显示样式： 排序： IF:  GO 导出

Statistical Downscaling with Spatial Misalignment: Application to Wildland Fire $$\hbox {PM}_{2.5}$$ PM 2.5 Concentration Forecasting J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20201015
Suman Majumder, Yawen Guan, Brian J. Reich, Susan O’Neill, Ana G. RappoldFine particulate matter, PM\(_{2.5}\), has been documented to have adverse health effects, and wildland fires are a major contributor to \(\hbox {PM}_{2.5}\) air pollution in the USA. Forecasters use numerical models to predict PM\(_{2.5}\) concentrations to warn the public of impending health risk. Statistical methods are needed to calibrate the numerical model forecast using monitor data to reduce

Modeling Crop Phenology in the US Corn Belt Using Spatially Referenced SMOS Satellite Data J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20201014
Colin LewisBeck, Zhengyuan Zhu, Victoria Walker, Brian HornbuckleSatellite measurements follow the growth and senescence of vegetation aid in monitoring crop development within and across growing seasons. For example, identifying when crops reach their peak growth stage or modeling the seasonal growing cycle is useful for agronomists and climatologists. In this paper, we analyze remote sensing data from an intensively cultivated agricultural region in the Midwest

Testing Independence Between Two Spatial Random Fields J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20201009
ShihHao Huang, HsinCheng Huang, Ruey S. Tsay, Guangming PanIn this article, we consider testing independence between two spatial Gaussian random fields evaluated, respectively, at p and q locations with sample size n, where both p and q are allowed to be larger than n. We impose no spatial stationarity and no parametric structure for the two random fields. Our approach is based on canonical correlation analysis (CCA). But instead of applying CCA directly to

Multilevel Block Designs for Comparative Experiments J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20201008
Rodney N. EdmondsonComplete replicate block designs are fully efficient for treatment effects and are the designs of choice for many agricultural field experiments. For experiments with a large number of treatments, however, they may not provide good control of variability over the whole experimental area. Nested incomplete block designs with a single level of nesting can then improve ‘withinblock’ homogeneity for moderate

Guest Editors’ Introduction to the Special Issue on “Recent Advances in Design and Analysis of Experiments and Observational Studies in Agriculture” J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20201001
HansPeter Piepho, Robert J. Tempelman, Emlyn R. WilliamsThe Journal of Agricultural, Biological and Environment Statistics (JABES) special issue on Recent Advances in Design and Analysis of Experiments and Observational Studies in Agriculture covers a select set of topics currently of primary importance in the field. Efficient use of resources in agricultural research, as well as valid statistical inference, requires good designs, and this special issue

Optimization of Selective Phenotyping and Population Design for Genomic Prediction J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200926
Nicolas Heslot, Vitaliy FeoktistovGenomic prediction, the joint analysis of highdensity molecular marker data and phenotype to predict the performance of individuals for breeding purpose, is now a method used in routine in many plant and animal breeding programs. This opens several new design questions such as how to select a subset of preexisting individuals for phenotyping based on the molecular marker data to estimate marker effects

Linear Variance, Psplines and Neighbour Differences for Spatial Adjustment in Field Trials: How are they Related? J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200918
Martin P. Boer, HansPeter Piepho, Emlyn R. WilliamsNearestneighbour methods based on first differences are an approach to spatial analysis of field trials with a long history, going back to the early work by Papadakis first published in 1937. These methods are closely related to a geostatistical model that assumes spatial covariance to be a linear function of distance. Recently, Psplines have been proposed as a flexible alternative to spatial analysis

Spatial Sampling Design Using Generalized Neyman–Scott Process J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200915
Sze Him Leung, Ji Meng Loh, Chun Yip Yau, Zhengyuan ZhuIn this paper we introduce a new procedure for spatial sampling design. It is found in previous studies (Zhu and Stein in J Agric Biol Environ Stat 11:24–44, 2006) that the optimal sampling design for spatial prediction with estimated parameters is nearly regular with a few clustered points. The pattern is similar to a generalization of the Neyman–Scott (GNS) process (Yau and Loh in Statistica Sinica

