Bayesian Regression Modeling with Inla

Wang, Xiaofeng; Yu, Yue; Faraway, Julian J.

doi:10.1201/9781351165761

Cited by 108 publications

(113 citation statements)

References 0 publications

Supporting

Mentioning

109

Contrasting

Unclassified

Order By: Relevance

“…The inla() function allows for missing values in the response variable, and computes the posterior marginal for the corresponding linear predictor. One does not need a posterior predictive simulation like in McMC approaches [36]. INLA will automatically compute the predictive distributions for all missing values in the response, which should be assigned a "NA" value when defining the data.…”

Section: Dengue Nowcasting Corrected For Reporting Delaymentioning

confidence: 99%

“…In conventional sampling-based Bayesian analysis, the prediction can be done by posterior predictive simulation, i.e., drawing random samples from the posterior distribution. In the INLA library, there is no function "predict" as, for example, lm command in R. However, one does not need a posterior predictive sampling like in McMC approaches [36]. Predictions can be done as a part of the model fitting itself in INLA.…”

Section: Authors' Contributionsmentioning

confidence: 99%

“…Predictions can be done as a part of the model fitting itself in INLA. As prediction is the same as fitting a model with some missing data, we need to set the response variables "NA" for those observations" we want to obtain estimates from the predictive distribution [36,40]. Suppose we have the real-time surveillance reporting delay structure as in Fig.…”

Section: Authors' Contributionsmentioning

confidence: 99%

See 2 more Smart Citations

Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand

et al. 2020

View full text Add to dashboard Cite

Background: The ability to produce timely and accurate estimation of dengue cases can significantly impact disease control programs. A key challenge for dengue control in Thailand is the systematic delay in reporting at different levels in the surveillance system. Efficient and reliable surveillance and notification systems are vital to monitor health outcome trends and early detection of disease outbreaks which vary in space and time.Methods: Predicting the trend in dengue cases in real-time is a challenging task in Thailand due to a combination of factors including reporting delays. We present decision support using a spatiotemporal nowcasting model which accounts for reporting delays in a Bayesian framework with sliding windows. A case study is presented to demonstrate the proposed nowcasting method using weekly dengue surveillance data in Bangkok at district level in 2010. Results:The overall real-time estimation accuracy was 70.69% with 59.05% and 79.59% accuracy during low and high seasons averaged across all weeks and districts. The results suggest the model was able to give a reasonable estimate of the true numbers of cases in the presence of delayed reports in the surveillance system. With sliding windows, models could also produce similar accuracy to estimation with the whole data. Conclusions:A persistent challenge for the statistical and epidemiological communities is to transform data into evidence-based knowledge that facilitates policy making about health improvements and disease control at the individual and population levels. Improving real-time estimation of infectious disease incidence is an important technical development. The effort in this work provides a template for nowcasting in practice to inform decision making for dengue control.

show abstract

Section: Dengue Nowcasting Corrected For Reporting Delaymentioning

confidence: 99%

Section: Authors' Contributionsmentioning

confidence: 99%

Section: Authors' Contributionsmentioning

confidence: 99%

See 1 more Smart Citation

Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand

et al. 2020

View full text Add to dashboard Cite

show abstract

“…However, thanks to the availability of Markov chain Monte Carlo (MCMC) samplers like JAGS (Plummer, 2003) or Stan (Carpenter et al, 2017), or the computationally efficient alternative provided by INLA, Bayesian computation has become increasingly accessible and more popular in the past decade. An advantage of the INLA approach used here over existing algorithms is that it circumvents time-consuming MCMC sampling by providing efficient approximations of marginal posterior distributions, and it has proven to be particularly useful for fitting GLMMs (Fong et al, 2010;Wang et al, 2018), spatial and space-time models (Blangiardo et al, 2013;Bakka et al, 2018), for modelling abundance data collected using distance sampling (Yuan et al, 2016), and for modelling species distributions more generally (Illian et al, 2013;Bakka et al, 2016). Here we discuss how to take advantage of INLA via its R interface R-INLA in the context of RSF and SSF modelling.…”

