Catálogo de publicaciones - libros
The Taste of Ethics
Christian Coff
Resumen/Descripción – provisto por la editorial
No disponible.
Palabras clave – provistas por la editorial
Ethics; Philosophy of Biology
Disponibilidad
Institución detectada | Año de publicación | Navegá | Descargá | Solicitá |
---|---|---|---|---|
No detectada | 2006 | SpringerLink |
Información
Tipo de recurso:
libros
ISBN impreso
978-1-4020-4553-0
ISBN electrónico
978-1-4020-4554-7
Editor responsable
Springer Nature
País de edición
Reino Unido
Fecha de publicación
2006
Información sobre derechos de publicación
© Springer 2006
Cobertura temática
Tabla de contenidos
Eating, Society and Ethics
Christian Coff
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
1 - Part I -Food and Ethics | Pp. 3-30
Food to Science: On the Intellectualization of Food
Christian Coff
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
2 - Part II -The Intellectualization of Food | Pp. 33-60
The Storylessness of Food
Christian Coff
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
2 - Part II -The Intellectualization of Food | Pp. 61-92
Tracing the Production History
Christian Coff
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
3 - Part III - Food Ethics and the Production History | Pp. 95-137
Food Ethics as the Ethics of the Trace
Christian Coff
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
3 - Part III - Food Ethics and the Production History | Pp. 139-165
Traceability and Food Ethics
Christian Coff
All of the standard inferences in RSM as presented in previous chapters are based on point estimators which have sampling, or experimental, variability. Assuming a classical or frequentist point of view, every quantity computed based on experimental data is subject to sampling variability and is therefore a random quantity itself. As Draper [48] pointed out, one should not expect precise conclusions when using mathematical optimization techniques based on data subject to large errors. This comment applies to every technique previously discussed, namely, the steepest ascent/descent direction, eigenvalues of the quadratic matrix and point estimators of the stationary or optimal points in quadratic (second order) optimization for both canonical and ridge analysis. It also applies to more sophisticated mathematical programming techniques. In the RSM literature, there has been an over-emphasis on using different types of such mathematical techniques which neglect the main statistical issue that arises from random data: if the experiment is repeated and new models fitted, the parameters (or even the response model form) may change, and this will necessarily result in a different optimal solution.
3 - Part III - Food Ethics and the Production History | Pp. 167-202