Nonparametric Competitors to the Two-Way ANOVA

↵LARRY E. TOOTHAKER is David Ross Boyd Professor of Psychology at the University of Oklahoma, Norman, OK 73019. He specializes in robustness of ANOVA, including repeated measures designs, multiple comparison procedures, and nonparametrics.Yeshttps://us.sagepub.com/en-us/nam/manuscript-submission-guideline

Investment Incentives Under Emission Trading: An Experimental Study

This paper presents the results of an experimental investigation on incentives to adopt advanced abatement technology under emissions trading. Our experimental design mimics an industry with small asymmetric polluting firms regulated by different schemes of tradable permits. We consider three allocation/auction policies: auctioning off (costly) permits through an ascending clock auction, grandfathering permits with re-allocation through a single-unit double auction, and grandfathering with re-allocation through an ascending clock auction. Our results confirm both dynamic and static theoretical equivalence of auctioning and grandfathering. We nevertheless find that although the market institution used to reallocate permits does not impact the dynamic efficiency from investment, it affects the static efficiency from permit trading

An Assessment of the Impact of Hafting on Paleoindian Point Variability

It has long been argued that the form of North American Paleoindian points was affected by hafting. According to this hypothesis, hafting constrained point bases such that they are less variable than point blades. The results of several studies have been claimed to be consistent with this hypothesis. However, there are reasons to be skeptical of these results. None of the studies employed statistical tests, and all of them focused on points recovered from kill and camp sites, which makes it difficult to be certain that the differences in variability are the result of hafting rather than a consequence of resharpening. Here, we report a study in which we tested the predictions of the hafting hypothesis by statistically comparing the variability of different parts of Clovis points. We controlled for the potentially confounding effects of resharpening by analyzing largely unused points from caches as well as points from kill and camp sites. The results of our analyses were not consistent with the predictions of the hypothesis. We found that several blade characters and point thickness were no more variable than the base characters. Our results indicate that the hafting hypothesis does not hold for Clovis points and indicate that there is a need to test its applicability in relation to post-Clovis Paleoindian points

Optimizing the procedure of grain nutrient predictions in barley via hyperspectral imaging

Hyperspectral imaging enables researchers and plant breeders to analyze various traits of interest like nutritional value in high throughput. In order to achieve this, the optimal design of a reliable calibration model, linking the measured spectra with the investigated traits, is necessary. In the present study we investigated the impact of different regression models, calibration set sizes and calibration set compositions on prediction performance. For this purpose, we analyzed concentrations of six globally relevant grain nutrients of the wild barley population HEB-YIELD as case study. The data comprised 1,593 plots, grown in 2015 and 2016 at the locations Dundee and Halle, which have been entirely analyzed through traditional laboratory methods and hyperspectral imaging. The results indicated that a linear regression model based on partial least squares outperformed neural networks in this particular data modelling task. There existed a positive relationship between the number of samples in a calibration model and prediction performance, with a local optimum at a calibration set size of ~40% of the total data. The inclusion of samples from several years and locations could clearly improve the predictions of the investigated nutrient traits at small calibration set sizes. It should be stated that the expansion of calibration models with additional samples is only useful as long as they are able to increase trait variability. Models obtained in a certain environment were only to a limited extent transferable to other environments. They should therefore be successively upgraded with new calibration data to enable a reliable prediction of the desired traits. The presented results will assist the design and conceptualization of future hyperspectral imaging projects in order to achieve reliable predictions. It will in general help to establish practical applications of hyperspectral imaging systems, for instance in plant breeding concepts