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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

Información sobre derechos de publicación

© Springer-Verlag London Limited 2005

Tabla de contenidos

Donald Barclay Slocum 1911–1983

Palabras clave: Orthopedic Surgery; American Orthopedic; Orthopedic Society; Sacred Heart Hospital; Lieutenant Colonel.

Pp. 307-308

Ian Scott Smillie 1907–1992

Palabras clave: Gold Medal; Expert Surgeon; Sahara Desert; Orthopedic Service; Severe Physical Disability.

Pp. 308-309

Robert William Smith 1807–1873

Palabras clave: Orthopedic Surgery; Distal Radius; Femoral Neck Fracture; Harvard Medical School; Joint Date.

Pp. 309-310

M.N. Smith-Petersen 1886–1953

Palabras clave: Orthopedic Surgery; Femoral Neck Fracture; Harvard Medical School; London Hospital; Honorary Fellowship.

Pp. 310-311

Harold Augustus Sofield 1900–1987

Palabras clave: Osteogenesis Imperfecta; Orthopedic Resi; Amherst College; State Medical Society; Shriner Hospital.

Pp. 312-313

Edgar William Somerville 1913–1996

Palabras clave: Orthopedic Research; Coxa Vara; Derotation Osteotomy; Orthopedic Service; State Medical Society.

Pp. 313-314

James Spencer Speed 1890–1970

Palabras clave: Orthopedic Surgery; Coxa Vara; Derotation Osteotomy; Orthopedic Service; County Medical Society.

Pp. 314-316

Richard N. Stauffer 1938–1998

Palabras clave: Orthopedic Surgery; Total Joint Replacement; Johns Hopkins Hospital; Allegheny General Hospital; Mayo Medical School.

Pp. 316-317

George Frederic Still 1868–1941

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. 322-323

Frank Stinchfield 1910–1992

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. 323-325