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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
Jacques-Malthieu Delpech 1777–1832
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. 86-87
Étienne Destot 1864–1918
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. 88-88
Herbert Alton Durham 1884–1946
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. 92-92
Joseph Gichard Duverney 1648–1730
Palabras clave: Orthopedic Surgeon; Biceps Femoris; French Court; Army Medical Corps; Lecture Tour.
Pp. 92-93
Allan Frederick Dwyer 1920–1975
Palabras clave: General Prac; Formidable Problem; Spinal Correction; Outstanding Ability; Army Medical Corps.
Pp. 93-93
Palmer O. Eicher 1904–1988
Palabras clave: Heart Attack; Mitral Valve Replacement; Intertrochanteric Fracture; Femoral Prosthesis; Bilateral Cataract.
Pp. 94-94
R.C. Elmslie 1878–1940
Palabras clave: Rotator Cuff; Club Foot; Orthopedic Hospital; British Medical Association; Valuable Book.
Pp. 96-97
Otto Anderson Engh 1904–1988
Palabras clave: Club Foot; Brachial Plexus Injury; Orthopedic Society; Spinal Cord Disease; Peripheral Nerve Disease.
Pp. 97-98
Wilhelm Heinrich Erb 1840–1921
Palabras clave: Brachial Plexus; National Hospital; Brachial Plexus Injury; Surgical Specialist; Resistance Movement.
Pp. 98-99
Sigmund Epstein 1880–1970
Palabras clave: Peripheral Nerve; York City; Orthopedic Surgeon; Present Form; Brachial Plexus.
Pp. 98-98