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Functional Lagged Regression with Sparse Noisy ObservationsMay 17 2019A (lagged) time series regression model involves the regression of scalar response time series on a time series of regressors that consists of a sequence of random functions (curves), also known as a functional time series. In practice, the underlying ... More
Model interpretation through lower-dimensional posterior summarizationMay 17 2019Nonparametric regression models have recently surged in their power and popularity, accompanying the trend of increasing dataset size and complexity. While these models have proven their predictive ability in empirical settings, they are often difficult ... More
Analytic Basis Expansions for Functional SnippetsMay 16 2019Estimation of mean and covariance functions is fundamental for functional data analysis. While this topic has been studied extensively in the literature, a key assumption is that there are enough data in the domain of interest to estimate both the mean ... More
More current with less particles due to power-law hoppingMay 16 2019We show that the simple model of an ordered one-dimensional non-interacting system with power-law hopping has extremely interesting transport properties. We restrict ourselves to the case where the power-law decay exponent $\alpha>1$, so that the thermodynamic ... More
The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High DimensionsMay 16 2019Discovering interaction effects on a response of interest is a fundamental problem faced in biology, medicine, economics, and many other scientific disciplines. In theory, Bayesian methods for discovering pairwise interactions enjoy many benefits such ... More
Non-Asymptotic Inference in a Class of Optimization ProblemsMay 16 2019This paper describes a method for carrying out non-asymptotic inference on partially identified parameters that are solutions to a class of optimization problems. The optimization problems arise in applications in which grouped data are used for estimation ... More
Moment-based Estimation of Mixtures of Regression ModelsMay 15 2019Finite mixtures of regression models provide a flexible modeling framework for many phenomena. Using moment-based estimation of the regression parameters, we develop unbiased estimators with a minimum of assumptions on the mixture components. In particular, ... More
Irreversible aspects of nanoscale frictionMay 15 2019Developing the non-equilibrium thermodynamics of friction is required for systematic design of low friction surfaces for a broad range of technological applications. Intuitively, the thermodynamic work done by a material sliding along a surface is expected ... More
mRSC: Multi-dimensional Robust Synthetic ControlMay 15 2019When evaluating the impact of a policy on a metric of interest, it may not be possible to conduct a randomized control trial. In settings where only observational data is available, Synthetic Control (SC) methods provide a popular data-driven approach ... More
The Statistical Finite Element MethodMay 15 2019The finite element method (FEM) is one of the great triumphs of modern day applied mathematics, numerical analysis and algorithm development. Engineering and the sciences benefit from the ability to simulate complex systems with FEM. At the same time ... More
False Discovery Rates to Detect Signals from Incomplete Spatially Aggregated DataMay 15 2019There are a number of ways to test for the absence/presence of a spatial signal in a completely observed fine-resolution image. One of these is a powerful nonparametric procedure called EFDR (Enhanced False Discovery Rate). A drawback of EFDR is that ... More
Simultaneous Inference for Pairwise Graphical Models with Generalized Score MatchingMay 15 2019Probabilistic graphical models provide a flexible yet parsimonious framework for modeling dependencies among nodes in networks. There is a vast literature on parameter estimation and consistent model selection for graphical models. However, in many of ... More
Robust change point tests by bounded transformationsMay 15 2019Classical moment based change point tests like the cusum test are very powerful in case of Gaussian time series with one change point but behave poorly under heavy tailed distributions and corrupted data. A new class of robust change point tests based ... More
Iterative Alpha Expansion for estimating gradient-sparse signals from linear measurementsMay 15 2019We consider estimating a piecewise-constant image, or a gradient-sparse signal on a general graph, from noisy linear measurements. We propose and study an iterative algorithm to minimize a penalized least-squares objective, with a penalty given by the ... More
A New Estimation Algorithm for Box-Cox Transformation Cure Rate Model and Comparison With EM AlgorithmMay 15 2019In this paper, we develop a new estimation procedure based on the non-linear conjugate gradient (NCG) algorithm for the Box-Cox transformation cure rate model. We compare the performance of the NCG algorithm with the well-known expectation maximization ... More