Flexible Modeling of Variable Asymmetries in CrossCovariance Functions for Multivariate Random Fields J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200910
Ghulam A. Qadir, Carolina Euán, Ying SunThe geostatistical analysis of multivariate spatial data for inference as well as joint predictions (cokriging) ordinarily relies on modeling of the marginal and crosscovariance functions. While the former quantifies the spatial dependence within variables, the latter quantifies the spatial dependence across distinct variables. The marginal covariance functions are always symmetric; however, the

Do Spatial Designs Outperform Classic Experimental Designs? J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200829
Raegan Hoefler, Pablo GonzálezBarrios, Madhav Bhatta, Jose A. R. Nunes, Ines Berro, Rafael S. Nalin, Alejandra Borges, Eduardo Covarrubias, Luis DiazGarcia, Martin Quincke, Lucia GutierrezControlling spatial variation in agricultural field trials is the most important step to compare treatments efficiently and accurately. Spatial variability can be controlled at the experimental design level with the assignment of treatments to experimental units and at the modeling level with the use of spatial corrections and other modeling strategies. The goal of this study was to compare the efficiency

Bias Correction in Estimating Proportions by Imperfect Pooled Testing J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200819
Graham Hepworth, Brad J. BiggerstaffIn the estimation of proportions by pooled testing, the MLE is biased. Hepworth and Biggerstaff (JABES, 22:602–614, 2017) proposed an estimator based on the bias correction method of Firth (Biometrika 80:27–38, 1993) and showed that it is almost unbiased across a range of pooled testing problems involving no misclassification. We now extend their work to allow for imperfect testing. We derive the estimator

A Generic Method for Estimating and Smoothing Multispecies Biodiversity Indicators Using Intermittent Data J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200817
Stephen N. Freeman, Nicholas J. B. Isaac, Panagiotis Besbeas, Emily B. Dennis, Byron J. T. MorganBiodiversity indicators summarise extensive, complex ecological data sets and are important in influencing government policy. Component data consist of timevarying indices for each of a number of different species. However, current biodiversity indicators suffer from multiple statistical shortcomings. We describe a statespace formulation for new multispecies biodiversity indicators, based on rates

Nonparametric Bayesian Functional MetaRegression: Applications in Environmental Epidemiology J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200809
Jaeeun Yu, Jinsu Park, Taeryon Choi, Masahiro Hashizume, Yoonhee Kim, Yasushi Honda, Yeonseung ChungTwostage metaanalysis has been popularly used in epidemiological studies to investigate an association between environmental exposure and health response by analyzing timeseries data collected from multiple locations. The first stage estimates the locationspecific association, while the second stage pools the associations across locations. The second stage often incorporates locationspecific predictors

Spatial Spread Sampling Using Weakly Associated Vectors J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200725
Raphaël Jauslin, Yves TilléGeographical data are generally autocorrelated. In this case, it is preferable to select spread units. In this paper, we propose a new method for selecting wellspread samples from a finite spatial population with equal or unequal inclusion probabilities. The proposed method is based on the definition of a spatial structure by using a stratification matrix. Our method exactly satisfies given inclusion

The Design of EarlyStage Plant Breeding Trials Using Genetic Relatedness J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200716
Brian R. Cullis, Alison B. Smith, Nicole A. Cocks, David G. ButlerThe use of appropriate statistical methods has a key role in improving the accuracy of selection decisions in a plant breeding program. This is particularly important in the early stages of testing in which selections are based on data from a limited number of field trials that include large numbers of breeding lines with minimal replication. The method of analysis currently recommended for earlystage

Optimal Design of Experiments for Hybrid Nonlinear Models, with Applications to Extended Michaelis–Menten Kinetics J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200715
Yuanzhi Huang, Steven G. Gilmour, Kalliopi Mylona, Peter GoosBiochemical mechanism studies often assume statistical models derived from Michaelis–Menten kinetics, which are used to approximate initial reaction rate data given the concentration level of a single substrate. In experiments dealing with industrial applications, however, there are typically a wide range of kinetic profiles where more than one factor is controlled. We focus on optimal design of such

