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Distributions.jl: Definition and Modeling of Probability Distributions in the JuliaStats EcosystemJul 19 2019Random variables and their distributions are a central part in many areas of statistical methods. The Distributions.jl package provides Julia users and developers tools for working with probability distributions, leveraging Julia features for their intuitive ... More
An Approach to Efficient Fitting of Univariate and Multivariate Stochastic Volatility ModelsJul 19 2019The stochastic volatility model is a popular tool for modeling the volatility of assets. The model is a nonlinear and non-Gaussian state space model, and consequently is difficult to fit. Many approaches, both classical and Bayesian, have been developed ... More
A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential FamiliesJul 18 2019We consider the problem of computing the maximum likelihood multivariate log-concave distribution for a set of points. Specifically, we present an algorithm which, given $n$ points in $\mathbb{R}^d$ and an accuracy parameter $\epsilon>0$, runs in time ... More
Bayesian Variable Selection for Gaussian copula regression modelsJul 18 2019We develop a novel Bayesian method to select important predictors in regression models with multiple responses of diverse types. In particular, a sparse Gaussian copula regression model is used to account for the multivariate dependencies between any ... More
Amortized Monte Carlo IntegrationJul 18 2019Current approaches to amortizing Bayesian inference focus solely on approximating the posterior distribution. Typically, this approximation is, in turn, used to calculate expectations for one or more target functions - a computational pipeline which is ... More
Multi-Scale Process Modelling and Distributed Computation for Spatial DataJul 17 2019Recent years have seen a huge development in spatial modelling and prediction methodology, driven by the increased availability of remote-sensing data and the reduced cost of distributed-processing technology. It is well known that modelling and prediction ... More
Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samplesJul 17 2019For many important problems the quantity of interest (or output) is an unknown function of the parameter space (or input), which is a random vector with known statistics. Since the dependence of the output on this random vector is unknown, the challenge ... More
Optimal Sampling for Generalized Linear Models under Measurement ConstraintsJul 17 2019Jul 18 2019Suppose we are using a generalized linear model to predict a scalar outcome $Y$ given a covariate vector $X$. We consider two related problems and propose a methodology for both. In the first problem, every data point in a large dataset has both $Y$ and ... More
Optimal Sampling for Generalized Linear Models under Measurement ConstraintsJul 17 2019Suppose we are using a generalized linear model to predict a scalar outcome $Y$ given a covariate vector $X$. We consider two related problems and propose a methodology for both. In the first problem, every data point in a large dataset has both $Y$ and ... More
A 1D kinetic model for CMB ComptonizationJul 17 2019This work presents a novel derivation of the expressions that describe the distortions of the CMB curve due to the interactions between photons and the electrons present in dilute ionized systems. In this approach, a simplified a one-dimensional evolution ... More
Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVPJul 16 2019Time-varying parameter (TVP) models are widely used in time series analysis to flexibly deal with processes which gradually change over time. However, the risk of overfitting in TVP models is well known. This issue can be dealt with using appropriate ... More
Stochastic gradient Markov chain Monte CarloJul 16 2019Markov chain Monte Carlo (MCMC) algorithms are generally regarded as the gold standard technique for Bayesian inference. They are theoretically well-understood and conceptually simple to apply in practice. The drawback of MCMC is that in general performing ... More
Designing Perfect Simulation Algorithms using Local CorrectnessJul 15 2019Consider a randomized algorithm that draws samples exactly from a distribution using recursion. Such an algorithm is called a perfect simulation, and here a variety of methods for building this type of algorithm are shown to derive from the same result: ... More