Simultaneous Inference Under the Vacuous Orientation AssumptionMay 15 2019I propose a novel approach to simultaneous inference that alleviates the need to specify a correlational structure among marginal errors. The vacuous orientation assumption retains what the normal i.i.d. assumption implies about the distribution of error ... More
Approximate Bayesian computation via the energy statisticMay 14 2019Approximate Bayesian computation (ABC) has become an essential part of the Bayesian toolbox for addressing problems in which the likelihood is prohibitively expensive or entirely unknown, making it intractable. ABC defines a quasi-posterior by comparing ... More
Fast and robust model selection based on ranksMay 14 2019We consider the problem of identifying important predictors in large data bases, where the relationship between the response variable and the explanatory variables is specified by the general single index model, with unknown link function and unknown ... More
Shifting attention to old age: Detecting mortality deceleration using focused model selectionMay 14 2019The decrease in the increase in death rates at old ages is a phenomenon that has repeatedly been discussed in demographic research. While mortality deceleration can be explained in the gamma-Gompertz model as an effect of selection in heterogeneous populations, ... More
Estimating Bayes factors from minimal ANOVA summaries for repeated-measures designsMay 14 2019In this paper, we develop a formula for estimating Bayes factors from repeated measures ANOVA designs. The formula, which requires knowing only minimal information about the ANOVA (e.g., the F -statistic), is based on the BIC approximation of the Bayes ... More
Convolutional Poisson Gamma Belief NetworkMay 14 2019For text analysis, one often resorts to a lossy representation that either completely ignores word order or embeds each word as a low-dimensional dense feature vector. In this paper, we propose convolutional Poisson factor analysis (CPFA) that directly ... More
Experimental Evaluation of Individualized Treatment RulesMay 14 2019In recent years, the increasing availability of individual-level data and the advancement of machine learning algorithms have led to the explosion of methodological development for finding optimal individualized treatment rules (ITRs). These new tools ... More
Transient trapping into metastable states in systems with competing ordersMay 14 2019The quench dynamics of a system involving two competing orders is investigated using a Ginzburg-Landau theory with relaxational dynamics. Modest differences in relaxation rates of the competing orders are found lead to post quench evolution into the local ... More
Scaling Bayesian Probabilistic Record Linkage with Post-Hoc Blocking: An Application to the California Great RegistersMay 14 2019Probabilistic record linkage (PRL) is the process of determining which records in two databases correspond to the same underlying entity in the absence of a unique identifier. Bayesian solutions to this problem provide a powerful mechanism for propagating ... More
Variational approximations using Fisher divergenceMay 13 2019Modern applications of Bayesian inference involve models that are sufficiently complex that the corresponding posterior distributions are intractable and must be approximated. The most common approximation is based on Markov chain Monte Carlo, but these ... More
Multiple imputation using dimension reduction techniques for high-dimensional dataMay 13 2019Missing data present challenges in data analysis. Naive analyses such as complete-case and available-case analysis may introduce bias and loss of efficiency, and produce unreliable results. Multiple imputation (MI) is one of the most widely used methods ... More
Hierarchical approaches for flexible and interpretable binary regression modelsMay 13 2019Binary regression models are ubiquitous in virtually every scientific field. Frequently, traditional generalized linear models fail to capture the variability in the probability surface that gives rise to the binary observations and novel methodology ... More
Dynamical purification phase transition induced by quantum measurementsMay 13 2019Continuously monitoring the environment of an open quantum many-body system reduces the entropy of (purifies) the reduced density matrix of the system, conditional on the outcome of the measurements. We show that a balanced competition between measurements ... More
Virial theorem, boundary conditions, and pressure for massless Dirac electronsMay 13 2019The virial and the Hellmann--Feynman theorems for massless Dirac electrons in a solid are derived and analyzed using generalized continuity equations and scaling transformations. Boundary conditions imposed on the wave function in a finite sample are ... More
