Physical Sciences and Mathematics Commons^™

Model Averaging In Agriculture And Natural Resources: What Is It? When Is It Useful? When Is It A Distraction?, Philip M. Dixon

Conference on Applied Statistics in Agriculture and Natural Resources

I use two examples to illustrate three methods for model averaging: using AIC weights, using BIC weights, and fully Bayesian analyses. The first example is a capture-recapture study that estimates the population size by averaging over 4 models for capture probabilities. The second is an analysis of a study of logging impacts on Curculionid weevils using a before-after-control-impact (BACI) study design. The estimated impact is averaged over 4 ecologically relevant models.

Both examples demonstrate the sensitivity of model weights, or posterior model probabilities, to the choice of prior model probabilities and prior distributions for parameters. The model averaged estimates and …

A Robust Clustering Method Using Compositional Data Restrictions: Studying Wood Properties In The Reforestation Of Portugal, Pamela M. Chiroque-Solano, Guido A. Moreira

Conference on Applied Statistics in Agriculture and Natural Resources

Classification of multivariate observations while preserving the data’s natural restriction is a challenge. Special properties such as identifiability, interpretability, and others need to be cared for to build a new approach. To avoid these complications, many transformation algorithms have been developed to use traditional models.In this context, the aim of this work is to propose a robust probabilistic distance algorithm to classify compositional data. Based on the probabilistic distance (PD) clustering approach, the proposal identifies clusters minimizing a joint distance function, JDF, which is part of a dissimilarity measure. This measure combines the PD clustering approach with the density of …

Random Regression For Modeling Semen Fertility In Hf Purebred And Crossbred Bulls Using A Bayesian Framework, Vrinda Ambike, R. Venkataramanan, S. M. K. Karthickeyan, K. G. Tirumurugaan, Kaustubh Bhave, M. Swaminathan

Conference on Applied Statistics in Agriculture and Natural Resources

Data on insemination records of Holstein Friesian (HF) purebred (n=45,497) and crossbred (n=58,497) collected from the BAIF Research Foundation were utilized. The conception rate was modeled as a binary trait, using linear repeatability models. Random regression models (RRM) were used to obtain the trajectory of variance components across age of the bulls. Legendre Polynomials up to order of fit of 4 were used for the random effects of additive genetic and permanent environmental effects. 200,000 Gibbs samples were generated with a burn-in of 20,000 and thinning interval of 50 using the THRGIBBS1F90 program. Heritability estimates were very low (0.1) in …

Principal Response Curve Analysis Of Arthropod Community Abundance Data With Sparse Subsets, Changjian Jiang, C. R. Brown, P. Asiimwe, Chen Meng, Adam W. Schapaugh

Conference on Applied Statistics in Agriculture and Natural Resources

Principal response curve (PRC) analysis was applied to an assessment of the ecological impact of the genetically-modified (GM), insect-resistant, cotton MON 88702 on predatory Hemiptera communities in the field. The field community was represented by ten taxa collected ten times across the season at six sites, in which individual taxa were not observed in at least 25% of the time (unique site x collection combinations). These complete absences and those nearly so, called sparse subsets of the data in this investigation, were the result of geoclimatic and seasonal variations, which are both independent of the treatment effect for which the …

Handling Non-Detects With Imputation In A Nested Design: A Simulation Study, Rose Adjei, John R. Stevens

Conference on Applied Statistics in Agriculture and Natural Resources

In this paper, a simulation study was conducted to assess whether it is ideal to address the issue of non-detects in data using a traditional substitution approach for non-detects, imputation, or a non-imputation based approach. Simulated data used were simple nested designs motivated by a real-life data in a study of bumble bee activity in a commercial cherry orchard by Kuivila et al. (2021). The simulated data were generated at different thresholds or censoring levels and at different effect sizes. For each simulated data, seven popular existing techniques to handle non-detects were applied: (i) Zero substitution, (ii) Substitution with half …

Overview Of Optimal Experimental Design And A Survey Of Its Expanse In Application To Agricultural Studies, Stephen J. Walsh

Conference on Applied Statistics in Agriculture and Natural Resources

Optimal Design of Experiments is currently recognized as the modern dominant approach to planning experiments in industrial engineering and manufacturing applications. This approach to design has gained traction among practitioners in the last two decades on two-fronts: 1) optimal designs are the result of a complicated optimization calculation and recent advances in both computing efficiency and algorithms have enabled this approach in real time for practitioners, and 2) such designs are now popular because they allow the researcher to ‘design for the experiment’ by working constraints, cost, number of experiments, and the model of the intended post-hoc data analysis into …

Pooling Of Variances: The Skeleton In The Mixed Model Closet?, Philip M. Dixon

Conference on Applied Statistics in Agriculture and Natural Resources

I explore three related issues concerning pooling of error variances: when is it appropriate (or not) to pool, how best to evaluate equality of variances, and whether there is a cost to never pooling. I focus on pooling decisions in a combined analysis of a multi-site experiment. A-priori, sites should have different error variances. My primary question is whether an analysis that ignores unequal variances is wrong.

I find that ignoring heteroscedasticity between sites maintains, or provides slightly conservative, tests of average treatment effects and treatment-by-site interactions. Models with site-specific variances do provide more powerful tests when variances are different. …