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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
2005
Información sobre derechos de publicación
© Springer-Verlag London Limited 2005
Cobertura temática
Tabla de contenidos
Marino Ortolani 1904–1983
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. 250-251
Ambroise Paré 1510–1590
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. 261-262
Clayton Parkhill 1860–1902
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. 262-262
Charles William Peabody 1891–1963
Palabras clave: Orthopedic Surgery; Orthopedic Clinic; Aseptic Necrosis; Henry Ford Hospital; Pavlik Harness.
Pp. 263-264
Arnold Pavlik 1902–1965
Palabras clave: Femoral Head; Orthopedic Surgery; Harvard Medical School; Functional Method; Ample Opportunity.
Pp. 263-263
George Perkins 1892–1979
Palabras clave: Orthopedic Department; Maxillofacial Injury; Verbal Sally; British Orthopedic Association; Army Medical Corps.
Pp. 265-267
Georg C. Perthes 1869–1927
Palabras clave: British Editorial; Editorial Board; Joint Surgery; Surgical Clinic; Atypical Case.
Pp. 267-267
Dallas Burton Phemister 1882–1951
Palabras clave: Cerebral Palsy; Cook County; Aseptic Necrosis; Presbyterian Hospital; Rush Medical College.
Pp. 268-269
Nikolai Ivanovich Pirogoff 1810–1881
Palabras clave: Military Hospital; Medical School Curriculum; Operative Shock; Western View; Intellectual Honesty.
Pp. 269-270