Partially Specified Space Time Autoregressive Model with Artificial Neural NetworkMay 13 2019The space time autoregressive model has been widely applied in science, in areas such as economics, public finance, political science, agricultural economics, environmental studies and transportation analyses. The classical space time autoregressive model ... More
Asymmetric tail dependence modeling, with application to cryptocurrency market dataMay 13 2019Since the inception of Bitcoin in 2008, cryptocurrencies have played an increasing role in the world of e-commerce, but the recent turbulence in the cryptocurrency market in 2018 has raised some concerns about their stability and associated risks. For ... More
Fast Parameter Inference in a Biomechanical Model of the Left Ventricle using Statistical EmulationMay 13 2019A central problem in biomechanical studies of personalised human left ventricular (LV) modelling is estimating the material properties and biophysical parameters from in-vivo clinical measurements in a time frame suitable for use within a clinic. Understanding ... More
A Spatial Concordance Correlation Coefficient with an Application to Image AnalysisMay 13 2019In this work we define a spatial concordance coefficient for second-order stationary processes. This problem has been widely addressed in a non-spatial context, but here we consider a coefficient that for a fixed spatial lag allows one to compare two ... More
Note on Thompson sampling for large decision problemsMay 12 2019There is increasing interest in using streaming data to inform decision making across a wide range of application domains including mobile health, food safety, security, and resource management. A decision support system formalizes online decision making ... More
Functional Correlations in the Pursuit of Performance Assessment of ClassifiersMay 12 2019In statistical classification, machine learning, social and other sciences, a number of measures of association have been developed and used for assessing and comparing individual classifiers, raters, and their groups. Among the measures, we find the ... More
Structural Equation Modeling using Computation GraphsMay 11 2019Structural equation modeling (SEM) is evolving as available data is becoming more complex, reaching the limits of what traditional estimation approaches can achieve. As SEM expands to ever larger, more complex applications, the estimation challenge grows ... More
Prediction and outlier detection: a distribution-free prediction set with a balanced objectiveMay 10 2019We consider the multi-class classification problem when the training data and the out-of-sample test data may have different distributions and propose a method called BCOPS (balanced and conformal optimized prediction set) that constructs a prediction ... More
Prediction and outlier detection: a distribution-free prediction set with a balanced objectiveMay 10 2019May 14 2019We consider the multi-class classification problem when the training data and the out-of-sample test data may have different distributions and propose a method called BCOPS (balanced and conformal optimized prediction set) that constructs a prediction ... More
Statistical inference with anchored Bayesian mixture of regressions models: A case study analysis of allometric dataMay 10 2019We present a case study in which we use a mixture of regressions model to improve on an ill-fitting simple linear regression model relating log brain mass to log body mass for 100 placental mammalian species. The slope of this regression model is of particular ... More
Hubbard pair cluster with elastic interactions. Studies of thermal expansion, magnetostriction and electrostrictionMay 10 2019The pair cluster is studied within the framework of the extended Hubbard model and the grand canonical ensemble. The elastic interatomic interactions and thermal vibrational energy of the atoms are taken into account. The total grand potential is constructed, ... More
Path sampling for collapse rates of metastable magnetic skyrmions and direct comparison with transition state theoryMay 10 2019We perform a direct comparison between transition state theory and forward flux sampling as a means to compute collapse rates of metastable magnetic skyrmions. We show that a good agreement is obtained between the two methods. We report variations of ... More
Extreme events evaluation using CRPS distributionsMay 10 2019Verification of ensemble forecasts for extreme events remains a challenging question. The general public as well as the media naturely pay particular attention on extreme events and conclude about the global predictive performance of ensembles, which ... More
Inverse optimal transportMay 10 2019Discrete optimal transportation problems arise in various contexts in engineering, the sciences and the social sciences. Often the underlying cost criterion is unknown, or only partly known, and the observed optimal solutions are corrupted by noise. In ... More