Markov chain Monte Carlo algorithms with sequential proposalsJul 15 2019We explore a general framework in Markov chain Monte Carlo (MCMC) sampling where sequential proposals are tried as a candidate for the next state of the Markov chain. This sequential-proposal framework can be applied to various existing MCMC methods, ... More
Bayesian Synthesis of Probabilistic Programs for Automatic Data ModelingJul 14 2019We present new techniques for automatically constructing probabilistic programs for data analysis, interpretation, and prediction. These techniques work with probabilistic domain-specific data modeling languages that capture key properties of a broad ... More
rcosmo: R Package for Analysis of Spherical, HEALPix and Cosmological DataJul 12 2019The analysis of spatial observations on a sphere is important in areas such as geosciences, physics and embryo research, just to name a few. The purpose of the package rcosmo is to conduct efficient information processing, visualisation, manipulation ... More
Directing Power Towards Sub-AlternativesJul 11 2019This paper proposes a novel test statistic for testing a potentially high-dimensional parameter vector. To derive the statistic, I generalize the Mahalanobis distance to measure length in a direction of interest. The test statistic is the sample analogue ... More
Sparse Unit-Sum RegressionJul 10 2019This paper considers sparsity in linear regression under the restriction that the regression weights sum to one. We propose an approach that combines $\ell_0$- and $\ell_1$-regularization. We compute its solution by adapting a recent methodological innovation ... More
Probabilistic programming for birth-death models of evolution using an alive particle filter with delayed samplingJul 10 2019We consider probabilistic programming for birth-death models of evolution and introduce a new widely-applicable inference method that combines an extension of the alive particle filter (APF) with automatic Rao-Blackwellization via delayed sampling. Birth-death ... More
Improving the Performance of the LSTM and HMM Models via HybridizationJul 09 2019Language models based on deep neural neural networks and traditionalstochastic modelling has become both highly functional and effective in recenttimes. In this work a general survey into the two types of language modelling is conducted. We investigate ... More
The Integrated nested Laplace approximation for fitting models with multivariate responseJul 09 2019This paper introduces a Laplace approximation to Bayesian inference in regression models for multivariate response variables. We focus on Dirichlet regression models, which can be used to analyze a set of variables on a simplex exhibiting skewness and ... More
Decentralized Gaussian Mixture Fusion through Unified Quotient ApproximationsJul 09 2019This work examines the problem of using finite Gaussian mixtures (GM) probability density functions in recursive Bayesian peer-to-peer decentralized data fusion (DDF). It is shown that algorithms for both exact and approximate GM DDF lead to the same ... More
Guidelines for benchmarking of optimization approaches for fitting mathematical modelsJul 08 2019Insufficient performance of optimization approaches for fitting of mathematical models is still a major bottleneck in systems biology. In this manuscript, the reasons and methodological challenges are summarized as well as their impact in benchmark studies. ... More
Filaments of crime: Informing policing via thresholded ridge estimationJul 06 2019Objectives: We introduce a new method for reducing crime in hot spots and across cities through ridge estimation. In doing so, our goal is to explore the application of density ridges to hot spots and patrol optimization, and to contribute to the policing ... More
Spatio-Temporal Reconstructions of Global CO2-Fluxes using Gaussian Markov Random FieldsJul 05 2019Atmospheric inverse modelling is a method for reconstructing historical fluxes of green-house gas between land and atmosphere, using observed atmospheric concentrations and an atmospheric tracer transport model. The small number of observed atmospheric ... More
On the Convergence Rate of the Quasi- to Stationary Distribution for the Shiryaev-Roberts DiffusionJul 05 2019Jul 15 2019For the classical Shiryaev--Roberts martingale diffusion considered on the interval $[0,A]$, where $A>0$ is a given absorbing boundary, it is shown that the rate of convergence of the diffusion's quasi-stationary cumulative distribution function (cdf), ... More
On the Convergence Rate of the Quasi- to Stationary Distribution for the Shiryaev--Roberts DiffusionJul 05 2019For the classical Shiryaev--Roberts martingale diffusion considered on the interval $[0,A]$, where $A>0$ is a given absorbing boundary, it is shown that the rate of convergence of the diffusion's quasi-stationary cumulative distribution function (cdf), ... More
