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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

Walter Rowley Bristow 1882–1947

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. 39-41

Sir Benjamin Collins Brodie 1783–1862

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. 41-46

Gurdon Buck 1807–1877

Palabras clave: York Hospital; Charitable Activity; Straight Position; Medical Gazette; Military Affair.

Pp. 46-47

Sterling Bunnell 1882–1957

Palabras clave: Hand Center; British Orthopedic Association; General Surgical Practice; American Orthopedic Association; Untiring Effort.

Pp. 47-49

Sir Stanford Cade 1895–1973

Palabras clave: British Medical Association; Morbid Anatomy; Westminster Hospital; Scottish Family; British Orthopedic Association.

Pp. 49-50

George William Callender 1830–1879

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. 50-50

Jacques Calvé 1875–1954

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. 51-51

Norman Leslie Capener 1898–1975

Palabras clave: Royal College; Charing Cross Hospital; House Surgeon; British Orthopaedic Association; Appliance Workshop.

Pp. 53-56

Arthur Chance 1889–1980

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. 56-56

Fremont A. Chandler 1893–1954

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. 57-60