A Nonstationary Spatial Covariance Model for Processes Driven by Point Sources J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200703
Joshua L. WarrenWe introduce a new nonstationary spatial covariance model for analyzing geostatistical pointreferenced data that contain point sources (i.e., known locations that impact the outcome). Our model is based on viewing the spatial domain on the polar coordinate scale, with the point source representing the reference location. As a result, we incorporate distances from the point source and angles of the

Vecchia Approximations of GaussianProcess Predictions J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200623
Matthias Katzfuss, Joseph Guinness, Wenlong Gong, Daniel ZilberGaussian processes (GPs) are highly flexible function estimators used for geospatial analysis, nonparametric regression, and machine learning, but they are computationally infeasible for large datasets. Vecchia approximations of GPs have been used to enable fast evaluation of the likelihood for parameter inference. Here, we study Vecchia approximations of spatial predictions at observed and unobserved

PseudoLikelihood or Quadrature? What We Thought We Knew, What We Think We Know, and What We Are Still Trying to Figure Out J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200621
Walt Stroup, Elizabeth ClaassenTwo predominant computing methods for generalized linear mixed models (GLMMs) are linearization, e.g., pseudolikelihood (PL), and integral approximation, e.g., Gauss–Hermite quadrature. The primary GLMM package in R, LME4, only uses integral approximation. The primary GLMM procedure in SAS®, PROC GLIMMIX, was originally developed using linearization, but integral approximation methods were added in

A Bayesian Markov Model with PólyaGamma Sampling for Estimating Individual Behavior Transition Probabilities from Accelerometer Classifications J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200615
Toryn L. J. Schafer, Christopher K. Wikle, Jay A. VonBank, Bart M. Ballard, Mitch D. WeegmanThe use of accelerometers in wildlife tracking provides a finescale data source for understanding animal behavior and decision making. Current methods in movement ecology focus on behavior as a driver of movement mechanisms. Our Markov model is a flexible and efficient method for inference related to effects on behavior that considers dependence between current and past behaviors. We applied this

Adjusting for Spatial Effects in Genomic Prediction J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200605
Xiaojun Mao, Somak Dutta, Raymond K. W. Wong, Dan NettletonThis paper investigates the problem of adjusting for spatial effects in genomic prediction. Despite being seldomly considered in genomic prediction, spatial effects often affect phenotypic measurements of plants. We consider a Gaussian random field model with an additive covariance structure that incorporates genotype effects, spatial effects and subpopulation effects. An empirical study shows the

A Distancebased Method for Spatial Prediction in the Presence of Trend J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200601
Carlos E. Melo, Jorge Mateu, Oscar O. MeloA new method based on distances for modeling continuous random data in Gaussian random fields is presented. In nonstationary cases in which a trend or drift is present, dealing with information in regionalized mixed variables (including categorical, discrete and continuous variables) is common in geosciences and environmental sciences. The proposed distancebased method is used in a geostatistical

Systematic Statistical Analysis of Microbial Data from Dilution Series J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200528
J. Andrés Christen, Albert E. ParkerIn microbial studies, samples are often treated under different experimental conditions and then tested for microbial survival. A technique, dating back to the 1880s, consists of diluting the samples several times and incubating each dilution to verify the existence of microbial colonyforming units or CFU’s, seen by the naked eye. The main problem in the dilution series data analysis is the uncertainty

Estimating Changes in the Observed Relationship Between Humidity and Temperature Using Noncrossing Quantile Smoothing Splines J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200515
Karen A. McKinnon, Andrew PoppickThe impacts of warm season heat extremes are dependent on both temperature and humidity, so it is critical to properly model their relationship, including how it may be changing. This presents statistical challenges because the bivariate temperature–humidity (measured here by dew point) distribution is complex and spatially variable. Here, we develop a flexible, semiparametric model based on quantile

History of the Statistical Design of Agricultural Experiments J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200505
L. Rob VerdoorenIn Section 1 the approach of improving crop yields by the development of agriculture and addition of various mineral or organic substances in the last 200–300 years is investigated. In Section 2 the principle of randomized experiments is treated. Section 3 describes the variety trials of field crops. The elimination of effects in two dimensions is shown in Section 4 on Row–Column designs. Fertilizer

