| 1. |
- Bodnar, Olha, senior lecturer, 1979-, et al.
(författare)
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Bayesian model selection : Application to the adjustment of fundamental physical constants
- 2023
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Ingår i: Annals of Applied Statistics. - : Institute of Mathematical Statistics. - 1932-6157 .- 1941-7330. ; 17:3, s. 2118-2138
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Tidskriftsartikel (refereegranskat)abstract
- A method originally suggested by Raymond Birge, using what came to be known as the Birge ratio, has been widely used in metrology and physics for the adjustment of fundamental physical constants, particularly in the periodic reevaluation carried out by the Task Group on Fundamental Physical Constants of CODATA (the Committee on Data of the International Science Council). The method involves increasing the reported uncertainties by a multiplicative factor large enough to make the measurement results mutually consistent. An alternative approach, predominant in the meta-analysis of medical studies, involves inflating the reported uncertainties by combining them, using the root sum of squares, with a sufficiently large constant (often dubbed dark uncertainty) that is estimated from the data.In this contribution we establish a connection between the method based on the Birge ratio and the location-scale model, which allows one to combine the results of various studies, while the additive adjustment is reviewed in the usual context of random-effects models. Framing these alternative approaches as statistical models facilitates a quantitative comparison of them using statistical tools for model comparison. The intrinsic Bayes factor (IBF) is derived for the Berger and Bernardo reference prior, and then it is used to select a model for a set of measurements of the Newtonian constant of gravitation (“Big G”) to estimate a consensus value for this constant and to evaluate the associated uncertainty. Our empirical findings support the method based on the Birge ratio. The same conclusion is reached when the IBF corresponding to the Jeffreys prior is used and also when the comparison is based on the Akaike information criterion (AIC). Finally, the results of a simulation study indicate that the suggested procedure for model selection provides clear guidance, even when the data comprise only a small number of measurements.
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| 2. |
- Bodnar, Olha, et al.
(författare)
-
Bayesian model selection : Application to the adjustment of fundamental physical constants
- 2023
-
Ingår i: Annals of Applied Statistics. - : Institute of Mathematical Statistics. - 1932-6157 .- 1941-7330. ; 17:3, s. 2118-2138
-
Tidskriftsartikel (refereegranskat)abstract
- A method originally suggested by Raymond Birge, using what came to be known as the Birge ratio, has been widely used in metrology and physics for the adjustment of fundamental physical constants, particularly in the periodic reevaluation carried out by the Task Group on Fundamental Physical Constants of CODATA (the Committee on Data of the International Science Council). The method involves increasing the reported uncertainties by a multiplicative factor large enough to make the measurement results mutually consistent. An alternative approach, predominant in the meta-analysis of medical studies, involves inflating the reported uncertainties by combining them, using the root sum of squares, with a sufficiently large constant (often dubbed dark uncertainty) that is estimated from the data.In this contribution we establish a connection between the method based on the Birge ratio and the location-scale model, which allows one to combine the results of various studies, while the additive adjustment is reviewed in the usual context of random-effects models. Framing these alternative approaches as statistical models facilitates a quantitative comparison of them using statistical tools for model comparison. The intrinsic Bayes factor (IBF) is derived for the Berger and Bernardo reference prior, and then it is used to select a model for a set of measurements of the Newtonian constant of gravitation ("Big G") to estimate a consensus value for this constant and to evaluate the associated uncertainty. Our empirical findings support the method based on the Birge ratio. The same conclusion is reached when the IBF corresponding to the Jeffreys prior is used and also when the comparison is based on the Akaike information criterion (AIC). Finally, the results of a simulation study indicate that the suggested procedure for model selection provides clear guidance, even when the data comprise only a small number of measurements.
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| 3. |
- Bolin, David, 1983, et al.
(författare)
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Spatial models generated by nested stochastic partial differential equations, with an application to global ozone mapping
- 2011
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Ingår i: Annals of Applied Statistics. - 1932-6157 .- 1941-7330. ; 5:1, s. 523-550
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Tidskriftsartikel (refereegranskat)abstract
- A new class of stochastic field models is constructed using nested stochastic partial differential equations (SPDEs). The model class is computationally efficient, applicable to data on general smooth manifolds, and includes both the Gaussian Matérn fields and a wide family of fields with oscillating covariance functions. Nonstationary covariance models are obtained by spatially varying the parameters in the SPDEs, and the model parameters are estimated using direct numerical optimization, which is more efficient than standard Markov Chain Monte Carlo procedures. The model class is used to estimate daily ozone maps using a large data set of spatially irregular global total column ozone data. © Institute of Mathematical Statistics, 2011.
