Catálogo de publicaciones - libros
SCALING AND UNCERTAINTY ANALYSIS IN ECOLOGY
JIANGUO WU ; K. BRUCE JONES ; HARBIN LI ; ORIE L. LOUCKS (eds.)
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
Ecology; Ecosystems; Landscape Ecology; Environmental Management; Nature Conservation
Disponibilidad
| Institución detectada | Año de publicación | Navegá | Descargá | Solicitá |
|---|---|---|---|---|
| No detectada | 2006 | SpringerLink |
Información
Tipo de recurso:
libros
ISBN impreso
978-1-4020-4662-9
ISBN electrónico
978-1-4020-4663-6
Editor responsable
Springer Nature
País de edición
Reino Unido
Fecha de publicación
2006
Información sobre derechos de publicación
© Springer Science+Business Media B.V. 2006
Cobertura temática
Tabla de contenidos
CONCEPTS OF SCALE AND SCALING
JIANGUO WU; HARBIN LI
The best way to understand a linear mixed model, or mixed linear model in some earlier literature, is to first recall a linear regression model. The latter can be expressed as = + , where is a vector of observations, is a matrix of known covariates, is a vector of unknown regression coefficients, and is a vector of (unobservable random) errors. In this model, the regression coefficients are considered fixed. However, there are cases in which it makes sense to assume that some of these coefficients are random. These cases typically occur when the observations are correlated. For example, in medical studies observations are often collected from the same individuals over time. It may be reasonable to assume that correlations exist among the observations from the same individual, especially if the times at which the observations are collected are relatively close. In animal breeding, lactation yields of dairy cows associated with the same sire may be correlated. In educational research, test scores of the same student may be related.
1 - PART I - CONCEPTS AND METHODS | Pp. 3-15
PERSPECTIVES AND METHODS OF SCALING
JIANGUO WU; HARBIN LI
The best way to understand a linear mixed model, or mixed linear model in some earlier literature, is to first recall a linear regression model. The latter can be expressed as = + , where is a vector of observations, is a matrix of known covariates, is a vector of unknown regression coefficients, and is a vector of (unobservable random) errors. In this model, the regression coefficients are considered fixed. However, there are cases in which it makes sense to assume that some of these coefficients are random. These cases typically occur when the observations are correlated. For example, in medical studies observations are often collected from the same individuals over time. It may be reasonable to assume that correlations exist among the observations from the same individual, especially if the times at which the observations are collected are relatively close. In animal breeding, lactation yields of dairy cows associated with the same sire may be correlated. In educational research, test scores of the same student may be related.
1 - PART I - CONCEPTS AND METHODS | Pp. 17-44
UNCERTAINTY ANALYSIS IN ECOLOGICAL STUDIES:AN OVERVIEW
JIANGUO WU; HARBIN LI
The best way to understand a linear mixed model, or mixed linear model in some earlier literature, is to first recall a linear regression model. The latter can be expressed as = + , where is a vector of observations, is a matrix of known covariates, is a vector of unknown regression coefficients, and is a vector of (unobservable random) errors. In this model, the regression coefficients are considered fixed. However, there are cases in which it makes sense to assume that some of these coefficients are random. These cases typically occur when the observations are correlated. For example, in medical studies observations are often collected from the same individuals over time. It may be reasonable to assume that correlations exist among the observations from the same individual, especially if the times at which the observations are collected are relatively close. In animal breeding, lactation yields of dairy cows associated with the same sire may be correlated. In educational research, test scores of the same student may be related.
1 - PART I - CONCEPTS AND METHODS | Pp. 45-66
MULTILEVEL STATISTICAL MODELS AND ECOLOGICAL SCALING
Richard A. Berk; Jan de Leeuw
The best way to understand a linear mixed model, or mixed linear model in some earlier literature, is to first recall a linear regression model. The latter can be expressed as = + , where is a vector of observations, is a matrix of known covariates, is a vector of unknown regression coefficients, and is a vector of (unobservable random) errors. In this model, the regression coefficients are considered fixed. However, there are cases in which it makes sense to assume that some of these coefficients are random. These cases typically occur when the observations are correlated. For example, in medical studies observations are often collected from the same individuals over time. It may be reasonable to assume that correlations exist among the observations from the same individual, especially if the times at which the observations are collected are relatively close. In animal breeding, lactation yields of dairy cows associated with the same sire may be correlated. In educational research, test scores of the same student may be related.
1 - PART I - CONCEPTS AND METHODS | Pp. 67-88
DOWNSCALING ABUNDANCE FROM THE DISTRIBUTION OF SPECIES: OCCUPANCY THEORY AND APPLICATIONS
Fangliang He; William Reed
The best way to understand a linear mixed model, or mixed linear model in some earlier literature, is to first recall a linear regression model. The latter can be expressed as = + , where is a vector of observations, is a matrix of known covariates, is a vector of unknown regression coefficients, and is a vector of (unobservable random) errors. In this model, the regression coefficients are considered fixed. However, there are cases in which it makes sense to assume that some of these coefficients are random. These cases typically occur when the observations are correlated. For example, in medical studies observations are often collected from the same individuals over time. It may be reasonable to assume that correlations exist among the observations from the same individual, especially if the times at which the observations are collected are relatively close. In animal breeding, lactation yields of dairy cows associated with the same sire may be correlated. In educational research, test scores of the same student may be related.