Efficient Parameter Estimation of Sampled Random FieldsJul 04 2019We provide a computationally and statistically efficient method for estimating the parameters of a stochastic Gaussian model observed on a spatial grid, which need not be rectangular. Standard methods are plagued by computational intractability, where ... More
Efficient Parameter Estimation of Sampled Random FieldsJul 04 2019Jul 15 2019We provide a computationally and statistically efficient method for estimating the parameters of a stochastic Gaussian model observed on a spatial grid, which need not be rectangular. Standard methods are plagued by computational intractability, where ... More
Subsampling Bias and The Best-Discrepancy Systematic Cross ValidationJul 04 2019Statistical machine learning models should be evaluated and validated before putting to work. Conventional k-fold Monte Carlo Cross-Validation (MCCV) procedure uses a pseudo-random sequence to partition instances into k subsets, which usually causes subsampling ... More
Bayesian Heterogeneity Pursuit Regression Models for Spatially Dependent DataJul 04 2019Most existing spatial clustering literatures discussed the cluster algorithm for spatial responses. In this paper, we consider a Bayesian clustered regression for spatially dependent data in order to detect clusters in the covariate effects. Our proposed ... More
Sequential Experimental Design for Functional Response ExperimentsJul 04 2019Understanding functional response within a predator-prey dynamic is essentially the cornerstone for most quantitative ecological studies. Over the past 60 years, the methodology for modelling functional response has gradually transitioned from the classic ... More
mgcpy: A Comprehensive High Dimensional Independence Testing Python PackageJul 03 2019With the increase in the amount of data in many fields, a method to consistently and efficiently decipher relationships within high dimensional data sets is important. Because many modern datasets are high-dimensional, univariate independence tests are ... More
An Econometric View of Algorithmic SubsamplingJul 03 2019Datasets that are terabytes in size are increasingly common, but computer bottlenecks often frustrate a complete analysis of the data. While more data are better than less, diminishing returns suggest that we may not need terabytes of data to estimate ... More
Model-based clustering and classification using mixtures of multivariate skewed power exponential distributionsJul 03 2019Families of mixtures of multivariate power exponential (MPE) distributions have been previously introduced and shown to be competitive for cluster analysis in comparison to other elliptical mixtures including mixtures of Gaussian distributions. Herein, ... More
A Bayesian Semiparametric Gaussian Copula Approach to a Multivariate Normality TestJul 03 2019In this paper, a Bayesian semiparametric copula approach is used to model the underlying multivariate distribution $F_{true}$. First, the Dirichlet process is constructed on the unknown marginal distributions of $F_{true}$. Then a Gaussian copula model ... More
A Bayesian Semiparametric Gaussian Copula Approach to a Multivariate Normality TestJul 03 2019Jul 04 2019In this paper, a Bayesian semiparametric copula approach is used to model the underlying multivariate distribution $F_{true}$. First, the Dirichlet process is constructed on the unknown marginal distributions of $F_{true}$. Then a Gaussian copula model ... More
Integrated Nested Laplace Approximations (INLA)Jul 02 2019This is a short description and basic introduction to the Integrated nested Laplace approximations (INLA) approach. INLA is a deterministic paradigm for Bayesian inference in latent Gaussian models (LGMs) introduced in Rue et al. (2009). INLA relies on ... More
Bayesian Analysis of High-dimensional Discrete Graphical ModelsJul 02 2019This work introduces a Bayesian methodology for fitting large discrete graphical models with spike-and-slab priors to encode sparsity. We consider a quasi-likelihood approach that enables node-wise parallel computation resulting in reduced computational ... More
Adaptive particle-based approximations of the Gibbs posterior for inverse problemsJul 02 2019In this work, we adopt a general framework based on the Gibbs posterior to update belief distributions for inverse problems governed by partial differential equations (PDEs). The Gibbs posterior formulation is a generalization of standard Bayesian inference ... More