Obtaining a Balanced Area Sample for the Bureau of Land Management Rangeland Survey J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200330
Cindy L. Yu, Jie Li, Michael G. Karl, Todd J. KruegerIn agricultural and environmental surveys, obtaining spatially balanced area samples that are also representative probability samples in the presence of auxiliary variables is a challenge, especially when the study regions have fragmentary boundaries and possess holes of various shapes. This paper describes a sampling procedure that achieves this goal and is implemented in the U.S. Department of the

Projecting FloodInducing Precipitation with a Bayesian Analogue Model J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200327
Gregory P. Bopp, Benjamin A. Shaby, Chris E. Forest, Alfonso MejíaThe hazard of pluvial flooding is largely influenced by the spatial and temporal dependence characteristics of precipitation. When extreme precipitation possesses strong spatial dependence, the risk of flooding is amplified due to catchment factors such as topography that cause runoff accumulation. Temporal dependence can also increase flood risk as storm water drainage systems operating at capacity

Hierarchical Modeling of Structural Coefficients for Heterogeneous Networks with an Application to Animal Production Systems J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200319
K. Chitakasempornkul, G. J. M. Rosa, A. Jager, N. M. BelloUnderstanding the interconnections between performance outcomes in a system is increasingly important for integrated management. Structural equation models (SEMs) are a type of multiplevariable modeling strategy that allows investigation of directionality in the association between outcome variables, thereby providing insight into their interconnections as putative causal links defining a functional

Estimation in Complex Sampling Designs Based on Resampling Methods J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200312
Bardia PanahbehaghGenerally, to select a representative sample of the population, we use a combination of several probabilistic sampling methods which is called a complex sampling design. A complex sampling design usually needs very sophisticated mathematical calculations to provide unbiased estimators of the population parameters. Therefore, only a limited number of sampling designs are commonly used in practice. In

Modeling Partially Surveyed Point Process Data: Inferring Spatial Point Intensity of Geomagnetic Anomalies J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200312
Kenneth A. Flagg, Andrew Hoegh, John J. BorkowskiMany former military training sites contain unexploded ordnance (UXO) and require environmental remediation. For the first phase of UXO remediation, locations of geomagnetic anomalies are recorded over a subregion of the study area to infer the spatial intensity of anomalies and identify high concentration areas. The data resulting from this sampling process contain locations of anomalies across narrow

Substitutes for the Nonexistent Square Lattice Designs for 36 Varieties J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200304
R. A. Bailey, Peter J. Cameron, L. H. Soicher, E. R. WilliamsSquare lattice designs are often used in trials of new varieties of various agricultural crops. However, there are no square lattice designs for 36 varieties in blocks of size six for four or more replicates. Here, we use three different approaches to construct designs for up to eight replicates. All the designs perform well in terms of giving a low average variance of variety contrasts. Supplementary

Sampling Strategies to Estimate Deer Density by Drive Counts J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200226
Lorenzo Fattorini, Alberto Meriggi, Enrico Merli, Paolo VaruzzaThe best evaluation of deer density can be achieved by accurate drive counts of deer performed in all the suitable wooded patches of the area of interest. This would provide the true density within drive areas which, in turn, should be akin to the true density within the study area. Because the drive of all these areas is prohibitive, only a subset is usually driven. Results are highly dependent on

Estimating Reproduction and Survival of Unmarked Juveniles Using Aerial Images and Marked Adults J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200222
Perry J. Williams, Cody Schroeder, Pat JacksonMethods for estimating juvenile survival of wildlife populations often rely on intensive data collection efforts to capture and uniquely mark individual juveniles and observe them through time. Capturing juveniles in a time frame sufficient to estimate survival can be challenging due to narrow and stochastic windows of opportunity. For many animals, juvenile survival depends on postnatal parental care

Statistical Development of Animal Density Estimation Using Random Encounter Modelling J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200222
N. O. A. S. Jourdain, D. J. Cole, M. S. Ridout, J. Marcus RowcliffeCamera trapping is widely used in ecological studies to estimate animal density, although these studies are largely restricted to animals that can be identified to the individual level. The random encounter model, developed by Rowcliffe et al. (J Anal Ecol 45(4):1228–1236, 2008), estimates animal density from cameratrap data without the need to identify animals. Although the REM can provide reliable

Correction to: Understanding the Stochastic Partial Differential Equation Approach to Smoothing J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20200220
David L. Miller, Richard Glennie, Andrew E. SeatonUnfortunately, in the original publication of the article, several definitional mistakes crept into the manuscript during writing.