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| 4. |
- Lindqvist, Roland
(författare)
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A BAYESIAN APPROACH TO THE EVALUATION OF RISK-BASED MICROBIOLOGICAL CRITERIA FOR CAMPYLOBACTER IN BROILER MEAT
- 2015
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Ingår i: Annals of Applied Statistics. - 1932-6157 .- 1941-7330. ; 9, s. 1415-1432
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Tidskriftsartikel (refereegranskat)abstract
- Shifting from traditional hazard-based food safety management toward risk-based management requires statistical methods for evaluating intermediate targets in food production, such as microbiological criteria (MC), in terms of their effects on human risk of illness. A fully risk-based evaluation of MC involves several uncertainties that are related to both the underlying Quantitative Microbiological Risk Assessment (QMRA) model and the production-specific sample data on the prevalence and concentrations of microbes in production batches. We used Bayesian modeling for statistical inference and evidence synthesis of two sample data sets. Thus, parameter uncertainty was represented by a joint posterior distribution, which we then used to predict the risk and to evaluate the criteria for acceptance of production batches. We also applied the Bayesian model to compare alternative criteria, accounting for the statistical uncertainty of parameters, conditional on the data sets. Comparison of the posterior mean relative risk, E(RR vertical bar data) = E(P(illness vertical bar criterion is met)/ P (illness)vertical bar data), and relative posterior risk, RPR = P(illness vertical bar data, criterion is met)/ P (illness vertical bar data), showed very similar results, but computing is more efficient for RPR. Based on the sample data, together with the QMRA model, one could achieve a relative risk of 0.4 by insisting that the default criterion be fulfilled for acceptance of each batch.
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| 5. |
- Nalenz, Malte, et al.
(författare)
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TREE ENSEMBLES WITH RULE STRUCTURED HORSESHOE REGULARIZATION
- 2018
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Ingår i: Annals of Applied Statistics. - : INST MATHEMATICAL STATISTICS. - 1932-6157 .- 1941-7330. ; 12:4, s. 2379-2408
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Tidskriftsartikel (refereegranskat)abstract
- We propose a new Bayesian model for flexible nonlinear regression and classification using tree ensembles. The model is based on the RuleFit approach in Friedman and Popescu [Ann. Appl. Stat. 2 (2008) 916-954] where rules from decision trees and linear terms are used in a Ll -regularized regression. We modify RuleFit by replacing the L1-regularization by a horseshoe prior, which is well known to give aggressive shrinkage of noise predictors while leaving the important signal essentially untouched. This is especially important when a large number of rules are used as predictors as many of them only contribute noise. Our horseshoe prior has an additional hierarchical layer that applies more shrinkage a priori to rules with a large number of splits, and to rules that are only satisfied by a few observations. The aggressive noise shrinkage of our prior also makes it possible to complement the rules from boosting in RuleFit with an additional set of trees from Random Forest, which brings a desirable diversity to the ensemble. We sample from the posterior distribution using a very efficient and easily implemented Gibbs sampler. The new model is shown to outperform state-of-the-art methods like RuleFit, BART and Random Forest on 16 datasets. The model and its interpretation is demonstrated on the well known Boston housing data, and on gene expression data for cancer classification. The posterior sampling, prediction and graphical tools for interpreting the model results are implemented in a publicly available R package.
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| 6. |
- Olives, Casey, et al.
(författare)
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Reduced-rank spatio-temporal modeling of air pollution concentrations in the Multi-Ethnic Study of Atherosclerosis and Air Pollution
- 2014
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Ingår i: Annals of Applied Statistics. - 1932-6157. ; 8:4, s. 2509-2537
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Tidskriftsartikel (refereegranskat)abstract
- There is growing evidence in the epidemiologic literature of the relationship between air pollution and adverse health outcomes. Prediction of individual air pollution exposure in the Environmental Protection Agency (EPA) funded Multi-Ethnic Study of Atheroscelerosis and Air Pollution (MESA Air) study relies on a exible spatio-temporal prediction model that integrates land-use regression with kriging to account for spatial dependence in pollutant concentrations. Temporal variability is captured using temporal trends estimated via modified singular value decomposition and temporally varying spatial residuals. This model utilizes monitoring data from existing regulatory networks and supplementary MESA Air monitoring data to predict concentrations for individual cohort members. In general, spatio-temporal models are limited in their efficacy for large datasets due to computational intractability. We develop reduced-rank versions of the MESA Air spatio-temporal model. To do so, we apply low-rank kriging to account for spatial variation in the mean process and discuss the limitations of this approach. As an alternative, we represent spatial variation using thin plate regression splines. We compare the performance of the outlined models using EPA and MESA Air monitoring data for predicting concentrations of oxides of nitrogen (NOx) - a pollutant of primary interest in MESA Air - in the Los Angeles metropolitan area via cross-validated R2. Our findings suggest that use of reduced-rank models can improve computational eciency in certain cases. Low-rank kriging and thin plate regression splines were competitive across the formulations considered, although TPRS appeared to be more robust in some settings.
