scholarly journals A linear mixed model formulation for spatio-temporal random processes with computational advances for the product, sum, and product-sum covariance functions

2021 ◽  
pp. 100510
Author(s):  
Michael Dumelle ◽  
Jay M. Ver Hoef ◽  
Claudio Fuentes ◽  
Alix Gitelman
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Processes ◽  
Model Formulation ◽  
Covariance Functions ◽  
Spatio Temporal

scholarly journals Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

2018 ◽  
Vol 147 ◽  
Author(s):  
A. Aswi ◽  
S. M. Cramb ◽  
P. Moraga ◽  
K. Mengersen
Keyword(s):  
Dengue Fever ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Assessment Tool ◽  
Temporal Dynamics ◽  
Random Effect ◽  
Temperature And Precipitation ◽  
Generalised Linear Mixed Model ◽  
Spatio Temporal

AbstractDengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.

scholarly journals Conventional and HD-tDCS May (or May Not) Modulate Overt Attentional Orienting: An Integrated Spatio-Temporal Approach and Methodological Reflections

2021 ◽  
Vol 12 (1) ◽  
pp. 71
Author(s):  
Lorenzo Diana ◽  
Giulia Scotti ◽  
Edoardo N. Aiello ◽  
Patrick Pilastro ◽  
Aleksandra K. Eberhard-Moscicka ◽  
...  
Keyword(s):  
Posterior Parietal Cortex ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Right Hemisphere ◽  
Visual Exploration ◽  
Frontal Eye Field ◽  
Baseline Performance ◽  
Spatio Temporal ◽  
The Individual ◽  
The Right

Transcranial Direct Current Stimulation (tDCS) has been employed to modulate visuo-spatial attentional asymmetries, however, further investigation is needed to characterize tDCS-associated variability in more ecological settings. In the present research, we tested the effects of offline, anodal conventional tDCS (Experiment 1) and HD-tDCS (Experiment 2) delivered over the posterior parietal cortex (PPC) and Frontal Eye Field (FEF) of the right hemisphere in healthy participants. Attentional asymmetries were measured by means of an eye tracking-based, ecological paradigm, that is, a Free Visual Exploration task of naturalistic pictures. Data were analyzed from a spatiotemporal perspective. In Experiment 1, a pre-post linear mixed model (LMM) indicated a leftward attentional shift after PPC tDCS; this effect was not confirmed when the individual baseline performance was considered. In Experiment 2, FEF HD-tDCS was shown to induce a significant leftward shift of gaze position, which emerged after 6 s of picture exploration and lasted for 200 ms. The present results do not allow us to conclude on a clear efficacy of offline conventional tDCS and HD-tDCS in modulating overt visuospatial attention in an ecological setting. Nonetheless, our findings highlight a complex relationship among stimulated area, focality of stimulation, spatiotemporal aspects of deployment of attention, and the role of individual baseline performance in shaping the effects of tDCS.

scholarly journals The t linear mixed model: model formulation, identifiability and estimation

2019 ◽  
pp. 1-25
Author(s):  
Marta Regis ◽  
Alberto Brini ◽  
Nazanin Nooraee ◽  
Reinder Haakma ◽  
Edwin R. van den Heuvel
Keyword(s):  
Mixed Model ◽  
Linear Mixed Model ◽  
Model Formulation

Heterogeneity of Fusarium Head Blight of Wheat: Multi-scale Distributions and Temporal Variation in Relation to Environment

2012 ◽  
Vol 13 (1) ◽  
pp. 30
Author(s):  
Alissa B. Kriss ◽  
Laurence V. Madden ◽  
Pierce A. Paul ◽  
Xiangming Xu
Keyword(s):  
Temporal Variation ◽  
Fusarium Head Blight ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Climate Index ◽  
Head Blight ◽  
Spatial And Temporal Scales ◽  
Multi Scale ◽  
Spatio Temporal ◽  
Serious Disease

