Journal of Agricultural Biological and Environmental Statistics最新文献

{"title":"Exploring the Efficacy of Statistical and Deep Learning Methods for Large Spatial Datasets: A Case Study","authors":"Arnab Hazra, Pratik Nag, Rishikesh Yadav, Ying Sun","doi":"10.1007/s13253-024-00602-4","DOIUrl":"https://doi.org/10.1007/s13253-024-00602-4","url":null,"abstract":"<p>Increasingly large and complex spatial datasets pose massive inferential challenges due to high computational and storage costs. Our study is motivated by the KAUST Competition on Large Spatial Datasets 2023, which tasked participants with estimating spatial covariance-related parameters and predicting values at testing sites, along with uncertainty estimates. We compared various statistical and deep learning approaches through cross-validation and ultimately selected the Vecchia approximation technique for model fitting. To overcome the constraints in the <span>R</span> package <span>GpGp</span>, which lacked support for fitting zero-mean Gaussian processes and direct uncertainty estimation—two things that are necessary for the competition, we developed additional <span>R</span> functions. Besides, we implemented certain subsampling-based approximations and parametric smoothing for skewed sampling distributions of the estimators. Our team DesiBoys secured the first position in two out of four sub-competitions and the second position in the other two, validating the effectiveness of our proposed strategies. Moreover, we extended our evaluation to a large real spatial satellite-derived dataset on total precipitable water, where we compared the predictive performances of different models using multiple diagnostics.</p>","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"527 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139759689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Two Tests of Significance for Preferred Direction in Tree Radial Growth Under a Linear-Circular Regression Model with Correlated Random Errors","authors":"Pierre Dutilleul, Tomoaki Imoto, Kunio Shimizu","doi":"10.1007/s13253-023-00599-2","DOIUrl":"https://doi.org/10.1007/s13253-023-00599-2","url":null,"abstract":"<p>To analyze tree growth statistically through annual ring widths measured in 2-D horizontal trunk sections, we propose two tests of significance defined under a linear-circular regression model with fixed trigonometric effects and normal random errors with a variance-covariance structure from the symmetric circulant family. The associated von Mises distribution has a preferred direction parameter. Accordingly, the first test aims to assess the presence of a preferred direction in the radial growth of a tree from the center of its trunk in a given year. Assuming there is a preferred direction of radial growth for the tree in two years, the second test extends the first one by assessing the equality of tree radial growth in the two preferred directions. Both tests of significance are modified <i>F</i>-tests with the denominator <i>df</i> adjusted for the presence of autocorrelation. Their validity is analyzed for two autoregressive symmetric circulant correlation structures, as a function of the number (<i>n</i>) of angular data and the autocorrelation parameter value. Effects of the inter-year correlation coefficient value are also studied in the two-year case. The performance of REstricted Maximum Likelihood as estimation method is scrutinized in an extensive Monte Carlo study, and the power of the tests is analyzed when valid. The new testing procedures are applied with <span>(n = 32, 64)</span> ring widths per year for a white spruce tree during 18 years of growth until its harvest. R codes are available. Conclusions and perspectives for future research are given. Supplementary materials accompanying this paper appear on-line.</p>","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"231 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139656375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Spatial Mixture Model for Spaceborne Lidar Observations Over Mixed Forest and Non-forest Land Types","authors":"Paul B. May, Andrew O. Finley, Ralph O. Dubayah","doi":"10.1007/s13253-024-00600-6","DOIUrl":"https://doi.org/10.1007/s13253-024-00600-6","url":null,"abstract":"<p>The Global Ecosystem Dynamics Investigation (GEDI) is a spaceborne lidar instrument that collects near-global measurements of forest structure. While expansive in scope, GEDI samples are spatially sparse and cover a small fraction of the land surface. Converting the sparse samples into spatially complete predictive maps is of practical importance for a number of ecological studies. A complicating factor is that GEDI collects measurements over forested and non-forested land alike, with no automatic labeling of the land type. Such classification is important, as it categorically influences the probability distribution of the spatial process and the ecological interpretation of the observations/predictions. We propose and implement a spatial mixture model, separating the observations and the greater spatial domain into two latent classes. The latent classes are governed by a Bernoulli spatial process, with spatial effects driven by a Gaussian process. Within each class, the process is governed by a separate spatial model, describing the unique probabilistic attributes. Model predictions take the form of scalar predictions of the GEDI observables as well as discrete labeling of the class membership. Inference is conducted through a Bayesian paradigm, yielding rich quantification of prediction and uncertainty through posterior predictive distributions. We demonstrate the method using GEDI data over Wollemi National Park, Australia, using optical data from Landsat 8 as model covariates. When compared to a single spatial model, the mixture model achieves much higher posterior predictive densities on the true value. When compared to a random forest model, a common algorithmic approach in the remote sensing community, the random forest achieves better absolute prediction accuracy for prediction locations far from observed training data locations, but at the expense of location-specific assessments of uncertainty. The unsupervised binary classifications of the mixture model appear broadly ecologically interpretable as forest and non-forest when compared to optical imagery, but further comparison to ground-truth data is required.