research area of
- BURGUEÑO FERREIRA, Juan Andres, Senior Biometrician / Head of BSU, CIMMYT, CIMMYT
- PEREZ RODRIGUEZ, Paulino, Consultant, CIMMYT, CIMMYT
linear model
Concept
- Overview
Overview
subject area of
- A parametric measure of lactation persistency in dairy cattle
- Analysis and Interpretation of Interactions in Agricultural Research
- Analysis of correlated discrete observations: background, examples and solutions.
- Bayesian Genomic-Enabled Prediction as an Inverse Problem
- Bayesian regularized quantile regression: A robust alternative for genome-based prediction of skewed data
- Calibration and validation of rainfall erosivity estimators for application in Rwanda
- Canal operations planner. I: maximizing delivery performance ratio
- Canopy cover estimation in miombo woodlands of Zambia: Comparison of Landsat 8 OLI versus RapidEye imagery using parametric, nonparametric, and semiparametric methods
- Combining field surveys, remote sensing, and regression trees to understand yield variations in an irrigated wheat landscape
- Comparison Between Linear and Non-parametric Regression Models for Genome-Enabled Prediction in Wheat
- Incorporating environmental and geographical information in forest data analysis: a new fitting approach for universal kriging
- META-R: A software to analyze data from multi-environment plant breeding trials
- Mean species cover: a harmonized indicator of shrub cover for forest inventories
- Modelling long-term fire occurrence factors in Spain by accounting for local variations with geographically weighted regression
- Potential of catchment-wide soil water content prediction using electromagnetic induction in a forest ecosystem
- Predicting soil permanganate oxidizable carbon (POXC) by coupling DRIFT spectroscopy and artificial neural networks (ANN)
- Prediction assessment of linear mixed models for multienvironment trials
- Solving nonlinear water management models using a combined genetic algorithm and linear programming approach
- Studying crossover genotype × environment interaction using linear-bilinear models and mixed models
- Using linear-bilinear models for studying gene expression × treatment interaction in microarray experiments