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| 7. |
- Reitan, Trond, et al.
(författare)
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Phenotypic evolution studied by layered stochastic differential equations
- 2012
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Ingår i: Annals of Applied Statistics. - 1932-6157 .- 1941-7330. ; 6:4, s. 1531-1551
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Tidskriftsartikel (refereegranskat)abstract
- Time series of cell size evolution in unicellular marine algae (division Haptophyta; Coccolithus lineage), covering 57 million years, are studied by a system of linear stochastic differential equations of hierarchical structure.The data consists of size measurements of fossilized calcite platelets (coccoliths) that cover the living cell, found in deep-sea sediment cores from six sites in the world oceans and dated to irregularly points in time. To accommodate biological theory of populations tracking their fitness optima, and to allow potentially interpretable correlations in time and space, the model framework allows for an upper layer of partially observed site-specific population means, a layer of site-specific theoretical fitness optima and a bottom layer representing environmental and ecological processes. While the modeled process has many components, it is Gaussian and analytically tractable. A total of 710 model specifications within this framework are compared and inference is drawn with respect to model structure, evolutionary speed and the effect of global temperature.
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| 8. |
- Soneson, Charlotte, et al.
(författare)
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A method for visual identification of small sample subgroups and potential biomarkers
- 2011
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Ingår i: Annals of Applied Statistics. - 1932-6157. ; 5:3, s. 2131-2149
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Tidskriftsartikel (refereegranskat)abstract
- In order to find previously unknown subgroups in biomedical data and generate testable hypotheses, visually guided exploratory analysis can be of tremendous importance. In this paper we propose a new dissimilarity measure that can be used within the Multidimensional Scaling framework to obtain a joint low-dimensional representation of both the samples and variables of a multivariate data set, thereby providing an alternative to conventional biplots. In comparison with biplots, the representations obtained by our approach are particularly useful for exploratory analysis of data sets where there are small groups of variables sharing unusually high or low values for a small group of samples.
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| 9. |
- Wu, Guohui, et al.
(författare)
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A Bayesian semiparametric Jolly–Seber model with individual heterogeneity : An application to migratory mallards at stopover
- 2021
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Ingår i: Annals of Applied Statistics. - : Institute of Mathematical Statistics - IMS. - 1932-6157 .- 1941-7330. ; 15:2, s. 813-830
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Tidskriftsartikel (refereegranskat)abstract
- We propose a Bayesian hierarchical Jolly-Seber model that can accommodate a semiparametric functional relationship between external covariates and capture probabilities, individual heterogeneity in departure due to an internal time-varying covariate and the dependence of arrival time on external covariates. Modelwise, we consider a stochastic process to characterize the evolution of the partially observable internal covariate that is linked to departure probabilities. Computationally, we develop a well-tailored Markov chain Monte Carlo algorithm that is free of tuning through data augmentation. Inferentially, our model allows us to make inference about stopover duration and population sizes, the impacts of various covariates on departure and arrival time and to identify flexible yet data-driven functional relationships between external covariates and capture probabilities. We demonstrate the effectiveness of our model through a motivating dataset collected for studying the migration of mallards (Arias platyrhynchos) in Sweden.
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| 10. |
- Ylitalo, A. K., et al.
(författare)
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What we look at in paintings: a Comparsion between Experienced and Inexperienced Art Viewers
- 2016
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Ingår i: Annals of Applied Statistics. - : Institute of Mathematical Statistics. - 1932-6157. ; 10:2, s. 549-574
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Tidskriftsartikel (refereegranskat)abstract
- How do people look at art? Are there any differences between how experienced and inexperienced art viewers look at a painting? We approach these questions by analyzing and modeling eye movement data from a cognitive art research experiment, where the eye movements of twenty test subjects, ten experienced and ten inexperienced art viewers, were recorded while they were looking at paintings. Eye movements consist of stops of the gaze as well as jumps between the stops. Hence, the observed gaze stop locations can be thought of as a spatial point pattern, which can be modeled by a spatio-temporal point process. We introduce some statistical tools to analyze the spatio-temporal eye movement data, and compare the eye movements of experienced and inexperienced art viewers. In addition, we develop a stochastic model, which is rather simple but fits quite well to the eye movement data, to further investigate the differences between the two groups through functional summary statistics.
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