Robust Nonparametric Regression for HeavyTailed Data J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20191205
Ferdos Gorji, Mina AminghafariWe propose a robust nonparametric regression method that can deal with heavytailed noise and also a heavytailed input variable. We decompose the trajectory matrix of the response variable of the regression problem to extract the regression function in a nonparametric way. We implement the decomposition in a robust way using iterative robust linear regressions. We show the effectiveness of the proposed

RightCensored Mixed Poisson Count Models with Detection Times J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20191115
WenHan Hwang, Rachel V. Blakey, Jakub StoklosaConducting complete surveys on flora and fauna species within a sampling unit (or quadrat) of interest can be costly, particularly if there are several species in high abundance. A commonly used approach, which aims to reduce time and costs, consists of occurrence data reflecting the status of occupancy of a species– e.g., rather than counting every individual, the survey is stopped as soon as one

Efficient Modelling of PresenceOnly Species Data via Local Background Sampling J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20191102
Jeffrey Daniel, Julie Horrocks, Gary J. UmphreyIn species distribution modelling, records of species presence are often modelled as a realization of a spatial point process whose intensity is a function of environmental covariates. One way to fit a spatial point process model is to apply logistic regression to an artificial case–control sample consisting of the observed presence records combined with a simulated pattern of background points, usually

A Case Study Competition Among Methods for Analyzing Large Spatial Data. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20190910
Matthew J Heaton,Abhirup Datta,Andrew O Finley,Reinhard Furrer,Joseph Guinness,Rajarshi Guhaniyogi,Florian Gerber,Robert B Gramacy,Dorit Hammerling,Matthias Katzfuss,Finn Lindgren,Douglas W Nychka,Furong Sun,Andrew ZammitMangionThe Gaussian process is an indispensable tool for spatial data analysts. The onset of the "big data" era, however, has lead to the traditional Gaussian process being computationally infeasible for modern spatial data. As such, various alternatives to the full Gaussian process that are more amenable to handling big spatial data have been proposed. These modern methods often exploit lowrank structures

Modeling and Prediction of Multiple Correlated Functional Outcomes. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20190409
Jiguo Cao,Kunlaya Soiaporn,Raymond J Carroll,David RuppertWe propose a copulabased approach for analyzing functional data with correlated multiple functional outcomes exhibiting heterogeneous shape characteristics. To accommodate the possibly large number of parameters due to having several functional outcomes, parameter estimation is performed in two steps: first, the parameters for the marginal distributions are estimated using the skew t family, and then

Bias Correction in Estimating Proportions by Pooled Testing. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20190115
Graham Hepworth,Brad J BiggerstaffIn the estimation of proportions by pooled testing, the MLE is biased, and several methods of correcting the bias have been presented in previous studies. We propose a new estimator based on the bias correction method introduced by Firth (Biometrika 80:2738, 1993), which uses a modification of the score function, and we provide an easily computable, NewtonRaphson iterative formula for its computation

ExactPermutation Based Sign Tests for Clustered Binary Data via Weighted and Unweighted Test Statistics. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20170620
Janie McDonald,Patrick D Gerard,Christopher S McMahan,William R SchucanyClustered binary data occur frequently in many application areas. When analyzing data of this form, ignoring key features, such as the intracluster correlation, may lead to inaccurate inference; e.g., inflated Type I error rates. For clustered binary data, Gerard and Schucany (2007) proposed an exact test for examining whether the marginal probability of a response differs from 0.5, which is the null

A Semiparametric Bayesian Approach for Differential Expression Analysis of RNAseq Data. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20160830
Fangfang Liu,Chong Wang,Peng LiuRNAsequencing (RNAseq) technologies have revolutionized the way agricultural biologists study gene expression as well as generated a tremendous amount of data waiting for analysis. Detecting differentially expressed genes is one of the fundamental steps in RNAseq data analysis. In this paper, we model the count data from RNAseq experiments with a PoissonGamma hierarchical model, or equivalently

