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Full-Text Articles in Physical Sciences and Mathematics
Spatial Clustering Using The Likelihood Function, April Kerby, David Marx, Ashok Samal, Viacheslav Adamchuk
Spatial Clustering Using The Likelihood Function, April Kerby, David Marx, Ashok Samal, Viacheslav Adamchuk
Conference on Applied Statistics in Agriculture
Clustering has been widely used as a tool to group multivariate observations that have similar characteristics. However, most attempts at formulating a method to group similar multivariate observations while taking into account their spatial location are relatively ad hoc and do not account for the underlying spatial structure of the variables measured [12, 13, 14]. This paper proposes a method to spatially cluster similar observations based on the likelihood function. The geographic or spatial location of the observations can be incorporated into the likelihood of the multivariate normal distribution through the variance-covariance matrix. The variance-covariance matrix can be computed using …
Modelling Within-Plant Spatial Dependencies Of Cotton Yield, E. B. Moser, R. E. Macchiavelli, D. J. Boquet
Modelling Within-Plant Spatial Dependencies Of Cotton Yield, E. B. Moser, R. E. Macchiavelli, D. J. Boquet
Conference on Applied Statistics in Agriculture
In field experiments during 1987-1990, cotton plants were grown under 8 different levels of nitrogen application to assess the impact of nitrogen fertilization on the fruiting and yield distribution of cotton within the plant (Boquet et al. 1993).lr.dividual boll weights and average seedcotton yield were determined at each fruiting site fur each main-stem node along the plant. Various models of dependence and independence are possible to explain and account for the dependencies of the yields among the sites and nodes of the plant. Here we investigate models of total yield per node and yield per node adjusted for the number …
Designed Experiments In The Presence Of Spatial Correlation, David B. Marx
Designed Experiments In The Presence Of Spatial Correlation, David B. Marx
Conference on Applied Statistics in Agriculture
Soil heterogeneity is generally the major cause of variation in plot yield data and the difficulty of its interpretation. If a large degree of variability is present at a test site, some method of controlling it must be found. Controlling experimental variability can be achieved either by good experimental design or by analysis procedures which account for the spatial correlation. Classical designs are only moderately equipped to adjust for spatially correlated data. More complex designs including nearest neighbor designs, Williams designs, and certain restricted Latin square designs are developed for field experimentation when spatial correlation causes classical designs to be …