Abstract
The t-Student distribution has been used to the spatial dependence modelling of soybean yield as an alternative to the normal distribution, being used for data with heavier tails or discrepant values. However, a usual Student t-distribution does not allow direct comparisons of geostatistical methods with a normal distribution. The aim of this study was to assess the soybean yield spatial variability through a reparameterized t-Student linear model, comparing the results with those of a Gaussian linear model. For parameter estimation, a complete maximum likelihood (CML) method was used through an expectation-maximization (EM) algorithm. The maps constructed with both reparameterized t-Student and normal distributions are dissimilar and present a kappa index (K) equivalent to 0.64. The reparameterized t-Student distribution is an alternative in studying data with discrepant values, showing the ability to decrease the influence of these points.
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Claims 2
Claim · model-written summary
The two model distributions produced dissimilar soybean yield maps with kappa 0.64.
- Taxon
- SOYBEAN
Glycine maxt0 - Outcome
- mapse0
- Direction
- dissimilare0
- Value
- 0.64e0
- Comparator
- normal distributionse0
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The maps constructed with both reparameterized t-Student and normal distributions are dissimilar and present a kappa index (K) equivalent to 0.64”
t0“SOYBEAN”
Claim · model-written summary
The reparameterized t-Student distribution decreased the influence of discrepant values in soybean yield data.
- Taxon
- SOYBEANGlycine maxt0
- Outcome
- influence of these pointse0
- Direction
- decreasee0
- Qualifier
- data with discrepant valuese0
Not reportedComparatorValue
Organisms pictured · Representative pictures of the matched species, not figures from the paper.
Verbatim
e0“The reparameterized t-Student distribution is an alternative in studying data with discrepant values, showing the ability to decrease the influence of these points.”
t0“SOYBEAN”