Section: Bayesian Computation For Rsfs and Ssfsmentioning

confidence: 99%

Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation

Muff

Signer

Fieberg

2018

Preprint

View full text Add to dashboard Cite

Word count: ≈ 8000 (without references).2 Abstract 1. Popular frameworks for studying habitat selection include resource-selection functions (RSFs) and step-selection functions (SSFs) estimated using logistic and conditional logistic regression, respectively. Both frameworks compare environmental covariates associated with locations animals visit with environmental covariates at a set of locations assumed available to the animal. Conceptually, random coefficients could be used to accommodate inter-individual heterogeneity with either approach, but straightforward and efficient one-step procedures for fitting SSFs with random coefficients are currently lacking.2. We take advantage of the fact that the conditional logistic regression model (i. e., the SSF) is likelihood-equivalent to a Poisson model with stratumspecific intercepts. By interpreting the intercepts as a random effect with a large (fixed) variance, inference becomes feasible with standard Bayesian techniques, but also with frequentist methods that allow one to fix the variance of a random effect. We compare this approach to other commonly applied alternatives, including random intercept-only models, and to a two-step algorithm for fitting mixed-effects models.3. We also reinforce the need to weight available points when fitting RSFs, since models fit using "infinitely weighted logistic regression" have been shown to be equivalent to an inhomogeneous Poisson process (IPP). We generalize this result to "infinitely weighted Poisson regression", which converges to the same underlying IPP distribution. 4.Using data from Eurasian otters (Lutra lutra) and mountain goats (Oreamnos americanus), we illustrate that our models lead to valid and feasible inference. In addition, we conduct a simulation study to demonstrate the importance of including random slopes when estimating individual-and population-level habitat-selection parameters. 5.By providing coded examples using integrated nested Laplace approximations (INLA) and Template Model Builder (TMB) for Bayesian and frequentist analysis via the R packages R-INLA and glmmTMB, we hope to make efficient estimation of RSFs and SSFs with random effects accessible to anyone in the field. SSFs with individual-specific coefficients are particularly attractive since they can provide insights into movement and habitat-selection processes at fine-spatial and temporal scales, but these models had previously been very challenging to fit.

show abstract

“…Spatial models fitted by Bayesian inference using Markov Chain Monte Carlo (MCMC) simulations are currently easily available (Banerjee and Fuentes, 2012;Bivand et al, 2015;Heaton et al, 2017). Although, for very complex spatial models, the MCMC method is still time and computationally demanding (Wang et al, 2018). The method called Integrated Nested Laplace Approximations (INLA) offers a faster and more friendly approach for fitting spatial models using spectra as covariates (Poggio et al, 2016;Rue et al, 2009).…”

Section: Regression Methods To Predict Plant Trait With Hyperspectralmentioning

confidence: 99%

Tuning a statistical trade-off between spectral and spatial domains to predict plant traits with hyperspectral remote sensing

Rocha¹

View full text Add to dashboard Cite

Other components of variability related to the spatial alignment between spectra and ground references are errors in plot coordinates, upscale or downscale, distortions to departing from the nadir, among others (Manolakis et al., 2003). The discretisation of continuous domains such as spectra, space or time results in the loss of a certain amount of information (Bruce et al., 2002). The spatial resolution of a remote sensing data (pixel) or the sample The possible solutions for selecting covariates for modelling using hyperspectral data and avoid multicollinearity include: (1) extracting spectral indices that explain causally or empirically the relationship with the target plant trait based on a-priori knowledge; (2) searching a coefficient from a combination of two or more bands that is highest correlated with the plant trait (Darvishzadeh et al., 2008); (3) combining wavelengths to create latent Predictive models for plant traits are mostly selected by data rather than based on theory, and often elected among different regression techniques (James et al., 2013). If the model is assessed with the same data as was fitted, more complexity, directly means more accuracy, as the prediction error always reduces when the complexity increases (James et al., 2013). Consequently, it is improper to assess and report the accuracy of predictive models with the same data as used for selecting the final model. Predictive models require to split the data into training and testing (sub) sets to assess accuracy (Esbensen and Geladi, 2010). There are many alternatives, from splitting an independent This thesis is comprised of six chapters, of which four research chapters are submitted, and three are currently accepted as scientific articles to peerreviewed ISI journals. The general outline is indicated below. Chapter 1: the introductory chapter discusses the importance of plant traits and the role of remote sensing to monitoring and understanding the underlying process. The chapter is designed to highlight issues that need further improvement when modelling plant traits with hyperspectral data. Chapter 2: demonstrates that empirical models using hyperspectral data to predict traits are very likely to lead to significant overfitting, even when selected by commonly used robust cross-validation. A new method named Naïve Overfitting Index Selection (NOIS) was developed to quantify overfitting while selecting model complexity (tuning). The method was tested using five hyperspectral datasets and seven machine learning regression techniques. Chapter 3: shows that machine learning regressions using hyperspectral data are likely to lead to inaccurate predictions when significant autocorrelation is

show abstract

Bayesian Regression Modeling with Inla

Cited by 108 publications

References 0 publications

Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand

Bayesian spatiotemporal modeling with sliding windows to correct reporting delays for real-time dengue surveillance in Thailand

Accounting for individual-specific variation in habitat-selection studies: Efficient estimation of mixed-effects models using Bayesian or frequentist computation

Tuning a statistical trade-off between spectral and spatial domains to predict plant traits with hyperspectral remote sensing

Contact Info

Product

Resources

About