Duality and topological pumping on entanglement and disorderMay 09 2019Topological pumping and duality transformations are paradigmatic concepts in condensed matter and statistical mechanics. In this letter, we integrate them within a single scheme: we demonstrate how dualities enable us to perform topological pumping of ... More
Entropy Production in Open Systems: The Predominant Role of Intra-Environment CorrelationsMay 09 2019May 13 2019We show that the entropy production in small open systems coupled to environments made of extended baths is predominantly caused by the displacement of the environment from equilibrium rather than, as often assumed, the mutual information between the ... More
Approximate Bayesian computation with the Wasserstein distanceMay 09 2019A growing number of generative statistical models do not permit the numerical evaluation of their likelihood functions. Approximate Bayesian computation (ABC) has become a popular approach to overcome this issue, in which one simulates synthetic data ... More
Stein Point Markov Chain Monte CarloMay 09 2019An important task in machine learning and statistics is the approximation of a probability measure by an empirical measure supported on a discrete point set. Stein Points are a class of algorithms for this task, which proceed by sequentially minimising ... More
Conformal prediction for exponential families and generalized linear modelsMay 09 2019Conformal prediction methods construct prediction regions for iid data that are valid in finite samples. Distribution-free conformal prediction methods have been proposed for regression. Generalized linear models (GLMs) are a widely used class of regression ... More
Comparison Between Bayesian and Frequentist Tail Probability EstimatesMay 09 2019In this paper, we investigate the reasons that the Bayesian estimator of the tail probability is always higher than the frequentist estimator. Sufficient conditions for this phenomenon are established both by using Jensen's Inequality and by looking at ... More
Asymptotic laws for upper and strong record values in the extreme domain of attraction and beyondMay 08 2019Asymptotic laws of records values have usually been investigated as limits in type. In this paper, we use functional representations of the tail of cumulative distribution functions in the extreme value domain of attraction to directly establish asymptotic ... More
Asymptotic laws for upper and strong record values in the extreme domain of attraction and beyondMay 08 2019May 10 2019Asymptotic laws of records values have usually been investigated as limits in type. In this paper, we use functional representations of the tail of cumulative distribution functions in the extreme value domain of attraction to directly establish asymptotic ... More
Optimal Rerandomization via a Criterion that Provides Insurance Against Failed ExperimentsMay 08 2019We present an optimized rerandomization design procedure for a non-sequential treatment-control experiment. Randomized experiments are the gold standard for finding causal effects in nature. But sometimes random assignments result in unequal partitions ... More
On the representation of speech and musicMay 08 2019In most automatic speech recognition (ASR) systems, the audio signal is processed to produce a time series of sensor measurements (e.g., filterbank outputs). This time series encodes semantic information in a speaker-dependent way. An earlier paper showed ... More
Conformalized Quantile RegressionMay 08 2019Conformal prediction is a technique for constructing prediction intervals that attain valid coverage in finite samples, without making distributional assumptions. Despite this appeal, existing conformal methods can be unnecessarily conservative because ... More
Prediction Model for the Africa Cup of Nations 2019 via Nested Poisson RegressionMay 08 2019This article is devoted to the forecast of the Africa Cup of Nations 2019 football tournament. It is based on a Poisson regression model that includes the Elo points of the participating teams as covariates and incorporates differences of team-specific ... More
Quantifying Triadic Closure in Multi-Edge Social NetworksMay 08 2019Multi-edge networks capture repeated interactions between individuals. In social networks, such edges often form closed triangles, or triads. Standard approaches to measure this triadic closure, however, fail for multi-edge networks, because they do not ... More
Consistent Fixed-Effects Selection in Ultra-high dimensional Linear Mixed Models with Error-Covariate EndogeneityMay 08 2019Recently, applied sciences, including longitudinal and clustered studies in biomedicine require the analysis of ultra-high dimensional linear mixed effects models where we need to select important fixed effect variables from a vast pool of available candidates. ... More
