Difference between revisions of "Data imputation"
(New page: A variety of '''Imputation techniques''' are available. The ''k''NN estimation method is one, or actually several (depending on the type of information being used, the definition of nearne...) |
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| − | A variety of '''Imputation techniques''' are available. The ''k''NN estimation method is one, or actually several (depending on the type of information being used, the definition of nearness, the weighting procedure, etc.). Imputation can be regarded as the opposite of amputation; information in a specific format is added to ensure functionality. A straightforward way is to impute plots from a reference data set to a stand described by totals and average values in a register. The imputation is done when values at the stand-level corresponds to those at the plot-level. Unrealistic stands, with more or less no variation (too homogenous), can be estimated if 1. only one plot per stand is imputed, 2. if more than one plot per stand is imputed but the reference data set is "complete" ( | + | A variety of '''Imputation techniques''' are available. The ''k''NN estimation method is one, or actually several (depending on the type of information being used, the definition of nearness, the weighting procedure, etc.). Imputation can be regarded as the opposite of amputation; information in a specific format is added to ensure functionality. A straightforward way is to impute plots from a reference data set to a stand described by totals and average values in a register. The imputation is done when values at the stand-level corresponds to those at the plot-level. Unrealistic stands, with more or less no variation (too homogenous), can be estimated if 1. only one plot per stand is imputed, 2. if more than one plot per stand is imputed but the reference data set is "complete" (all possible plots are represented) and a specific plot can be imputed to the same stand more than once, or 3. if no measures are taken into account, to secure a certain variation. |
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| + | This is done in the first dialog box[?, not really sure what's done here...]. | ||
Revision as of 17:43, 8 September 2009
A variety of Imputation techniques are available. The kNN estimation method is one, or actually several (depending on the type of information being used, the definition of nearness, the weighting procedure, etc.). Imputation can be regarded as the opposite of amputation; information in a specific format is added to ensure functionality. A straightforward way is to impute plots from a reference data set to a stand described by totals and average values in a register. The imputation is done when values at the stand-level corresponds to those at the plot-level. Unrealistic stands, with more or less no variation (too homogenous), can be estimated if 1. only one plot per stand is imputed, 2. if more than one plot per stand is imputed but the reference data set is "complete" (all possible plots are represented) and a specific plot can be imputed to the same stand more than once, or 3. if no measures are taken into account, to secure a certain variation.
This is done in the first dialog box[?, not really sure what's done here...].