GPU-based Parallel Computation Support for StanJul 01 2019This paper details an extensible OpenCL framework that allows Stan to utilize heterogeneous compute devices. It includes GPU-optimized routines for the Cholesky decomposition, its derivative, other matrix algebra primitives and some commonly used likelihoods, ... More
Mean Dimension of Ridge FunctionsJul 01 2019We consider the mean dimension of some ridge functions of spherical Gaussian random vectors of dimension $d$. If the ridge function is Lipschitz continuous, then the mean dimension remains bounded as $d\to\infty$. If instead, the ridge function is discontinuous, ... More
ensr: R Package for Simultaneous Selection of Elastic Net Tuning ParametersJul 01 2019Motivation: Elastic net regression is a form of penalized regression that lies between ridge and least absolute shrinkage and selection operator (LASSO) regression. The elastic net penalty is a powerful tool controlling the impact of correlated predictors ... More
Coupling techniques for nonlinear ensemble filteringJun 30 2019We consider filtering in high-dimensional non-Gaussian state-space models with intractable transition kernels, nonlinear and possibly chaotic dynamics, and sparse observations in space and time. We propose a novel filtering methodology that harnesses ... More
A Data-Validated Host-Parasite Model for Infectious Disease OutbreaksJun 29 2019The use of model experimental systems and mathematical models is important to further understanding of infectious disease dynamics and strategize disease mitigation. Gyrodactylids are helminth ectoparasites of teleost fish which have many dynamical characteristics ... More
trialr: Bayesian Clinical Trial Designs in R and StanJun 29 2019This manuscript introduces an \proglang{R} package called \pkg{trialr} that implements a collection of clinical trial methods in \proglang{Stan} and \proglang{R}. In this article, we explore three methods in detail. The first is the continual reassessment ... More
Fast and Exact Simulation of Multivariate Normal and Wishart Random Variables with Box ConstraintsJun 28 2019Models which include domain constraints occur in myriad contexts such as econometrics, genomics, and environmetrics, though simulating from constrained distributions can be computationally expensive. In particular, repeated sampling from constrained distributions ... More
Consensus Monte Carlo for Random Subsets using Shared AnchorsJun 28 2019We present a consensus Monte Carlo algorithm that scales existing Bayesian nonparametric models for clustering and feature allocation to big data. The algorithm is valid for any prior on random subsets such as partitions and latent feature allocation, ... More
Efficient stochastic optimisation by unadjusted Langevin Monte Carlo. Application to maximum marginal likelihood and empirical Bayesian estimationJun 28 2019Stochastic approximation methods play a central role in maximum likelihood estimation problems involving intractable likelihood functions, such as marginal likelihoods arising in problems with missing or incomplete data, and in parametric empirical Bayesian ... More
Improving and benchmarking of algorithms for decision making with lower previsionsJun 28 2019Maximality, interval dominance, and E-admissibility are three well-known criteria for decision making under severe uncertainty using lower previsions. We present a new fast algorithm for finding maximal gambles. We compare its performance to existing ... More
missSBM: An R Package for Handling Missing Values in the Stochastic Block ModelJun 28 2019The Stochastic Block Model (SBM) is a popular probabilistic model for random graph. It is commonly used to perform clustering on network data by aggregating nodes that share similar connectivity patterns into blocks. When fitting an SBM to a network which ... More
Dealing with Stochastic Volatility in Time Series Using the R Package stochvolJun 28 2019The R package stochvol provides a fully Bayesian implementation of heteroskedasticity modeling within the framework of stochastic volatility. It utilizes Markov chain Monte Carlo (MCMC) samplers to conduct inference by obtaining draws from the posterior ... More
Modeling Univariate and Multivariate Stochastic Volatility in R with stochvol and factorstochvolJun 28 2019Stochastic volatility (SV) models are nonlinear state-space models that enjoy increasing popularity for fitting and predicting heteroskedastic time series. However, due to the large number of latent quantities, their efficient estimation is non-trivial ... More