1 - PART I - CONCEPTS AND METHODS | Pp. 89-108
SCALING TERRESTRIAL BIOGEOCHEMICAL PROCESSES CONTRASTING INTACT AND MODEL EXPERIMENTAL SYSTEMS
Mark A. Bradford; James F. Reynolds
The best way to understand a linear mixed model, or mixed linear model in some earlier literature, is to first recall a linear regression model. The latter can be expressed as = + , where is a vector of observations, is a matrix of known covariates, is a vector of unknown regression coefficients, and is a vector of (unobservable random) errors. In this model, the regression coefficients are considered fixed. However, there are cases in which it makes sense to assume that some of these coefficients are random. These cases typically occur when the observations are correlated. For example, in medical studies observations are often collected from the same individuals over time. It may be reasonable to assume that correlations exist among the observations from the same individual, especially if the times at which the observations are collected are relatively close. In animal breeding, lactation yields of dairy cows associated with the same sire may be correlated. In educational research, test scores of the same student may be related.
1 - PART I - CONCEPTS AND METHODS | Pp. 109-130
A FRAMEWORK AND METHODS FOR SIMPLIFYING COMPLEX LANDSCAPES TO REDUCE UNCERTAINTY IN PREDICTIONS
Debra P. C. Peters; Jin Yao; Laura F. Huenneke; Robert P. Gibbens; Kris M. Havstad; Jeffrey E. Herrick; Albert Rango; William H. Schlesinger
The best way to understand a linear mixed model, or mixed linear model in some earlier literature, is to first recall a linear regression model. The latter can be expressed as = + , where is a vector of observations, is a matrix of known covariates, is a vector of unknown regression coefficients, and is a vector of (unobservable random) errors. In this model, the regression coefficients are considered fixed. However, there are cases in which it makes sense to assume that some of these coefficients are random. These cases typically occur when the observations are correlated. For example, in medical studies observations are often collected from the same individuals over time. It may be reasonable to assume that correlations exist among the observations from the same individual, especially if the times at which the observations are collected are relatively close. In animal breeding, lactation yields of dairy cows associated with the same sire may be correlated. In educational research, test scores of the same student may be related.
1 - PART I - CONCEPTS AND METHODS | Pp. 131-146
BUILDING UP WITH A TOP-DOWN APPROACH: THE ROLE OF REMOTE SENSING IN DECIPHERING FUNCTIONAL AND STRUCTURAL DIVERSITY
Carol A. Wessman; C. Ann Bateson
The best way to understand a linear mixed model, or mixed linear model in some earlier literature, is to first recall a linear regression model. The latter can be expressed as = + , where is a vector of observations, is a matrix of known covariates, is a vector of unknown regression coefficients, and is a vector of (unobservable random) errors. In this model, the regression coefficients are considered fixed. However, there are cases in which it makes sense to assume that some of these coefficients are random. These cases typically occur when the observations are correlated. For example, in medical studies observations are often collected from the same individuals over time. It may be reasonable to assume that correlations exist among the observations from the same individual, especially if the times at which the observations are collected are relatively close. In animal breeding, lactation yields of dairy cows associated with the same sire may be correlated. In educational research, test scores of the same student may be related.
1 - PART I - CONCEPTS AND METHODS | Pp. 147-163
CARBON FLUXES ACROSS REGIONS: OBSERVATIONAL CONSTRAINTS AT MULTIPLE SCALES
Beverly E. Law; Dave Turner; John Campbell; Michael Lefsky; Michael Guzy; Osbert Sun; Steve Van Tuyl; Warren Cohen
The best way to understand a linear mixed model, or mixed linear model in some earlier literature, is to first recall a linear regression model. The latter can be expressed as = + , where is a vector of observations, is a matrix of known covariates, is a vector of unknown regression coefficients, and is a vector of (unobservable random) errors. In this model, the regression coefficients are considered fixed. However, there are cases in which it makes sense to assume that some of these coefficients are random. These cases typically occur when the observations are correlated. For example, in medical studies observations are often collected from the same individuals over time. It may be reasonable to assume that correlations exist among the observations from the same individual, especially if the times at which the observations are collected are relatively close. In animal breeding, lactation yields of dairy cows associated with the same sire may be correlated. In educational research, test scores of the same student may be related.
2 - PART II - CASE STUDIES | Pp. 167-190
LANDSCAPE AND REGIONAL SCALE STUDIES OF NITROGEN GAS FLUXES
Peter M. Groffman; Rodney T. Venterea; Louis V. Verchot; Christopher S. Potter
The best way to understand a linear mixed model, or mixed linear model in some earlier literature, is to first recall a linear regression model. The latter can be expressed as = + , where is a vector of observations, is a matrix of known covariates, is a vector of unknown regression coefficients, and is a vector of (unobservable random) errors. In this model, the regression coefficients are considered fixed. However, there are cases in which it makes sense to assume that some of these coefficients are random. These cases typically occur when the observations are correlated. For example, in medical studies observations are often collected from the same individuals over time. It may be reasonable to assume that correlations exist among the observations from the same individual, especially if the times at which the observations are collected are relatively close. In animal breeding, lactation yields of dairy cows associated with the same sire may be correlated. In educational research, test scores of the same student may be related.
2 - PART II - CASE STUDIES | Pp. 191-203