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Estimation of Dose–Response Models for Discrete and Continuous Data in Weed Science

Published online by Cambridge University Press:  20 January 2017

William J. Price ,
Bahman Shafii  and
Steven S. Seefeldt
William J. Price
Affiliation:
Statistical Programs, College of Agricultural and Life Sciences, University of Idaho, Moscow, ID 83844
Bahman Shafii*
Affiliation:
Statistical Programs, College of Agricultural and Life Sciences, University of Idaho, Moscow, ID 83844
Steven S. Seefeldt
Affiliation:
USDA-ARS, Subarctic Agricultural Research Unit, University of Alaska Fairbanks, Fairbanks, AK 99775
*
Corresponding author's E-mail: bshafii@uidaho.edu
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Abstract

Dose–response analysis is widely used in biological sciences and has application to a variety of risk assessment, bioassay, and calibration problems. In weed science, dose–response methodologies have typically relied on least squares estimation under the assumptions of normal, homoscedastic, and independent errors. Advances in computational abilities and available software, however, have given researchers more flexibility and choices for data analysis when these assumptions are not appropriate. This article will explore these techniques and demonstrate their use to provide researchers with an up-to-date set of tools necessary for analysis of dose–response problems. Demonstrations of the techniques are provided using a variety of data examples from weed science.

El análisis de respuesta a dosis es ampliamente usado en las ciencias biológicas y tiene aplicación a una variedad de problemas de evaluación de riesgo, bioensayos y calibración. En la ciencia de malezas, las metodologías de respuesta a dosis han dependido típicamente de la estimación de los mínimos cuadrados bajo la premisa de que los error son normales, homocedásticos e independientes. Sin embargo, los avances en las habilidades computacionales y la disponibilidad de software han dado a los investigadores más flexibilidad y opciones para analizar datos cuando estas premisas no son apropiadas. Este artículo explora estas técnicas y demuestra su uso con la intención de brindar a los investigadores un grupo actualizado de herramientas necesarias para el análisis de problemas de respuesta a dosis. Se proveen demostraciones de las técnicas usando una variedad de ejemplos de datos para la ciencia de malezas.

Keywords

Alternative model estimationbioassaymaximum likelihoodnonlinear modelstreatment comparison
Type
Special Topics
Information
Weed Technology , Volume 26 , Issue 3 , September 2012 , pp. 587 - 601
Copyright
Copyright © Weed Science Society of America 

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