























This paper has been withdrawn by Marco Mandap PhD
No PDF available, click to view other formats
Abstract:Comparing yield quality distributions across multiple agricultural fields is fundamental for evaluating management practices, yet it is complicated by two pervasive data characteristics: non-normality and spatial autocorrelation. Traditional parametric tests, such as ANOVA, frequently suffer from severe Type I error inflation when the independence assumption is violated by spatial dependence. This paper introduces a novel rank-based test framework that utilizes spatial kernel smoothing to construct robust empirical distribution functions (EDFs). We establish the asymptotic properties of the test statistic under $\alpha$-mixing conditions, proving its convergence to a weighted sum of chi-squared random variables. To facilitate practical inference, we employ a Satterthwaite approximation to derive effective degrees of freedom that account for the spatial 'inflation' of variance. The theoretical framework is developed in detail, providing a rigorous foundation for the proposed method. Simulation studies and applications to real yield quality data are left to future work.
From: Marco Mandap PhD [view email]
[v1]
Sun, 29 Dec 2024 01:40:12 UTC (7 KB)
[v2]
Sun, 1 Mar 2026 03:35:56 UTC (19 KB)
[v3]
Sun, 14 Jun 2026 04:16:32 UTC (1 KB) (withdrawn)
此内容由惯性聚合(RSS阅读器)自动聚合整理,仅供阅读参考。 原文来自 — 版权归原作者所有。