Estimability Analysis and Optimal Design in Dynamic Multiscale Models of Cardiac Electrophysiology. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20160623
Matthew S Shotwell,Richard A GrayWe present an applied approach to optimal experimental design and estimability analysis for mechanistic models of cardiac electrophysiology, by extending and improving on existing computational and graphical methods. These models are 'multiscale' in the sense that the modeled phenomena occur over multiple spatiotemporal scales (e.g., single cell vs. whole heart). As a consequence, empirical observations

Empirical Bayes analysis of RNAseq data for detection of gene expression heterosis. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20160506
Jarad Niemi,Eric Mittman,Will Landau,Dan NettletonAn important type of heterosis, known as hybrid vigor, refers to the enhancements in the phenotype of hybrid progeny relative to their inbred parents. Although hybrid vigor is extensively utilized in agriculture, its molecular basis is still largely unknown. In an effort to understand phenotypic heterosis at the molecular level, researchers are measuring transcript abundance levels of thousands of

Hierarchical Modeling and Differential Expression Analysis for RNAseq Experiments with Inbred and Hybrid Genotypes. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20160426
Andrew Lithio,Dan NettletonThe performance of inbred and hybrid genotypes is of interest in plant breeding and genetics. Highthroughput sequencing of RNA (RNAseq) has proven to be a useful tool in the study of the molecular genetic responses of inbreds and hybrids to environmental stresses. Commonly used experimental designs and sequencing methods lead to complex data structures that require careful attention in data analysis

Detecting Differentially Expressed Genes with RNAseq Data Using Backward Selection to Account for the Effects of Relevant Covariates. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20151215
Yet Nguyen,Dan Nettleton,Haibo Liu,Christopher K TuggleA common challenge in analysis of transcriptomic data is to identify differentially expressed genes, i.e., genes whose mean transcript abundance levels differ across the levels of a factor of scientific interest. Transcript abundance levels can be measured simultaneously for thousands of genes in multiple biological samples using RNA sequencing (RNAseq) technology. Part of the variation in RNAseq

Incorporating Genetic Heterogeneity in WholeGenome Regressions Using Interactions. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20151215
Gustavo de Los Campos,Yogasudha Veturi,Ana I Vazquez,Christina Lehermeier,Paulino PérezRodríguezNaturally and artificially selected populations usually exhibit some degree of stratification. In GenomeWide Association Studies and in WholeGenome Regressions (WGR) analyses, population stratification has been either ignored or dealt with as a potential confounder. However, systematic differences in allele frequency and in patterns of linkage disequilibrium can induce subpopulationspecific effects

Assessing Assay Variability of Pesticide Metabolites in the Presence of Heavy LeftCensoring. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20150530
Haiying Chen,Sara A Quandt,Dana Boyd Barr,Thomas A ArcuryAssessing assay variability for field samples in environmental research is challenging, since a quantitative assay is typically constrained by a lower limit of detection. The purpose of this paper is to compare three parametric models for assessing assay variability using duplicate data subject to heavy leftcensoring. Efron information criterion (EIC) and Bayesian information criterion (BIC) are used

Characterization of Weighted Quantile Sum Regression for Highly Correlated Data in a Risk Analysis Setting. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20150301
Caroline Carrico,Chris Gennings,David C Wheeler,Pam FactorLitvakIn 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

Maximum Pairwise PseudoLikelihood Estimation of the Covariance Matrix from LeftCensored Data. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20150301
Michael P Jones,Sarah S Perry,Peter S ThorneToxicological studies often depend on laboratory assays that have thresholds below which environmental pollutants cannot be measured with accuracy. Exposure levels below this limit of detection may well be toxic and hence it is vital to use data analytic methods that handle such leftcensored data with as little estimation bias as possible. In an ongoing study for which our methodology is developed

