 Art. 25, 23 pp. (electronic). · Zbl 1166.62387 |

[9] | van der Laan, M. J. and Rose, S., eds. (2011). Targeted Learning : Causal Inference for Observational and Experimental Data . Springer, New York. · Zbl 1218.62121 |

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This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.