Automated characterization of noise distributions in diffusion MRI dataJun 28 2019Purpose: To understand and characterize noise distributions in parallel imaging for diffusion MRI. Theory and Methods: Two new automated methods using the moments and the maximum likelihood equations of the Gamma distribution were developed. Simulations ... More
Large scale Lasso with windowed active set for convolutional spike sortingJun 28 2019Spike sorting is a fundamental preprocessing step in neuroscience that is central to access simultaneous but distinct neuronal activities and therefore to better understand the animal or even human brain. But numerical complexity limits studies that require ... More
Recursion scheme for the largest $β$-Wishart-Laguerre eigenvalue and Landauer conductance in quantum transportJun 28 2019The largest eigenvalue distribution of the Wishart-Laguerre ensemble, indexed by Dyson parameter $\beta$ and Laguerre parameter $a$, is fundamental in multivariate statistics and finds applications in diverse areas. Based on a generalization of the Selberg ... More
A Python Library For Empirical CalibrationJun 27 2019Dealing with biased data samples is a common task across many statistical fields. In survey sampling, bias often occurs due to the unrepresentative samples. In causal studies with observational data, the treated vs untreated group assignment is often ... More
A Simultaneous Transformation and Rounding Approach for Modeling Integer-Valued DataJun 27 2019We propose a simple yet powerful framework for modeling integer-valued data. The integer-valued data are modeled by Simultaneously Transforming And Rounding (STAR) a continuous-valued process, where the transformation may be known or learned from the ... More
Time-evolving psychological processes over repeated decisionsJun 26 2019Many psychological experiments have participants repeat a simple task. This repetition is often necessary in order to gain the statistical precision required to answer questions about quantitative theories of the psychological processes underlying performance. ... More
Spatial 3D Matérn priors for fast whole-brain fMRI analysisJun 25 2019Bayesian whole-brain functional magnetic resonance imaging (fMRI) analysis with three-dimensional spatial smoothing priors have been shown to produce state-of-the-art activity maps without pre-smoothing the data. The proposed inference algorithms are ... More
Bayesian Nonparametric Clustering of Continuous-Time Hidden Markov Models for Health TrajectoriesJun 24 2019We develop clustering procedures for healthcare trajectories based on a continuous-time hidden Markov model and a generalized linear observation model. Specifically, we carry out Bayesian nonparametric inference for a Dirichlet process mixture model, ... More
A Role for Symmetry in the Bayesian Solution of Differential EquationsJun 24 2019The interpretation of numerical methods, such as finite difference methods for differential equations, as point estimators suggests that formal uncertainty quantification can also be performed in this context. Competing statistical paradigms can be considered ... More
A Role for Symmetry in the Bayesian Solution of Differential EquationsJun 24 2019Jun 26 2019The interpretation of numerical methods, such as finite difference methods for differential equations, as point estimators suggests that formal uncertainty quantification can also be performed in this context. Competing statistical paradigms can be considered ... More
Accelerating Metropolis-within-Gibbs sampler with localized computations of differential equationsJun 23 2019Inverse problem is ubiquitous in science and engineering, and Bayesian methodologies are often used to infer the underlying parameters. For high dimensional temporal-spatial models, classical Markov chain Monte Carlo (MCMC) methods are often slow to converge, ... More
Models of Continuous-Time Networks with Tie Decay, Diffusion, and ConvectionJun 22 2019The study of temporal networks in discrete time has yielded numerous insights into time-dependent networked systems in a wide variety of applications. For many complex systems, however, it is useful to develop continuous-time models of networks and to ... More
Copula Density Estimation by Finite Mixture of Parametric Copula DensitiesJun 22 2019A Copula density estimation method that is based on a finite mixture of heterogeneous parametric copula densities is proposed here. More specifically, the mixture components are Clayton, Frank, Gumbel, T, and normal copula densities, which are capable ... More
