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Spatial confounding in Bayesian species distribution modeling
estimation bias Gaussian process spatial confounding spatial random effect species distribution model
2023/11/20
Species distribution models (SDMs) are currently the main tools to derive species niche estimates and spatially explicit predictions for species geographical distribution. However, unobserved environm...
A GENERAL MINIMUM LOWER-ORDER CONFOUNDING CRITERION FOR TWO-LEVEL REGULAR DESIGNS
GENERAL MINIMUM LOWER-ORDER CONFOUNDING CRITERION TWO-LEVEL REGULAR DESIGNS
2015/3/20
Based on the hierarchical ordering principle of factorial effects in experimental design, we propose an aliased effect-number pattern (AENP) as a criterion to judge a two-level regular design; such a ...
A General Minimum Lower-Order Confounding Criterion for Two-Level Regular Designs
Clear effects criterion Estimation ability
2015/3/18
Based on the hierarchical ordering principle of factorial effects in experimental
design, we propose an aliased effect-number pattern (AENP) as a criterion to judge
a two-level regular design; such ...
CONFOUNDING CONSTITUENTS IN REMOTE SENSING OF PHYCOCYANIN
remote sensing phycocyanin blue-green algae cyanobacteria
2015/1/9
This project examines the impact of confounding variables that have limited the accuracy of remotely predicting phycocyanin in three Indiana drinking and recreational water reservoirs. In-situ field r...
Comparison And Application Of Methods To Address Confounding By Indication In Non-Randomized Clinical Studies
Antidepressant medication type 2 diabetes epidemiology marginal structural models propensity score survival analysis
2014/11/6
Objective: The project aimed to compare marginal structural models, and propensity score adjusted models with Cox Proportional Hazards models to address confounding by indication due to time-dependent...
Inference about ATE from Observational Studies with Continuous Outcome and Unmeasured Confounding
Inference about ATE Observational Studies Continuous Outcome Unmeasured Confounding
2013/4/28
For settings with a binary treatment and a binary outcome, instrumental variables can be used to construct bounds on a causal treatment effect. With continuous outcomes, meaningful bounds are more dif...
Confounding of three binary-variables counterfactual model
Causal effect Independence hypothesis Counterfactual model Confounding bias Irrelevant Ancillary information
2011/11/9
Confounding of three binary-variables counterfactual model is discussed in this paper. According to the effect between the control variable and the covariate variable, we investigate three counterfact...
The Importance of Scale for Spatial-Confounding Bias and Precision of Spatial Regression Estimators
Epidemiology, identifiability, mixed model,penalized likelihood random effects spatial correlation splines
2010/11/9
Residuals in regression models are often spatially correlated.Prominent examples include studies in environmental epidemiology to understand the chronic health effects of pollutants.
Deciphering the effect of climate change and separating the influence of confounding factors in sediment core records using additive models
Deciphering the effect of climate change separating the influence of confounding factors sediment core records using additive models
2014/4/17
We describe a new approach to modeling sediment core records, one that uses additive models (AMs) incorporating a serial correlation structure to model residual autocorrelation. Species assemblages, f...
Climate change as a confounding factor in reversibility of acidification: RAIN and CLIMEX projects
acid deposition global change
2009/3/24
The RAIN and CLIMEX experiments at Risdalsheia, southernmost Norway, together cover 17 years (1984-2000) of whole-catchment manipulation of acid deposition and climate. A 1200 m2 roof placed over the ...
This paper deals both with the issues of confounding and of control, as the definition of a confounding factor is far from universal and there exist different methodological approaches, ex ante and ex...