Search · four archives
Search · four archives
21 papers · ranked by Valyu relevance
Sean C. Anderson, Eric J. Ward, Philina A. English, Lewis A. K. Barnett
Geostatistical data—spatially referenced observations related to some continuous spatial phenomenon— are ubiquitous in ecology and can reveal ecological processes and inform management decisions. However, appropriate models to analyze these data, such as generalized linear mixed effects models (GLMMs) with Gaussian…
Emiko Dupont, Simon N. Wood, Nicole H. Augustin
In spatial regression models, collinearity between covariates and spatial effects can lead to significant bias in effect estimates. This problem, known as spatial confounding, is encountered modeling forestry data to assess the effect of temperature on tree health. Reliable inference is difficult as results depend on…
John Hughes
Spatially referenced data arise in many fields, including imaging, ecology, public health, and marketing. Although principled smoothing or interpolation is paramount for many practitioners, regression, too, can be an important (or even the only or most important) goal of a spatial analysis. When doing spatial…
Sooran Kim, Mark S. Kaiser, Xiongtao Dai
Simulation Study and Analysis of COVID-19 Authors: ['Sooran Kim' 'Mark S. Kaiser' 'Xiongtao Dai'] Implementation of spatial generalized linear models with a functional covariate can be accomplished through the use of a truncated basis expansion of the covariate process. In practice, one must select a truncation level…
Roméo Tayewo, François Septier, Ido Nevat, Gareth W. Peters + 1 more
'Donald J. Jacobs'] We develop a new model for spatio-temporal data. More specifically, a graph penalty function is incorporated in the cost function in order to estimate the unknown parameters of a spatio-temporal mixed-effect model based on a generalized linear model. This model allows for more flexible and general…
Emiko Dupont, Simon N. Wood, Nicole H. Augustin
In spatial regression models, collinearity between covariates and spatial effects can lead to significant bias in effect estimates. This problem, known as spatial confounding, is encountered modelling forestry data to assess the effect of temperature on tree health. Reliable inference is difficult as results depend on…
Duarte S. Viana, Petr Keil, Alienor Jeliazkov
Community ecologists and macroecologists have long sought to evaluate the importance of environmental conditions in determining species distributions, community composition, and diversity across sites. Different methods have been used to estimate species-environment relationships, but their differences to jointly fit…
N. A. Cruz, J. D. Toloza-Delgado, Oscar O. Melo
This paper presents the generalized spatial autoregression (GSAR) model, a significant advance in spatial econometrics for non-normal response variables belonging to the exponential family. The GSAR model extends the logistic SAR, probit SAR, and Poisson SAR approaches by offering greater flexibility in modeling…
Mohammadreza Mohebbi, Rory Wolfe, Damien Jolley
Background Analytic methods commonly used in epidemiology do not account for spatial correlation between observations. In regression analyses, omission of that autocorrelation can bias parameter estimates and yield incorrect standard error estimates. Methods We used age standardised incidence ratios (SIRs) of…
Chaohao Gu, Hu Chen, Zhandong Liu
Spatial transcriptomics, situated at the intersection of genomics and spatial biology, offers profound insights into the spatial organization of gene expression within tissues. However, its potential has been constrained by either limited resolution or throughput. While single-cell RNA-seq allows for in-depth profiling…
Daisuke Murakami
| 1. | Introduction 2 | | --- | --- | | 1.1. | Outline 2 | | 1.2. | Model 2 | | 1.3. | Coding for specifying the transformation 5 | | 2. | Example 1: Disease mapping and regression with count data 6 | | 2.1. | Data 6 | | 2.2. | Model 8 | | 2.3. | Regression and disease mapping 11 | | 3. | Example 2: Spatial prediction…
Mahsa Nadifar, Hossein Baghishani, Afshin Fallah, Håvard Rue
Assessing water quality and recognizing its associated risks to human health and the broader environment is undoubtedly essential. Groundwater is widely used to supply water for drinking, industry, and agriculture purposes. The groundwater quality measurements vary for different climates and various human behaviors…
Julio G. Velazco, María Xosé Rodríguez-Álvarez, Martin P. Boer, David R. Jordan + 3 more
Key message A flexible and user-friendly spatial method called SpATS performed comparably to more elaborate and trial-specific spatial models in a series of sorghum breeding trials. Abstract Adjustment for spatial trends in plant breeding field trials is essential for efficient evaluation and selection of genotypes.…
Alexandre Courtiol, François Rousset
Isoscapes are maps depicting the continuous spatial (and sometimes temporal) variation in isotope composition. They have various applications ranging from the study of isotope circulation in the main earth systems to the determination of the provenance of migratory animals. Isoscapes can be produced from the fit of…
Kyle Lin Wu, Sudipto Banerjee
We investigate spatial confounding in the presence of multivariate disease dependence. In the "analysis model perspective" of spatial confounding, adding a spatially dependent random effect can lead to significant variance inflation of the posterior distribution of the fixed effects. The "data generation perspective"…
Francisco Louzada, Diego C. Nascimento, Osafu Augustine Egbon
Spatial documentation is exponentially increasing given the availability of Big IoT Data, enabled by the devices miniaturization and data storage capacity. Bayesian spatial statistics is a useful statistical tool to determine the dependence structure and hidden patterns over space through prior knowledge and data…
Ariel I. Mundo, John R. Tipton, Timothy J. Muldoon
In biomedical research, the outcome of longitudinal studies has been traditionally analyzed using the repeated measures analysis of variance (rm-ANOVA) or more recently, linear mixed models (LMEMs). Although LMEMs are less restrictive than rm-ANOVA in terms of correlation and missing observations, both methodologies…
Anna Gloria Billé, Giuseppe Arbia
Despite spatial econometrics is now considered a consolidated discipline, only in recent years we have experienced an increasing attention to the possibility of applying it to the field of discrete choices (e.g. Smirnov, 2010 for a recent review) and limited dependent variable models. In particular, only a small number…
Dylan G.E. Gomes
As (generalized) linear mixed-effects models (GLMMs) have become a widespread tool in ecology, the need to guide the use of such tools is increasingly important. One common ‘rule of thumb’ is that one needs at least five levels of a random effect. Having such few levels makes the estimation of the variance of random…
Benjamin F Trueman, Wendy H Krkošek, Graham A Gagnon
Orthophosphate can limit lead contamination of tap water, but its benefits are difficult to quantify since lead concentrations are so site-specific. Sentinel homes serviced by lead pipe are ideal for monitoring orthophosphate treatment, but best practices dictate the removal of lead once identified. The best sentinel…
Kevin Robben, Christopher Cheatum
We report a comprehensive study of the efficacy of least-squares fitting of multidimensional spectra to generalized Kubo lineshape models and introduce a novel least-squares fitting metric, termed the Scale Invariant Gradient Norm (SIGN), that enables a highly reliable and versatile algorithm. The precision of…