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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

Jacques Leveuf 1886–1948

Palabras clave: Acetabular Fracture; Surgical Clinic; Congenital Dislocation; Acute Coronary Occlusion; Intern Appointment.

Pp. 194-195

Paul Budd Magnuson 1884–1968

Palabras clave: Rheumatic Heart Disease; Rehabilitation Institute; Good Medical Care; Surgical Association; State Medical Society.

Pp. 215-217

Joseph François Malgaigne 1806–1865

Palabras clave: Orthopedic Society; Surgical Association; Sport Medicine Clinic; State Medical Society; British Orthopedic Association.

Pp. 217-219

John L. Marshall 1936–1980

Palabras clave: Anterior Cruciate Ligament; Football Club; American Orthopedic; Thoroughbred Horse; Athletic Department.

Pp. 219-220

Earl D. McBride 1891–1975

Palabras clave: Orthopedic Surgeon; Oklahoma City; Disability Evaluation; Musculoskeletal Problem; Residential Structure.

Pp. 222-222

John Laing McDonald 1895–1967

Palabras clave: Army Medical; Trust Fund; Surgical Consultant; Street Hospital; Orthopedic Problem.

Pp. 223-223

Bryan Leslie McFarland 1900–1963

Palabras clave: Royal College; Trade Licence; British Medical Association; Bereave Family; Orthopedic Consultant.

Pp. 224-225

Archibald Hector McIndoe 1900–1960

Palabras clave: Plastic Surgery; Hospital Medical College; Plastic Surgeon; Coronary Occlusion; Vice President.

Pp. 226-226

Thomas Porter McMurray 1888–1949

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. 229-230

Walter Mercer 1891–1971

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. 230-232