Alex Stringer
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View article: Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature
Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature Open
Naomi is a spatial evidence synthesis model used to produce district-level HIV epidemic indicators in sub-Saharan Africa. Multiple outcomes of policy interest, including HIV prevalence, HIV incidence, and antiretroviral therapy treatment c…
View article: Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature
Fast approximate Bayesian inference of HIV indicators using PCA adaptive Gauss-Hermite quadrature Open
Naomi is a spatial evidence synthesis model used to produce district-level HIV epidemic indicators in sub-Saharan Africa. Multiple outcomes of policy interest, including HIV prevalence, HIV incidence, and antiretroviral therapy treatment c…
View article: Estimating associations between cumulative exposure and health via generalized distributed lag non-linear models using penalized splines
Estimating associations between cumulative exposure and health via generalized distributed lag non-linear models using penalized splines Open
Quantifying associations between short-term exposure to ambient air pollution and health outcomes is an important public health priority. Many studies have investigated the association considering delayed effects within the past few days. …
View article: Candidate Dark Galaxy-2: Validation and Analysis of an Almost Dark Galaxy in the Perseus Cluster
Candidate Dark Galaxy-2: Validation and Analysis of an Almost Dark Galaxy in the Perseus Cluster Open
Candidate Dark Galaxy-2 (CDG-2) is a potential dark galaxy consisting of four globular clusters (GCs) in the Perseus cluster, first identified in D. Li et al. through a sophisticated statistical method. The method searched for overdensitie…
View article: Estimating Associations Between Cumulative Exposure and Health via Generalized Distributed Lag Non-Linear Models using Penalized Splines
Estimating Associations Between Cumulative Exposure and Health via Generalized Distributed Lag Non-Linear Models using Penalized Splines Open
Quantifying associations between short-term exposure to ambient air pollution and health outcomes is an important public health priority. Many studies have investigated the association considering delayed effects within the past few days. …
View article: Inference for generalized additive mixed models via penalized marginal likelihood
Inference for generalized additive mixed models via penalized marginal likelihood Open
The Laplace approximation is sometimes not sufficiently accurate for smoothing parameter estimation in generalized additive mixed models. A novel estimation strategy is proposed that solves this problem and leads to estimates exhibiting th…
View article: Case-crossover designs and overdispersion with application to air pollution epidemiology
Case-crossover designs and overdispersion with application to air pollution epidemiology Open
Over the last three decades, case-crossover designs have found many applications in health sciences, especially in air pollution epidemiology. They are typically used, in combination with partial likelihood techniques, to define a conditio…
View article: Semi-parametric benchmark dose analysis with monotone additive models
Semi-parametric benchmark dose analysis with monotone additive models Open
Benchmark dose analysis aims to estimate the level of exposure to a toxin associated with a clinically significant adverse outcome and quantifies uncertainty using the lower limit of a confidence interval for this level. We develop a novel…
View article: Repository for the paper "Poisson Cluster Process for Detecting Ultra-Diffuse Galaxies."
Repository for the paper "Poisson Cluster Process for Detecting Ultra-Diffuse Galaxies." Open
Repository for data, figures, and reproducible code for the paper "Poisson Cluster Process Models for Detecting Ultra-Diffuse Galaxies".
View article: Case-crossover designs and overdispersion with application in air pollution epidemiology
Case-crossover designs and overdispersion with application in air pollution epidemiology Open
Over the last three decades, case-crossover designs have found many applications in health sciences, especially in air pollution epidemiology. They are typically used, in combination with partial likelihood techniques, to define a conditio…
View article: Model-Based Smoothing with Integrated Wiener Processes and Overlapping Splines
Model-Based Smoothing with Integrated Wiener Processes and Overlapping Splines Open
In many applications that involve the inference of an unknown smooth function, the inference of its derivatives is also important. To make joint inferences of the function and its derivatives, a class of Gaussian processes called pth order…
View article: Semi-parametric Benchmark Dose Analysis with Monotone Additive Models
Semi-parametric Benchmark Dose Analysis with Monotone Additive Models Open
Benchmark dose analysis aims to estimate the level of exposure to a toxin that results in a clinically-significant adverse outcome and quantifies uncertainty using the lower limit of a confidence interval for this level. We develop a novel…
View article: Exact Gradient Evaluation for Adaptive Quadrature Approximate Marginal Likelihood in Mixed Models for Grouped Data
Exact Gradient Evaluation for Adaptive Quadrature Approximate Marginal Likelihood in Mixed Models for Grouped Data Open
A method is introduced for approximate marginal likelihood inference via adaptive Gaussian quadrature in mixed models with a single grouping factor. The core technical contribution is an algorithm for computing the exact gradient of the ap…
View article: Marginal additive models for population‐averaged inference in longitudinal and cluster‐correlated data
