Catálogo de publicaciones - libros
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
Lewis Atterbury Stimson 1844–1917
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. 323-323
Hugh Owen Thomas 1834–1891
Palabras clave: Orthopedic Surgery; Slow Traction; Texas Medical School; Paralyzed Muscle; Ancestral Background.
Pp. 330-332
Frederick Roeck Thompson 1907–1983
Palabras clave: Orthopedic Surgery; Roosevelt Hospital; Texas Medical School; Medical Manuscript; Nerve Suture.
Pp. 332-333
Friedrich Trendelenburg 1844–1924
Palabras clave: Orthopedic Condition; Nerve Suture; Itary Surgeon; Generous Friend; Royal National Orthopedic Hospital.
Pp. 333-334
Jules Tinel 1879–1952
Palabras clave: Nerve Injury; Future Career; Medical Degree; Escape Route; Excellent Book.
Pp. 333-333
W.H. Trethowan 1882–1934
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. 334-335
Joseph Trueta 1897–1977
Palabras clave: Renal Circulation; Chief Surgeon; Honorary Fellowship; Royal Veterinary College; Busy Clinical Practice.
Pp. 335-337
Alfred Herbert Tubby 1862–1930
Palabras clave: Orthopedic Surgery; Wild Boar; Tendon Repair; Gold Medal; British Orthopedic Association.
Pp. 338-341
Kauko Vainio 1913–1989
Palabras clave: Orthopedic Surgeon; Spinal Stenosis; Intermittent Claudication; Clinical Acumen; Postgraduate Train.
Pp. 341-342
Richard Von Volkmann 1830–1889
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. 344-344