Estimation and Testing of Gene Expression Heterosis. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20141202
Tieming Ji,Peng Liu,Dan NettletonHeterosis, also known as the hybrid vigor, occurs when the mean phenotype of hybrid offspring is superior to that of its two inbred parents. The heterosis phenomenon is extensively utilized in agriculture though the molecular basis is still unknown. In an effort to understand phenotypic heterosis at the molecular level, researchers have begun to compare expression levels of thousands of genes between

Nonlinear Varying Coefficient Models with Applications to Studying Photosynthesis. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20140701
Esra Kürüm,Runze Li,Yang Wang,Damla SEntürkMotivated by a study on factors affecting the level of photosynthetic activity in a natural ecosystem, we propose nonlinear varying coefficient models, in which the relationship between the predictors and the response variable is allowed to be nonlinear. Onestep local linear estimators are developed for the nonlinear varying coefficient models and their asymptotic normality is established leading

Hierarchical Rank Aggregation with Applications to Nanotoxicology. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20140520
Trina Patel,Donatello Telesca,Robert Rallo,Saji George,Tian Xia,André E NelThe development of high throughput screening (HTS) assays in the field of nanotoxicology provide new opportunities for the hazard assessment and ranking of engineered nanomaterials (ENMs). It is often necessary to rank lists of materials based on multiple risk assessment parameters, often aggregated across several measures of toxicity and possibly spanning an array of experimental platforms. Bayesian

Inference for Size Demography from Point Pattern Data using Integral Projection Models. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20131114
Souparno Ghosh,Alan E Gelfand,James S ClarkPopulation dynamics with regard to evolution of traits has typically been studied using matrix projection models (MPMs). Recently, to work with continuous traits, integral projection models (IPMs) have been proposed. Imitating the path with MPMs, IPMs are handled first with a fitting stage, then with a projection stage. Fitting these models has so far been done only with individuallevel transition

Estimating the Health Impact of Climate Change with Calibrated Climate Model Output. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20130917
Jingwen Zhou,Howard H Chang,Montserrat FuentesStudies on the health impacts of climate change routinely use climate model output as future exposure projection. Uncertainty quantification, usually in the form of sensitivity analysis, has focused predominantly on the variability arise from different emission scenarios or multimodel ensembles. This paper describes a Bayesian spatial quantile regression approach to calibrate climate model output

Approximate and PseudoLikelihood Analysis for Logistic Regression Using External Validation Data to Model Log Exposure. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20130913
Robert H Lyles,Lawrence L KupperA common goal in environmental epidemiologic studies is to undertake logistic regression modeling to associate a continuous measure of exposure with binary disease status, adjusting for covariates. A frequent complication is that exposure may only be measurable indirectly, through a collection of subjectspecific variables assumed associated with it. Motivated by a specific study to investigate the

Estimating Velocity for Processive Motor Proteins with Random Detachment. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20130605
John Hughes,Shankar Shastry,William O Hancock,John FricksWe show that, for a wide range of models, the empirical velocity of processive motor proteins has a limiting Pearson type VII distribution with finite mean but infinite variance. We develop maximum likelihood inference for this Pearson type VII distribution. In two simulation studies, we compare the performance of our MLE with the performance of standard Student's tbased inference. The studies show

Uncertainty Analysis for Computationally Expensive Models with Multiple Outputs. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20121201
David Ruppert,Christine A Shoemaker,Yilun Wang,Yingxing Li,Nikolay BliznyukBayesian MCMC calibration and uncertainty analysis for computationally expensive models is implemented using the SOARS (Statistical and Optimization Analysis using Response Surfaces) methodology. SOARS uses a radial basis function interpolator as a surrogate, also known as an emulator or metamodel, for the logarithm of the posterior density. To prevent wasteful evaluations of the expensive model,

Flexible Distributed Lag Models using Random Functions with Application to Estimating Mortality Displacement from HeatRelated Deaths. J. Agric. Biol. Environ. Stat. (IF 1.65) Pub Date : 20121106
Matthew J Heaton,Roger D PengAs climate continues to change, scientists are left to analyze the effects these changes will have on the public. In this article, a flexible class of distributed lag models are used to analyze the effects of heat on mortality in four major metropolitan areas in the U.S. (Chicago, Dallas, Los Angeles, and New York). Specifically, the proposed methodology uses Gaussian processes as a prior model for