Robust regression based on shrinkage estimatorsMay 08 2019A robust estimator is proposed for the parameters that characterize the linear regression problem. It is based on the notion of shrinkages, often used in Finance and previously studied for outlier detection in multivariate data. A thorough simulation ... More
Predictive inference with the jackknife+May 08 2019This paper introduces the jackknife+, which is a novel method for constructing predictive confidence intervals. Whereas the jackknife outputs an interval centered at the predicted response of a test point, with the width of the interval determined by ... More
Bacterial hopping and trapping in porous mediaMay 07 2019Diverse processes--e.g. bioremediation, biofertilization, and microbial drug delivery--rely on bacterial migration in disordered, three-dimensional (3D) porous media. However, how pore-scale confinement alters bacterial motility is unknown due to the ... More
On Weighted Multivariate Sign FunctionsMay 07 2019Multivariate sign functions are often used for robust estimation and inference. We propose using data dependent weights in association with such functions. These weighted sign functions retain desirable robustness properties, while significantly improving ... More
A mixture model approach for clustering bipartite networksMay 07 2019This paper investigates the latent structure of bipartite networks via a model-based clustering approach which is able to capture both latent groups of sending nodes and latent trait variability of propensity of sending nodes to create links with receiving ... More
Minimax Hausdorff estimation of density level setsMay 07 2019Given a random sample of points from some unknown density, we propose a data-driven method for estimating density level sets under the r-convexity assumption. This shape condition generalizes the convexity property. However, the main problem in practice ... More
F-measure Maximizing Logistic RegressionMay 07 2019Logistic regression is a widely used method in several fields. When applying logistic regression to imbalanced data, for which majority classes dominate over minority classes, all class labels are estimated as `majority class.' In this article, we use ... More
Determination of Universal Critical Exponents Using Lee-Yang TheoryMay 07 2019Lee-Yang zeros are complex values of an external control parameter at which the partition function vanishes for a many-body system of finite size. In the thermodynamic limit, the Lee-Yang zeros approach the critical value on the real-axis where a phase ... More
Spectral and Steady-State Properties of Random LiouvilliansMay 06 2019We study generic open quantum systems with Markovian dissipation, focusing on a class of stochastic Liouvillian operators of Lindblad form with independent random dissipation channels (jump operators) and a random Hamiltonian. We establish that the global ... More
On the existence and optimisation of finite-time adiabatic processesMay 06 2019The concept of adiabaticity is ubiquitous in physics, and often associated to long time operations. We show here how this notion can be rigorously extended to finite-time processes in the context of stochastic thermodynamics. The case of a trapped Brownian ... More
Propensity Process: a Balancing FunctionalMay 06 2019In observational clinic registries, time to treatment is often of interest, but treatment can be given at any time during follow-up and there is no structure or intervention to ensure regular clinic visits for data collection. To address these challenges, ... More
Estimating the Mutual Information between two Discrete, Asymmetric Variables with Limited SamplesMay 06 2019Determining the strength of non-linear statistical dependencies between two variables is a crucial matter in many research fields. The established measure for quantifying such relations is the mutual information. However, estimating mutual information ... More
Spectral density estimation using P-spline priorsMay 06 2019This article proposes a Bayesian approach to estimating the spectral density of a stationary time series using a prior based on a mixture of P-spline distributions. Our proposal is motivated by the B-spline Dirichlet process prior of Edwards et al. (2018a) ... More
Non-standard inference for augmented double autoregressive models with null volatility coefficientsMay 06 2019This paper considers an augmented double autoregressive (DAR) model, which allows null volatility coefficients to circumvent the over-parameterization problem in the DAR model. Since the volatility coefficients might be on the boundary, the statistical ... More
Decision Making with Machine Learning and ROC CurvesMay 05 2019The Receiver Operating Characteristic (ROC) curve is a representation of the statistical information discovered in binary classification problems and is a key concept in machine learning and data science. This paper studies the statistical properties ... More
