BSL: An R Package for Efficient Parameter Estimation for Simulation-Based Models via Bayesian Synthetic Likelihood

Bayesian synthetic likelihood (BSL; Price, Drovandi, Lee, and Nott 2018) is a popular method for estimating the parameter posterior distribution for complex statistical models and stochastic processes that possess a computationally intractable likelihood function. Instead of evaluating the likelihood, BSL approximates the likelihood of a judiciously chosen summary statistic of the data via model simulation and density estimation. Compared to alternative methods such as approximate Bayesian computation (ABC), BSL requires little tuning and requires less model simulations than ABC when the chosen summary statistic is high-dimensional. The original synthetic likelihood relies on a multivariate normal approximation of the intractable likelihood, where the mean and covariance are estimated by simulation. An extension of BSL considers replacing the sample covariance with a penalized covariance estimator to reduce the number of required model simulations. Further, a semi-parametric approach has been developed to relax the normality assumption. Finally, another extension of BSL aims to develop a more robust synthetic likelihood estimator while acknowledging there might be model misspecification. In this paper, we present the R package BSL that amalgamates the aforementioned methods and more into a single, easy-to-use and coherent piece of software. The package also includes several examples to illustrate use of the package and the utility of the methods

Electrical tuning of robust layered antiferromagnetism in MXene monolayer

A-type antiferromagnetism, with an in-plane ferromagnetic order and the interlayer antiferromagnetic coupling, owns inborn advantages for electrical manipulations but is naturally rare in real materials except in those artificial antiferromagnetic heterostructures. Here, a robust layered antiferromagnetism with a high N\'eel temperature is predicted in a MXene Cr$_2$CCl$_2$ monolayer, which provides an ideal platform as a magnetoelectric field effect transistor. Based on first-principles calculations, we demonstrate that an electric field can induce the band splitting between spin-up and spin-down channels. Although no net magnetization is generated, the inversion symmetry between the lower Cr layer and the upper Cr layer is broken via electronic cloud distortions. Moreover, this electric field can be replaced by a proximate ferroelectric layer for nonvolatility. The magneto-optic Kerr effect can be used to detect this magnetoelectricity, even if it is a collinear antiferromagnet with zero magnetization

Accelerating Bayesian synthetic likelihood with the graphical lasso

Simulation-based Bayesian inference methods are useful when the statistical model of interest does not possess a computationally tractable likelihood function. One such likelihood-free method is approximate Bayesian computation (ABC), which approximates the likelihood of a carefully chosen summary statistic via model simulation and nonparametric density estimation. ABC is known to suffer a curse of dimensionality with respect to the size of the summary statistic. When the model summary statistic is roughly normally distributed in regions of the parameter space of interest, Bayesian synthetic likelihood (BSL), which uses a normal likelihood approximation for a summary statistic, is a useful method that can be more computationally efficient than ABC. However, BSL requires estimation of the covariance matrix of the summary statistic for each proposed parameter, which requires a large number of simulations to estimate precisely using the sample covariance matrix when the summary statistic is high dimensional. In this article, we propose to use the graphical lasso to provide a sparse estimate of the precision matrix. This approach can estimate the covariance matrix accurately with significantly fewer model simulations. We discuss the nontrivial issue of tuning parameter choice in the context of BSL and demonstrate on several complex applications that our method, which we call BSLasso, provides significant improvements in computational efficiency whilst maintaining the ability to produce similar posterior distributions to BSL. The BSL and BSLasso methods applied to the examples of this article are implemented in the BSL package in R, which is available on the Comprehensive R Archive Network. Supplemental materials for this article are available online.</p

Association between per- and polyfluoroalkyl substances and risk of hypertension: a systematic review and meta-analysis

