Q-Function-Based Diagnostic and Spatial Dependence in Reparametrized t -Student Linear Model

Miguel A. Uribe-Opazo, Rosangela C. Schemmer, Fernanda De Bastiani, Manuel Galea, Rosangela A. B. Assumpção and Tamara C. Maltauro ()
Additional contact information
Miguel A. Uribe-Opazo: Technological and Exact Sciences Center, Western Paraná State University, Cascavel 85819-110, PR, Brazil
Rosangela C. Schemmer: Biopark Technology Park, Toledo 85919-899, PR, Brazil
Fernanda De Bastiani: Department of de Statistics, Federal University of Pernambuco, Recife 50670-420, PE, Brazil
Manuel Galea: Department of de Statistics, Faculty of Mathematics, Pontifical Catholic University of Chile, Santiago 7820436, Chile
Rosangela A. B. Assumpção: Mathematics Coordination, Federal Technological University of Paraná, Toledo 85902-490, PR, Brazil
Tamara C. Maltauro: Technological and Exact Sciences Center, Western Paraná State University, Cascavel 85819-110, PR, Brazil

Mathematics, 2025, vol. 13, issue 18, 1-19

Abstract: Characterizingthe spatial variability of agricultural data is a fundamental step in precision agriculture, especially in soil management and the creation of differentiated management units for increasing productivity. Modeling the spatial dependence structure using geostatistical methods is of great importance for efficiency, estimating the parameters that define this structure, and performing kriging-based interpolation. This work presents diagnostic techniques for global and local influence and generalized leverage using the displacement of the conditional expectation of the logarithm of the joint-likelihood, called the Q-function. This method is used to identify the presence of influential observations that can interfere with parameter estimations, geostatistics model selection, map construction, and spatial variability. To study spatially correlated data, we used reparameterized t -Student distribution linear spatial modeling. This distribution has been used as an alternative to the normal distribution when data have outliers, and it has the same form of covariance matrix as the normal distribution, which enables a direct comparison between them. The methodology is illustrated using one real data set, and the results showed that the modeling was more robust in the presence of influential observations. The study of these observations is indispensable for decision-making in precision agriculture.

Keywords: geostatistics; precision agriculture; soybean productivity; spatial variability (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/13/18/3035/pdf (application/pdf)
https://www.mdpi.com/2227-7390/13/18/3035/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:13:y:2025:i:18:p:3035-:d:1754209

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().