Efficient selection of predictive biomarkers for individual treatment selectionMay 05 2019The development of molecular diagnostic tools to achieve individualized medicine requires identifying predictive biomarkers associated with subgroups of individuals who might receive beneficial or harmful effects from different available treatments. However, ... More
Multivariate Signal Modelling with Applications to Inertial Sensor CalibrationMay 04 2019The common approach to inertial sensor calibration for navigation purposes has been to model the stochastic error signals of individual sensors independently, whether as components of a single inertial measurement unit (IMU) in different directions or ... More
Multivariate Signal Modelling with Applications to Inertial Sensor CalibrationMay 04 2019May 12 2019The common approach to inertial sensor calibration for navigation purposes has been to model the stochastic error signals of individual sensors independently, whether as components of a single inertial measurement unit (IMU) in different directions or ... More
Regularized estimation for highly multivariate log Gaussian Cox processesMay 04 2019Statistical inference for highly multivariate point pattern data is challenging due to complex models with large numbers of parameters. In this paper, we develop numerically stable and efficient parameter estimation and model selection algorithms for ... More
ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical VariablesMay 04 2019To address the challenge of backpropagating the gradient through categorical variables, we propose the augment-REINFORCE-swap-merge (ARSM) gradient estimator that is unbiased and has low variance. ARSM first uses variable augmentation, REINFORCE, and ... More
Test for homogeneity with unordered paired observationsMay 04 2019In some applications, an experimental unit is composed of two distinct but related subunits. The response from such a unit is $(X_{1}, X_{2})$ but we observe only $Y_1 = \min\{X_{1},X_{2}\}$ and $Y_2 = \max\{X_{1},X_{2}\}$, i.e., the subunit identities ... More
Parallel Gaussian process surrogate method to accelerate likelihood-free inferenceMay 03 2019We consider Bayesian inference when only a limited number of noisy log-likelihood evaluations can be obtained. This occurs for example when complex simulator-based statistical models are fitted to data, and synthetic likelihood (SL) is used to form the ... More
Simulation study of estimating between-study variance and overall effect in meta-analyses of log-response-ratio for lognormal dataMay 03 2019Methods for random-effects meta-analysis require an estimate of the between-study variance, $\tau^2$. The performance of estimators of $\tau^2$ (measured by bias and coverage) affects their usefulness in assessing heterogeneity of study-level effects, ... More
Causality without potential outcomes and the dynamic approachMay 03 2019Several approaches to causal inference from observational studies have been proposed. Since the proposal of Rubin (1974) many works have developed a counterfactual approach to causality, statistically formalized by potential outcomes. Pearl (2000) has ... More
Online Control of the False Coverage Rate and False Sign RateMay 03 2019The false coverage rate (FCR) is the expected ratio of number of constructed confidence intervals (CIs) that fail to cover their respective parameters to the total number of constructed CIs. Procedures for FCR control exist in the offline setting, but ... More
Robust Model Selection for Finite Mixture of Regression Models Through TrimmingMay 03 2019In this article, we introduce a new variable selection technique through trimming for finite mixture of regression models. Compared to the traditional variable selection techniques, the new method is robust and not sensitive to outliers. The estimation ... More
Selection of the Number of Clusters in Functional Data AnalysisMay 02 2019Identifying the number $K$ of clusters in a dataset is one of the most difficult problems in clustering analysis. A choice of $K$ that correctly characterizes the features of the data is essential for building meaningful clusters. In this paper we tackle ... More
High dimensional VAR with low rank transitionMay 02 2019We propose a vector auto-regressive (VAR) model with a low-rank constraint on the transition matrix. This new model is well suited to predict high-dimensional series that are highly correlated, or that are driven by a small number of hidden factors. We ... More
A Conditional Empirical Likelihood Based Method for Model Parameter Estimation from Complex survey DatasetsMay 02 2019We consider an empirical likelihood framework for inference for a statistical model based on an informative sampling design. Covariate information is incorporated both through the weights and the estimating equations. The estimator is based on conditional ... More