BackgroundExisting evidence indicates that exposure to per- and polyfluoroalkyl substances (PFASs) may increase the risk of hypertension, but the findings are inconsistent. Therefore, we aimed to explore the relationship between PFASs and hypertension through this systematic review and meta-analysis.MethodsWe searched PubMed, Embase, and the Web of Science databases for articles published in English that examined the relationship between PFASs and hypertension before 13 August 2022. The random effects model was used to aggregate the evaluation using Stata 15.0 for Windows. We also conducted subgroup analyses by region and hypertension definition. In addition, a sensitivity analysis was carried out to determine the robustness of the findings.ResultsThe meta-analysis comprised 15 studies in total with 69,949 individuals. The risk of hypertension was substantially and positively correlated with exposure to perfluorooctane sulfonate (PFOS) (OR = 1.31, 95% CI: 1.14, 1.51), perfluorooctanoic acid (PFOA) (OR = 1.16, 95% CI: 1.07, 1.26), and perfluorohexane sulfonate (PFHxS) (OR = 1.04, 95% CI: 1.00, 1.09). However, perfluorononanoic acid (PFNA) exposure and hypertension were not significantly associated (OR = 1.08, 95% CI: 0.99, 1.17).ConclusionWe evaluated the link between PFASs exposure and hypertension and discovered that higher levels of PFOS, PFOA, and PFHxS were correlated with an increased risk of hypertension. However, further high-quality population-based and pathophysiological investigations are required to shed light on the possible mechanism and demonstrate causation because of the considerable variability.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/ PROSPERO, registration number: CRD 42022358142

Regime Types, Credible Commitment Institutions, and Foreign Direct Investment: Rethinking How Autocratic Countries Attract FDI

This dissertation explores the relationship between regime types, credible commitment institutions and FDI, aiming to answer the puzzle—how do autocratic countries solve the commitment issues and attract a high level of FDI. To achieve this aim, the dissertation was designed to include two empirical chapters and two additional chapters for the introduction and the conclusion. The first empirical chapter examines the effect of regime types and property rights institutions on FDI. The empirical analysis presents a significant and surprising finding that the regime type is not what foreign firms necessarily care about, and what really matters to foreign investors are specific institutional features of the host country—in the present chapter, the effect of property rights institutions is tested. In other words, countries with sound institutions are not necessary democracies. Autocratic countries with well-established institutions to protect property rights and enforce contracts can also attract high level of FDI inflows. The second empirical chapter only focuses on autocratic countries. No longer view all autocratic countries as a single type opposing democratic countries as in the previous chapter; the great institutional differences among autocratic regimes will be witnessed and discussed. I argue that, besides the property rights institutions, some other institutional features—the power-sharing political institutions in some autocratic regimes, as well as the additional protections from international commitment institutions could help autocratic countries attract more FDI inflows. These effects can also complement property rights institutions and jointly affect FDI. Overall, the major contribution of this dissertation is that it verifies what matters for FDI inflows is not regime types but certain credible commitment institutions. The autocratic countries that can solve commitment issues by establishing strong credible commitment institutions can also attract a high level of FDI

Contributions to Bayesian synthetic likelihood

Complex statistical models pose a great challenge to practitioners because of methodological and computational difficulties. While traditional ways of running statistical inference are prohibitive for various reasons, new methods which rely only on model simulations have received increasing attention. This thesis develops novel simulation-based statistical inference methods that are both computationally efficient and robust allowing them to perform well on a wide variety of applications. We also provide statistical software to facilitate timely analyses

BSL: Bayesian Synthetic Likelihood

Bayesian synthetic likelihood (BSL, Price et al. (2018) ) is an alternative to standard, non-parametric approximate Bayesian computation (ABC). BSL assumes a multivariate normal distribution for the summary statistic likelihood and it is suitable when the distribution of the model summary statistics is sufficiently regular. This package provides a Metropolis Hastings Markov chain Monte Carlo implementation of four methods (BSL, uBSL, semiBSL and BSLmisspec) and two shrinkage estimators (graphical lasso and Warton's estimator). uBSL (Price et al. (2018) ) uses an unbiased estimator to the normal density. A semi-parametric version of BSL (semiBSL, An et al. (2018) ) is more robust to non-normal summary statistics. BSLmisspec (Frazier et al. 2019 ) estimates the Gaussian synthetic likelihood whilst acknowledging that there may be incompatibility between the model and the observed summary statistic. Shrinkage estimation can help to decrease the number of model simulations when the dimension of the summary statistic is high (e.g., BSLasso, An et al. (2019) ). Extensions to this package are planned. For a journal article describing how to use this package, see An et al. (2022)