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Who's Who in Orthopedics
Seyed Behrooz Mostofi
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
Orthopedics; History of Medicine
Disponibilidad
Institución detectada | Año de publicación | Navegá | Descargá | Solicitá |
---|---|---|---|---|
No detectada | 2005 | SpringerLink |
Información
Tipo de recurso:
libros
ISBN impreso
978-1-85233-786-5
ISBN electrónico
978-1-84628-070-2
Editor responsable
Springer Nature
País de edición
Reino Unido
Fecha de publicación
2005
Información sobre derechos de publicación
© Springer-Verlag London Limited 2005
Cobertura temática
Tabla de contenidos
Börje Walldius 1913—
Seyed Behrooz Mostofi
Cluster analysis is an exploratory technique. Functional data methods offer the advantage of allowing a greater variety of clustering matrixes to choose from. The examples involving the clustering of Canadian weather stations are meant to be illustrative, since the known locations of weather stations can be used to infer which ones should exhibit similar weather patterns. The objective is not so much to find “real” clusters of stations, but rather to learn how the weather patterns at the different stations are related. Some of the clusters obtained consist of stations that are located in the same region, which we would expect similar to have weather patterns. Other aspects of the clustering are harder to interpret (e.g., assignment of Prince Rupert and Halifax to the same cluster), although they may also indicate relationships in weather patterns for stations at some distance from each other. A cluster analysis that accounted for both precipitation and temperature (and other weather related variables such as humidity) might be preferable, provided a suitable clustering metric could be found.
Methods for determining the number of clusters in functional cluster analysis are identical to those in the classical case, and thus are not discussed further here.
If groupings for some of the data are known in advance, it may be preferable to use a discriminant function analysis to find the variables and matrix that best classify the remaining observations. In the chapter on functional generalized linear models, we use a form of discriminant function analysis, functional logistic models, to classify the weather stations.
Pp. 345-345
Henning Waldenström 1877–1972
Seyed Behrooz Mostofi
Cluster analysis is an exploratory technique. Functional data methods offer the advantage of allowing a greater variety of clustering matrixes to choose from. The examples involving the clustering of Canadian weather stations are meant to be illustrative, since the known locations of weather stations can be used to infer which ones should exhibit similar weather patterns. The objective is not so much to find “real” clusters of stations, but rather to learn how the weather patterns at the different stations are related. Some of the clusters obtained consist of stations that are located in the same region, which we would expect similar to have weather patterns. Other aspects of the clustering are harder to interpret (e.g., assignment of Prince Rupert and Halifax to the same cluster), although they may also indicate relationships in weather patterns for stations at some distance from each other. A cluster analysis that accounted for both precipitation and temperature (and other weather related variables such as humidity) might be preferable, provided a suitable clustering metric could be found.
Methods for determining the number of clusters in functional cluster analysis are identical to those in the classical case, and thus are not discussed further here.
If groupings for some of the data are known in advance, it may be preferable to use a discriminant function analysis to find the variables and matrix that best classify the remaining observations. In the chapter on functional generalized linear models, we use a form of discriminant function analysis, functional logistic models, to classify the weather stations.
Pp. 345-345
Frederick Oldfield Ward 1818–1877
Seyed Behrooz Mostofi
Cluster analysis is an exploratory technique. Functional data methods offer the advantage of allowing a greater variety of clustering matrixes to choose from. The examples involving the clustering of Canadian weather stations are meant to be illustrative, since the known locations of weather stations can be used to infer which ones should exhibit similar weather patterns. The objective is not so much to find “real” clusters of stations, but rather to learn how the weather patterns at the different stations are related. Some of the clusters obtained consist of stations that are located in the same region, which we would expect similar to have weather patterns. Other aspects of the clustering are harder to interpret (e.g., assignment of Prince Rupert and Halifax to the same cluster), although they may also indicate relationships in weather patterns for stations at some distance from each other. A cluster analysis that accounted for both precipitation and temperature (and other weather related variables such as humidity) might be preferable, provided a suitable clustering metric could be found.
Methods for determining the number of clusters in functional cluster analysis are identical to those in the classical case, and thus are not discussed further here.
If groupings for some of the data are known in advance, it may be preferable to use a discriminant function analysis to find the variables and matrix that best classify the remaining observations. In the chapter on functional generalized linear models, we use a form of discriminant function analysis, functional logistic models, to classify the weather stations.
Pp. 346-347
Masaki Watanabe 1911–1995
Seyed Behrooz Mostofi
Cluster analysis is an exploratory technique. Functional data methods offer the advantage of allowing a greater variety of clustering matrixes to choose from. The examples involving the clustering of Canadian weather stations are meant to be illustrative, since the known locations of weather stations can be used to infer which ones should exhibit similar weather patterns. The objective is not so much to find “real” clusters of stations, but rather to learn how the weather patterns at the different stations are related. Some of the clusters obtained consist of stations that are located in the same region, which we would expect similar to have weather patterns. Other aspects of the clustering are harder to interpret (e.g., assignment of Prince Rupert and Halifax to the same cluster), although they may also indicate relationships in weather patterns for stations at some distance from each other. A cluster analysis that accounted for both precipitation and temperature (and other weather related variables such as humidity) might be preferable, provided a suitable clustering metric could be found.
Methods for determining the number of clusters in functional cluster analysis are identical to those in the classical case, and thus are not discussed further here.
If groupings for some of the data are known in advance, it may be preferable to use a discriminant function analysis to find the variables and matrix that best classify the remaining observations. In the chapter on functional generalized linear models, we use a form of discriminant function analysis, functional logistic models, to classify the weather stations.
Pp. 347-347
Royal Whitman 1857–1946
Palabras clave: Orthopedic Surgery; Club Foot; Orthopedic Literature; Manipulative Treatment; Somatic Patient.
Pp. 358-360
Hans Robert Willenegger 1910–1998
Palabras clave: Massachusetts General Hospital; Plate Osteosynthesis; Malleolar Fracture; Osseous Healing; Somatic Patient.
Pp. 360-361
Philip Duncan Wilson 1886–1969
Palabras clave: Orthopedic Surgeon; Orthopedic Surgery; Massachusetts General Hospital; Malleolar Fracture; High Surgical Training.
Pp. 361-364
Julius Wolff 1836–1902
Palabras clave: Orthopedic Surgery; Club Foot; East 90th Street; Nasal Deformity; General Medical Practice.
Pp. 364-365
Iwao Yasuda 1909—
Palabras clave: Orthopedic Surgery; Club Foot; Royal Academy; Trabecular Structure; Orthopedic Clinic.
Pp. 365-365