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Activity Number: 129 - Quantile and Nonparametric Regression Models
Type: Contributed
Date/Time: Monday, July 31, 2017 : 8:30 AM to 10:20 AM
Sponsor: Section on Nonparametric Statistics
Abstract #323689 View Presentation
Title: Quantile Function Modeling with Application to Salinity Tolerance Analysis in Plant Growth Data
Author(s): Gaurav Agarwal* and Ying Sun
Companies: and King Abdullah University of Science and Technology
Keywords: conditional quantiles ; joint estimation ; plant growth ; spatial variability ; stress tolerance ; yields

Quantile regression is a way to estimate the conditional quantiles of a response variable distribution in the linear model that provides a complete view of possible causal relationships between variables. In plant science, the study of salinity tolerance is crucial to understanding plant growth and productivity. Motivated by the barley growth data collected from plants irrigated with fresh and saline water, we develop a quantile regression framework for salinity tolerance analysis. Conditional and marginal quantiles are used to evaluate plants' performance in both yields and salinity tolerance. We then introduce a quantile modeling approach that models the entire quantile function using splines with covariates, such as flowering time, harvest index, and other yield-related traits. The proposed model also accounts for spatial variation in the field. We develop efficient joint estimation procedure using data from both the control and saline conditions, and the regression coefficients in different levels of quantiles are used to interpret the effect of measured traits under both conditions.

Authors who are presenting talks have a * after their name.

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