Sparsity Double Robust Inference of Average Treatment EffectsMay 02 2019Many popular methods for building confidence intervals on causal effects under high-dimensional confounding require strong "ultra-sparsity" assumptions that may be difficult to validate in practice. To alleviate this difficulty, we here study a new method ... More
Reliability of relational event model estimates under sampling: how to fit a relational event model to 360 million dyadic eventsMay 02 2019We assess the reliability of relational event model parameters estimated under two sampling schemes: (1) uniform sampling from the observed events and (2) case-control sampling which samples non-events, or null dyads ("controls"), from a suitably defined ... More
Please Stop Permuting Features: An Explanation and AlternativesMay 01 2019This paper advocates against permute-and-predict (PaP) methods for interpreting black box functions. Methods such as the variable importance measures proposed for random forests, partial dependence plots, and individual conditional expectation plots remain ... More
Total positivity in structured binary distributionsMay 01 2019We study binary distributions that are multivariate totally positive of order 2 (MTP2). Binary distributions can be represented as an exponential family and we show that MTP2 exponential families are convex. Moreover, MTP2 quadratic exponential families, ... More
Two-sample inference for high-dimensional Markov networksMay 01 2019Markov networks are frequently used in sciences to represent conditional independence relationships underlying observed variables arising from a complex system. It is often of interest to understand how an underlying network differs between two conditions. ... More
Quantum Generalized Linear ModelsMay 01 2019Generalized linear models (GLM) are link function based statistical models. Many supervised learning algorithms are extensions of GLMs and have link functions built into the algorithm to model different outcome distributions. There are two major drawbacks ... More
Bridging of liquid drops at chemically structured wallsMay 01 2019Using mesoscopic interfacial models and microscopic density functional theory we study fluid adsorption at a dry wall decorated with three completely wet stripes of width $L$ separated by distances $D_1$ and $D_2$. The stripes interact with the fluid ... More
Variational Bayesian Inference for Mixed Logit Models with Unobserved Inter- and Intra-Individual HeterogeneityMay 01 2019Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for Bayesian estimation of mixed logit models. In this paper, we derive a VB method for posterior inference in mixed ... More
A multiple-frame approach of crop yield estimation from satellite remotely sensed dataMay 01 2019Many studies have recently explored the information from the satellite-remotely sensed data (SRSD) for estimating the crop production statistics. The value of this information depends on the aerial and spatial resolutions of SRSD. The SRSD with fine spatial ... More
Scalable GWR: A linear-time algorithm for large-scale geographically weighted regression with polynomial kernelsMay 01 2019While a number of studies have developed fast geographically weighted regression (GWR) algorithms for large samples, none of them achieves the linear-time estimation that is considered requisite for big data analysis in machine learning, geostatistics, ... More
Handling an uncertain control group event risk in non-inferiority trials: non-inferiority frontiers and the power-stabilising transformationMay 01 2019Background. Non-inferiority (NI) trials are increasingly used to evaluate new treatments expected to have secondary advantages over standard of care, but similar efficacy on the primary outcome. When designing a NI trial with a binary primary outcome, ... More
Asymptotically optimal sequential FDR and pFDR control with (or without) prior information on the number of signalsMay 01 2019We investigate asymptotically optimal multiple testing procedures for streams of sequential data in the context of prior information on the number of false null hypotheses ("signals"). We show that the "gap" and "gap-intersection" procedures, recently ... More
Smooth Density Spatial Quantile RegressionApr 30 2019We derive the properties and demonstrate the desirability of a model-based method for estimating the spatially-varying effects of covariates on the quantile function. By modeling the quantile function as a combination of I-spline basis functions and Pareto ... More
A guide to Value of Information methods for prioritising research in health impact modellingApr 30 2019Health impact simulation models are used to predict how a proposed intervention or scenario will affect public health outcomes, based on available data and knowledge of the process. The outputs of these models are uncertain due to uncertainty in the structure ... More