Fusarium head blight (FHB) is a serious disease of wheat, which is highly variable at several spatial and temporal scales. Different statistical approaches were used to either quantify or partially explain this heterogeneity. First, a generalized linear mixed model was fitted to hierarchical survey data for the incidence of FHB in Ohio. Estimated variance terms indicated large and significant spatial heterogeneity among counties and among fields within counties, with substantially lower variation among sites within fields. Second, window-pane analysis was used to investigate the effects of environment on the inter-annual variation in FHB in four United States (US) states and the spatio-temporal variation across three European countries. Moisture- or wetness-related variables (e.g., average daily relative humidity) were positively associated with FHB intensity for multiple window lengths and starting times, especially for the last 2 months of the growing season. Third, cross-spectral analysis was used to determine whether there was coherency between variation in FHB in Ohio and global climatic patterns. There were significant coherencies at one or more inter-annual time scales (i.e., periods), with peaks in FHB following lows in the climate index (a reflection of a La Niña event) by about 1 year. Accepted for publication 27 April 2012. Published 23 July 2012.

scholarly journals Up hill, down dale: quantitative genetics of curvaceous traits

2005 ◽  
Vol 360 (1459) ◽  
pp. 1443-1455 ◽  
Author(s):  
Karin Meyer ◽  
Mark Kirkpatrick
Keyword(s):  
Quantitative Genetics ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Selection Response ◽  
Repeated Measurements ◽  
Random Regression ◽  
Model Framework ◽  
Covariance Functions ◽  
Continuous Scale ◽  
Estimated Breeding Values

‘Repeated’ measurements for a trait and individual, taken along some continuous scale such as time, can be thought of as representing points on a curve, where both means and covariances along the trajectory can change, gradually and continually. Such traits are commonly referred to as ‘function-valued’ (FV) traits. This review shows that standard quantitative genetic concepts extend readily to FV traits, with individual statistics, such as estimated breeding values and selection response, replaced by corresponding curves, modelled by respective functions. Covariance functions are introduced as the FV equivalent to matrices of covariances. Considering the class of functions represented by a regression on the continuous covariable, FV traits can be analysed within the linear mixed model framework commonly employed in quantitative genetics, giving rise to the so-called random regression model. Estimation of covariance functions, either indirectly from estimated covariances or directly from the data using restricted maximum likelihood or Bayesian analysis, is considered. It is shown that direct estimation of the leading principal components of covariance functions is feasible and advantageous. Extensions to multi-dimensional analyses are discussed.

Analyzing discontinuities in longitudinal count data: A multilevel generalized linear mixed model.

2020 ◽  
Author(s):  
James L. Peugh ◽  
Sarah J. Beal ◽  
Meghan E. McGrady ◽  
Michael D. Toland ◽  
Constance Mara
Keyword(s):  
Count Data ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Generalized Linear Mixed Model ◽  
Longitudinal Count Data

Predicting bycatch hotspots based on suitable habitat derived from fishery-independent data

2020 ◽  
Vol 641 ◽  
pp. 159-175
Author(s):  
J Runnebaum ◽  
KR Tanaka ◽  
L Guan ◽  
J Cao ◽  
L O’Brien ◽  
...  
Keyword(s):  
Habitat Suitability ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Gulf Of Maine ◽  
Homarus Americanus ◽  
Management Tool ◽  
American Lobster ◽  
Suitable Habitat ◽  
Climate Conditions ◽  
Suitability Index

Bycatch remains a global problem in managing sustainable fisheries. A critical aspect of management is understanding the timing and spatial extent of bycatch. Fisheries management often relies on observed bycatch data, which are not always available due to a lack of reporting or observer coverage. Alternatively, analyzing the overlap in suitable habitat for the target and non-target species can provide a spatial management tool to understand where bycatch interactions are likely to occur. Potential bycatch hotspots based on suitable habitat were predicted for cusk Brosme brosme incidentally caught in the Gulf of Maine American lobster Homarus americanus fishery. Data from multiple fisheries-independent surveys were combined in a delta-generalized linear mixed model to generate spatially explicit density estimates for use in an independent habitat suitability index. The habitat suitability indices for American lobster and cusk were then compared to predict potential bycatch hotspot locations. Suitable habitat for American lobster has increased between 1980 and 2013 while suitable habitat for cusk decreased throughout most of the Gulf of Maine, except for Georges Basin and the Great South Channel. The proportion of overlap in suitable habitat varied interannually but decreased slightly in the spring and remained relatively stable in the fall over the time series. As Gulf of Maine temperatures continue to increase, the interactions between American lobster and cusk are predicted to decline as cusk habitat continues to constrict. This framework can contribute to fisheries managers’ understanding of changes in habitat overlap as climate conditions continue to change and alter where bycatch interactions could occur.

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