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1. |
Record Nr. |
UNINA9910137096203321 |
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Autore |
Nadia Dominici |
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Titolo |
Neuro-motor control and feed-forward models of locomotion in humans / / edited by: Marco Iosa, Nadia Dominici, Federica Tamburella and Leonardo Gizzi |
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Pubbl/distr/stampa |
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Frontiers Media SA, 2015 |
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[Lausanne, Switzerland] : , : Frontiers Media SA, , 2015 |
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©2015 |
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Descrizione fisica |
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1 online resource (190 pages) : illustrations; digital file(s) |
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Collana |
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Frontiers Research Topics |
Frontiers in Human Neuroscience |
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Soggetti |
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Neurophysiology |
Human locomotion - Physiological aspects |
Motor neurons |
Spinal Cord Injuries - rehabilitation |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Sommario/riassunto |
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Locomotion involves many different muscles and the need of controlling several degrees of freedom. Despite the Central Nervous System can finely control the contraction of individual muscles, emerging evidences indicate that strategies for the reduction of the complexity of movement and for compensating the sensori-motor delays may be adopted. The scope of this Topic includes, but is not limited to, studies aimed at understanding the role played in control of locomotion of different neural circuits located at brain, cerebellum, and/or spinal cord levels, and related internal models. |
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2. |
Record Nr. |
UNINA9910993944503321 |
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Autore |
Maruyama Yuzo |
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Titolo |
Stein Estimation / / by Yuzo Maruyama, Tatsuya Kubokawa, William E. Strawderman |
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Pubbl/distr/stampa |
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Singapore : , : Springer Nature Singapore : , : Imprint : Springer, , 2023 |
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ISBN |
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Edizione |
[1st ed. 2023.] |
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Descrizione fisica |
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1 online resource (0 pages) |
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Collana |
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JSS Research Series in Statistics, , 2364-0065 |
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Disciplina |
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Soggetti |
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Statistics |
Applied Statistics |
Statistical Theory and Methods |
Bayesian Inference |
Bayesian Network |
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Lingua di pubblicazione |
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Formato |
Materiale a stampa |
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Livello bibliografico |
Monografia |
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Nota di bibliografia |
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Includes bibliographical references. |
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Nota di contenuto |
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1. Decision Theory Preliminaries -- 2. Minimaxity and Improvement on the James-Stein estimator -- 3. Admissibility. |
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Sommario/riassunto |
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This book provides a self-contained introduction of Stein/shrinkage estimation for the mean vector of a multivariate normal distribution. The book begins with a brief discussion of basic notions and results from decision theory such as admissibility, minimaxity, and (generalized) Bayes estimation. It also presents Stein's unbiased risk estimator and the James-Stein estimator in the first chapter. In the following chapters, the authors consider estimation of the mean vector of a multivariate normal distribution in the known and unknown scale case when the covariance matrix is a multiple of the identity matrix and the loss is scaled squared error. The focus is on admissibility, inadmissibility, and minimaxity of (generalized) Bayes estimators, where particular attention is paid to the class of (generalized) Bayes estimators with respect to an extended Strawderman-type prior. For almost all results of this book, the authors present a self-contained proof. The book is helpful for researchers and graduate students in various fields requiring data analysis skills as well as in mathematical |
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