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JournalISSN: 1085-7117

Journal of Agricultural Biological and Environmental Statistics 

Springer Science+Business Media
About: Journal of Agricultural Biological and Environmental Statistics is an academic journal published by Springer Science+Business Media. The journal publishes majorly in the area(s): Population & Estimator. It has an ISSN identifier of 1085-7117. Over the lifetime, 924 publications have been published receiving 23374 citations. The journal is also known as: JABES.


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Journal ArticleDOI
TL;DR: In this paper, the similarity between two activity patterns may be quantified by a measure of the extent to which the patterns overlap, and several methods of estimating this overlap measure are described and their comparative performance for activity data is investigated in a simulation study.
Abstract: Data from camera traps that record the time of day at which photographs are taken are used widely to study daily activity patterns of photographed species. It is often of interest to compare activity patterns, for example, between males and females of a species or between a predator and a prey species. In this article we propose that the similarity between two activity patterns may be quantified by a measure of the extent to which the patterns overlap. Several methods of estimating this overlap measure are described and their comparative performance for activity data is investigated in a simulation study. The methods are illustrated by comparing activity patterns of three sympatric felid species using data from camera traps in Kerinci Seblat National Park, Sumatra.

818 citations

Journal ArticleDOI
TL;DR: Evidence is found that the most global model considered provides a poor fit to the data, hence an overdispersion factor is estimated to adjust model selection procedures and inflate standard errors.
Abstract: Few species are likely to be so evident that they will always be detected at a site when present. Recently a model has been developed that enables estimation of the proportion of area occupied, when the target species is not detected with certainty. Here we apply this modeling approach to data collected on terrestrial salamanders in the Plethodon glutinosus complex in the Great Smoky Mountains National Park, USA, and wish to address the question “how accurately does the fitted model represent the data?” The goodness-of-fit of the model needs to be assessed in order to make accurate inferences. This article presents a method where a simple Pearson chi-square statistic is calculated and a parametric bootstrap procedure is used to determine whether the observed statistic is unusually large. We found evidence that the most global model considered provides a poor fit to the data, hence estimated an overdispersion factor to adjust model selection procedures and inflate standard errors. Two hypothetical datasets with known assumption violations are also analyzed, illustrating that the method may be used to guide researchers to making appropriate inferences. The results of a simulation study are presented to provide a broader view of the methods properties.

715 citations

Journal ArticleDOI
TL;DR: The authors identify three major components of spatial variation in plot errors from field experiments and extend the two-dimensional spatial procedures of Cullis and Gleeson (1991) to account for them.
Abstract: We identify three major components of spatial variation in plot errors from field experiments and extend the two-dimensional spatial procedures of Cullis and Gleeson (1991) to account for them. The components are nonstationary, large-scale (global) variation across the field, stationary variation within the trial (natural variation or local trend), and extraneous variation that is often induced by experimental procedures and is predominantly aligned with rows and columns. We present a strategy for identifying a model for the plot errors that uses a trellis plot of residuals, a perspective plot of the sample variogram and, where possible, likelihood ratio tests to identify which components are present. We demonstrate the strategy using two illustrative examples. We conclude that although there is no one model that adequately fits all field experiments, the separable autoregressive model is dominant. However, there is often additional identifiable variation present.

690 citations

Journal ArticleDOI
TL;DR: In this article, the authors considered the design of early generation variety trials with a prespecified spatial correlation structure and introduced a new class of partially replicated designs called p-rep designs in which the plots of standard varieties are replaced by additional plots of test lines.
Abstract: This article considers the design of early generation variety trials with a prespecified spatial correlation structure and introduces a new class of partially replicated designs called p-rep designs in which the plots of standard varieties are replaced by additional plots of test lines. We show how efficient p-rep designs can be readily generated using the modified Reactive TABU search algorithm. The expected and realized genetic gain of p-rep and grid plot designs is compared in a simulation study.

599 citations

Journal ArticleDOI
TL;DR: A weighted quantile sum (WQS) approach to estimating a body burden index, which identifies “bad actors” in a set of highly correlated environmental chemicals, and demonstrates the improvement in accuracy this method provides over traditional ordinary regression and shrinkage methods.
Abstract: In risk evaluation, the effect of mixtures of environmental chemicals on a common adverse outcome is of interest. However, due to the high dimensionality and inherent correlations among chemicals that occur together, the traditional methods (e.g. ordinary or logistic regression) suffer from collinearity and variance inflation, and shrinkage methods have limitations in selecting among correlated components. We propose a weighted quantile sum (WQS) approach to estimating a body burden index, which identifies “bad actors” in a set of highly correlated environmental chemicals. We evaluate and characterize the accuracy of WQS regression in variable selection through extensive simulation studies through sensitivity and specificity (i.e., ability of the WQS method to select the bad actors correctly and not incorrect ones). We demonstrate the improvement in accuracy this method provides over traditional ordinary regression and shrinkage methods (lasso, adaptive lasso, and elastic net). Results from simulations demonstrate that WQS regression is accurate under some environmentally relevant conditions, but its accuracy decreases for a fixed correlation pattern as the association with a response variable diminishes. Nonzero weights (i.e., weights exceeding a selection threshold parameter) may be used to identify bad actors; however, components within a cluster of highly correlated active components tend to have lower weights, with the sum of their weights representative of the set.

499 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202328
202244
202149
202035
201936
201830