</p>","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"35 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139649379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Clustering and Geodesic Scaling of Dissimilarities on the Spherical Surface","authors":"","doi":"10.1007/s13253-023-00597-4","DOIUrl":"https://doi.org/10.1007/s13253-023-00597-4","url":null,"abstract":"<h3>Abstract</h3> <p>Spherical embedding is an important tool in several fields of data analysis, including environmental data, spatial statistics, text mining, gene expression analysis, medical research and, in general, areas in which the geodesic distance is a relevant factor. Many data acquisition technologies are related to massive data acquisition, and these high-dimensional vectors are often normalised and transformed into spherical data. In this representation of data on spherical surfaces, multidimensional scaling plays an important role. Traditionally, the methods of clustering and representation have been combined, since the precision of the representation tends to decrease when a large number of objects are involved, which makes interpretation difficult. In this paper, we present a model that partitions objects into classes while simultaneously representing the cluster centres on a spherical surface based on geodesic distances. The model combines a partition algorithm based on the approximation of dissimilarities to geodesic distances with a representation procedure for geodesic distances. In this process, the dissimilarities are transformed in order to optimise the radius of the sphere. The efficiency of the procedure described is analysed by means of an extensive Monte Carlo experiment, and its usefulness is illustrated for real data sets. Supplementary material to this paper is provided online.</p>","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"2 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139649550","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Animal Density Estimation for Large Unmarked Populations Using a Spatially Explicit Model","authors":"Riki Herliansyah, Ruth King, Dede Aulia Rahman, Stuart King","doi":"10.1007/s13253-023-00598-3","DOIUrl":"https://doi.org/10.1007/s13253-023-00598-3","url":null,"abstract":"<p>Obtaining abundance and density estimates is a particularly important aspect within wildlife conservation and management. To monitor wildlife populations, the use of motion-sensor camera traps is becoming increasing popular due to its non-invasive nature. However, animal identification is not always feasible in practice due to poor quality images and/or individuals not having uniquely identifiable physical characteristics. Spatially explicit models for unmarked individuals permit the estimation of animal density when individuals cannot be uniquely identified. Due to the structure of these models, a Bayesian super-population (data augmentation) approach is often used to fit the models to data, which involves specifying some reasonably large upper limit for the population. However, this approach presents substantial computational challenges for larger populations, as demonstrated by the motivating dataset relating to barking deer (<i>Muntiacus muntjak</i>) collected in Ujung Kulon National Park, Indonesia (with a population size in the low thousands). We develop a new and computationally efficient Bayesian algorithm for fitting the models to data that does not require specifying an upper population limit <i>a priori</i>. We apply the new algorithm to the large barking deer dataset, where the standard super-population approach is computationally expensive, and demonstrate a substantial improvement in computational efficiency.Supplementary material to this paper is provided online.</p>","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"30 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2024-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139554890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimating Species Abundance from Presence–Absence Maps by Kernel Estimation","authors":"Ya-Mei Chang, Ying-Chi Huang","doi":"10.1007/s13253-023-00589-4","DOIUrl":"https://doi.org/10.1007/s13253-023-00589-4","url":null,"abstract":"<p>We present a novel method for estimating species abundance using presence–absence maps. Our approach takes the spatial context into consideration, distinguishing it from conventional methods. The proposed method is built upon a well-known kernel estimation for point pattern intensity, with the addition of a new parameter representing the mean abundance in each occupied cell. The parameter estimate is obtained through maximum likelihood estimation. The expected abundance corresponds to the integral of the intensity over the study area, which can be estimated by taking the Riemann sum of the intensity. The implementation of our method is straightforward, using existing packages in the R software. We compared various bandwidth selection methods within our approach and assessed the estimation performance against some approaches based on the random placement model or negative binomial model through the simulation study and an empirical forestry data in Barro Colorado Island (BCI), Panama. The results of the simulation and the application demonstrate that our method, with a carefully chosen bandwidth, outperforms the alternatives for highly aggregated data and improves the issue of underestimation. Supplementary materials accompanying this paper appear online.