Marginal additive models for population‐averaged inference in longitudinal and cluster‐correlated data Open
We propose a novel marginal additive model (MAM) for modeling cluster‐correlated data with nonlinear population‐averaged associations. The proposed MAM is a unified framework for estimation and uncertainty quantification of a marginal mean…
View article: Identifiability constraints in generalized additive models
Identifiability constraints in generalized additive models Open
Identifiability constraints are necessary for parameter estimation when fitting models with nonlinear covariate associations. The choice of constraint affects standard errors of the estimated curve. Centring constraints are often applied b…
View article: Model-based Smoothing with Integrated Wiener Processes and Overlapping Splines
Model-based Smoothing with Integrated Wiener Processes and Overlapping Splines Open
In many applications that involve the inference of an unknown smooth function, the inference of its derivatives will often be just as important as that of the function itself. To make joint inferences of the function and its derivatives, a…
View article: Model-based Smoothing with Integrated Wiener Processes and Overlapping Splines
Model-based Smoothing with Integrated Wiener Processes and Overlapping Splines Open
In many applications that involve the inference of an unknown smooth function, the inference of its derivatives is also important. To make joint inferences of the function and its derivatives, a class of Gaussian processes called pth order…
View article: Bayesian inference for Cox proportional hazard models with partial likelihoods, nonlinear covariate effects and correlated observations
Bayesian inference for Cox proportional hazard models with partial likelihoods, nonlinear covariate effects and correlated observations Open
We propose a flexible and scalable approximate Bayesian inference methodology for the Cox Proportional Hazards model with partial likelihood. The model we consider includes nonlinear covariate effects and correlated survival times. The pro…
View article: On the Tightness of the Laplace Approximation for Statistical Inference
On the Tightness of the Laplace Approximation for Statistical Inference Open
Laplace's method is used to approximate intractable integrals in a statistical problems. The relative error rate of the approximation is not worse than $O_p(n^{-1})$. We provide the first statistical lower bounds showing that the $n^{-1}$ …
View article: Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models
Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models Open
We define a novel class of additive models, called Extended Latent Gaussian\nModels, that allow for a wide range of response distributions and flexible\nrelationships between the additive predictor and mean response. The new class\ncovers …
View article: Flexible Marginal Models for Dependent Data
Flexible Marginal Models for Dependent Data Open
Models for dependent data are distinguished by their targets of inference. Marginal models are useful when interest lies in quantifying associations averaged across a population of clusters. When the functional form of a covariate-outcome …
View article: Asymptotics of numerical integration for two-level mixed models
Asymptotics of numerical integration for two-level mixed models Open
We study mixed models with a single grouping factor, where inference about unknown parameters requires optimizing a marginal likelihood defined by an intractable integral. Low-dimensional numerical integration techniques are regularly used…
View article: Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models
Fast, Scalable Approximations to Posterior Distributions in Extended Latent Gaussian Models Open
We define a novel class of additive models, called Extended Latent Gaussian Models, that allow for a wide range of response distributions and flexible relationships between the additive predictor and mean response. The new class covers a b…
View article: Stochastic Convergence Rates and Applications of Adaptive Quadrature in Bayesian Inference
Stochastic Convergence Rates and Applications of Adaptive Quadrature in Bayesian Inference Open
We provide the first stochastic convergence rates for a family of adaptive quadrature rules used to normalize the posterior distribution in Bayesian models. Our results apply to the uniform relative error in the approximate posterior densi…
View article: Stochastic Convergence Rates and Applications of Adaptive Quadrature in Bayesian Inference
Stochastic Convergence Rates and Applications of Adaptive Quadrature in Bayesian Inference Open
We provide the first stochastic convergence rates for a family of adaptive quadrature rules used to normalize the posterior distribution in Bayesian models. Our results apply to the uniform relative error in the approximate posterior densi…
View article: Implementing Adaptive Quadrature for Bayesian Inference: the aghq Package
Implementing Adaptive Quadrature for Bayesian Inference: the aghq Package Open
I introduce the aghq package for implementing Bayesian inference using adaptive Gauss-Hermite quadrature. I describe the method and software, and illustrate its use in several challenging low- and high-dimensional examples. Specifically, I…
View article: Implementing Approximate Bayesian Inference using Adaptive Quadrature: the aghq Package
Implementing Approximate Bayesian Inference using Adaptive Quadrature: the aghq Package Open
The aghq package for implementing approximate Bayesian inference using adaptive quadrature is introduced. The method and software are described, and use of the package in making approximate Bayesian inferences in several challenging low- a…