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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

Henry L. Jaffe 1896–1979

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. 161-163

Arthur Rocyn Jones 1883–1972

Palabras clave: Orthopedic Surgery; Club Foot; Spinal Frame; Wounded Soldier; Good Stead.

Pp. 163-164

Robert Jones 1857–1933

Palabras clave: Club Foot; Cleft Palate; Young Surgeon; Orthopedic Center; British Orthopedic Association.

Pp. 164-167

Robert Judet 1909–1980

Palabras clave: Orthopedic Surgeon; Club Foot; Mandatory Retirement; Warm Glow; Zoological Garden.

Pp. 167-168

Emanuel B. Kaplan 1894–1980

Palabras clave: Inguinal Hernia Repair; Shoulder Dislocation; Mandatory Retirement; Zoological Garden; Great Famine.

Pp. 168-169

Robert Kienböck 1871–1953

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. 176-176

Constantine Lambrinudi 1890–1943

Palabras clave: Orthopedic Surgery; Manual Train; Congenital Dislocation; Physical Weakness; Chief Surgeon.

Pp. 182-183

Arthur Thornton Legg 1874–1939

Palabras clave: State Department; Operative Procedure; Harvard College; Medical Association; Assistant Professor.

Pp. 189-189

Irwin S. Leinbach 1907–1994

Palabras clave: Orthopedic Surgeon; Acetabular Fracture; Lifetime Achievement Award; Acetabular Surgery; Reading Hospital.

Pp. 190-191

Emile Letournel 1927–1994

Palabras clave: Acetabular Fracture; French Institute; Ular Fracture; Lifetime Achievement Award; Orthopedic Trauma Surgery.

Pp. 191-194