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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

Walther Müller 1888–1949

Pp. 239-239

Thomas George Morton 1835–1903

Pp. 239-239

John B. Murphy 1857–1916

Palabras clave: Medical Association; American Medical; Operative Treatment; Great Majority; American Medical Association.

Pp. 240-240

E.A. Nicoll 1902–1993

Palabras clave: Joint Surgery; Italian Journal; Esophageal Diverticulum; Wedge Fracture; Piano Playing.

Pp. 242-243

Carl Nicoladoni 1847–1902

Palabras clave: Orthopedic Surgery; Social Event; Nerve Ending; Joint Capsule; Spending Time.

Pp. 242-242

Karl Iversen Nissen 1906–1995

Palabras clave: Total Joint Replacement; Notable Television; Intertrochanteric Osteotomy; Chief Surgeon; Honorary Fellowship.

Pp. 244-245

George C. Noulis 1849–1919

Palabras clave: Medical Degree; Lachman Test; Paris School; Toral Thesis; Chief Surgeon.

Pp. 245-246

Frank Roberts Ober 1881–1960

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. 247-249

Hiram Winnett Orr 1877–1956

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. 249-250

Richard L. O’Connor 1933–1980

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. 249-249