</p>","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"22 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508444","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A New Type of LASSO Regression Model with Cauchy Noise","authors":"Amir Hossein Ghatari, Mina Aminghafari, Adel Mohammadpour","doi":"10.1007/s13253-023-00583-w","DOIUrl":"https://doi.org/10.1007/s13253-023-00583-w","url":null,"abstract":"<p>Many datasets have heavy-tailed behavior, and classical penalized models are not appropriate for them. To treat this problem, we propose a penalized regression that handles model selection and outliers issues simultaneously. We provide a LASSO regression for models with Cauchy distributed noises using the negative log-likelihood loss function. To select the regularization parameter, we define <i>AIC</i> and <i>BIC</i> type criteria. We study the distribution of the regression coefficients estimator in the simulation experiments. In addition, simulation study and real datasets analysis confirm the superiority of the proposed method.</p>","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"116 1-2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508477","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing Spatial Stationarity and Segmenting Spatial Processes into Stationary Components","authors":"ShengLi Tzeng, Bo-Yu Chen, Hsin-Cheng Huang","doi":"10.1007/s13253-023-00588-5","DOIUrl":"https://doi.org/10.1007/s13253-023-00588-5","url":null,"abstract":"<p>In this research, we propose a novel technique for visualizing nonstationarity in geostatistics, particularly when confronted with a single realization of data at irregularly spaced locations. Our method hinges on formulating a statistic that tracks a stable microergodic parameter of the exponential covariance function, allowing us to address the intricate challenges of nonstationary processes that lack repeated measurements. We implement the fused lasso technique to elucidate nonstationary patterns at various resolutions. For prediction purposes, we segment the spatial domain into stationary sub-regions via Voronoi tessellations. Additionally, we devise a robust test for stationarity based on contrasting the sample means of our proposed statistics between two selected Voronoi subregions. The effectiveness of our method is demonstrated through simulation studies and its application to a precipitation dataset in Colorado. Supplementary materials accompanying this paper appear online.</p>","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"116 3","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508476","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatial Confounding and Spatial+ for Nonlinear Covariate Effects","authors":"Emiko Dupont, Nicole H. Augustin","doi":"10.1007/s13253-023-00586-7","DOIUrl":"https://doi.org/10.1007/s13253-023-00586-7","url":null,"abstract":"<p>Regression models for spatially varying data use spatial random effects to reflect spatial correlation structure. Such random effects, however, may interfere with the covariate effect estimates and make them unreliable. This problem, known as spatial confounding, is complex and has only been studied for models with linear covariate effects. However, as illustrated by a forestry example in which we assess the effect of soil, climate, and topography variables on tree health, the covariate effects of interest are in practice often unknown and nonlinear. We consider, for the first time, spatial confounding in spatial models with nonlinear effects implemented in the generalised additive models (GAMs) framework. We show that spatial+, a recently developed method for alleviating confounding in the linear case, can be adapted to this setting. In practice, spatial+ can then be used both as a diagnostic tool for investigating whether covariate effect estimates are affected by spatial confounding and for correcting the estimates for the resulting bias when it is present. Supplementary materials accompanying this paper appear online.</p>","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"20 2","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the Temperature-Driven Development of Stage-Structured Insect Populations with a Bayesian Hierarchical Model","authors":"Kala Studens, Benjamin M. Bolker, Jean-Noël Candau","doi":"10.1007/s13253-023-00581-y","DOIUrl":"https://doi.org/10.1007/s13253-023-00581-y","url":null,"abstract":"<p>The management of forest pests relies on an accurate understanding of the species’ phenology. Thermal performance curves (TPCs) have traditionally been used to model insect phenology. Many such models have been proposed and fitted to data from both wild and laboratory-reared populations. Using Hamiltonian Monte Carlo for estimation, we implement and fit an individual-level, Bayesian hierarchical model of insect development to the observed larval stage durations of a population reared in a laboratory at constant temperatures. This hierarchical model handles interval censoring and temperature transfers between two constant temperatures during rearing. It also incorporates individual variation, quadratic variation in development rates across insects’ larval stages, and “flexibility” parameters that allow for deviations from a parametric TPC. Using a Bayesian method ensures a proper propagation of parameter uncertainty into predictions and provides insights into the model at hand. The model is applied to a population of eastern spruce budworm (<i>Choristoneura fumiferana</i>) reared at 7 constant temperatures. Resulting posterior distributions can be incorporated into a workflow that provides prediction intervals for the timing of life stages under different temperature regimes. We provide a basic example for the spruce budworm using a year of hourly temperature data from Timmins, Ontario, Canada. Supplementary materials accompanying this paper appear on-line.</p>","PeriodicalId":56336,"journal":{"name":"Journal of Agricultural Biological and Environmental Statistics","volume":"5 1","pages":""},"PeriodicalIF":1.4,"publicationDate":"2023-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138508467","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}