A Halo Merger Tree Generation and Evaluation FrameworkJun 22 2019Semi-analytic models are best suited to compare galaxy formation and evolution theories with observations. These models rely heavily on halo merger trees, and their realistic features (i.e., no drastic changes on halo mass or jumps on physical locations). ... More
Adaptive Approximate Bayesian Computation Tolerance SelectionJun 21 2019Approximate Bayesian Computation (ABC) methods are increasingly used for inference in situations in which the likelihood function is either computationally costly or intractable to evaluate. Extensions of the basic ABC rejection algorithm have improved ... More
A web application for the design of multi-arm clinical trialsJun 21 2019Multi-arm designs provide an effective means of evaluating several treatments within the same clinical trial. Given the large number of treatments now available for testing in many disease areas, it has been argued that their utilisation should increase. ... More
A Multiscale Scan Statistic for Adaptive Submatrix LocalizationJun 20 2019We consider the problem of localizing a submatrix with larger-than-usual entry values inside a data matrix, without the prior knowledge of the submatrix size. We establish an optimization framework based on a multiscale scan statistic, and develop algorithms ... More
Pushing the Limits of Importance Sampling through Iterative Moment MatchingJun 20 2019The accuracy of an integral approximation via Monte Carlo sampling depends on the distribution of the integrand and the existence of its moments. In importance sampling, the choice of the proposal distribution markedly affects the existence of these moments ... More
The Finite-Horizon Two-Armed Bandit Problem with Binary Responses: A Multidisciplinary Survey of the History, State of the Art, and MythsJun 20 2019In this paper we consider the two-armed bandit problem, which often naturally appears per se or as a subproblem in some multi-armed generalizations, and serves as a starting point for introducing additional problem features. The consideration of binary ... More
Formulating the Kramers problem in field theoryJun 20 2019The escape problem is defined in the context of quantum field theory. The escape rate is explicitly derived for a scalar field governed by fluctuation-dissipation dynamics, through generalizing the standard Kramers problem. In the presence of thermal ... More
Robust Clustering Using Tau-ScalesJun 19 2019K means is a popular non-parametric clustering procedure introduced by Steinhaus (1956) and further developed by MacQueen (1967). It is known, however, that K means does not perform well in the presence of outliers. Cuesta-Albertos et al (1997) introduced ... More
Importance conditional sampling for Bayesian nonparametric mixturesJun 19 2019Nonparametric mixture models based on the Pitman-Yor process represent a flexible tool for density estimation and clustering. Natural generalization of the popular class of Dirichlet process mixture models, they allow for more robust inference on the ... More
PLS Generalized Linear Regression and Kernel Multilogit Algorithm (KMA) for Microarray Data ClassificationJun 19 2019We implement extensions of the partial least squares generalized linear regression (PLSGLR) due to Bastien et al. (2005) through its combination with logistic regression and linear discriminant analysis, to get a partial least squares generalized linear ... More
Bayesian inverse regression for supervised dimension reduction with small datasetsJun 19 2019We consider supervised dimension reduction problems, namely to identify a low dimensional projection of the predictors $\-x$ which can retain the statistical relationship between $\-x$ and the response variable $y$. We follow the idea of the sliced inverse ... More
Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial dataJun 18 2019Jun 21 2019Generalized Gaussian processes (GGPs) are highly flexible models that combine latent GPs with potentially non-Gaussian likelihoods from the exponential family. GGPs can be used in a variety of settings, including GP classification, nonparametric count ... More
Vecchia-Laplace approximations of generalized Gaussian processes for big non-Gaussian spatial dataJun 18 2019Generalized Gaussian processes (GGPs) are highly flexible models that combine latent GPs with potentially non-Gaussian likelihoods from the exponential family. GGPs can be used in a variety of settings, including GP classification, nonparametric count ... More
Monte Carlo simulation on the Stiefel manifold via polar expansionJun 18 2019Motivated by applications to Bayesian inference for statistical models with orthogonal matrix parameters, we present $\textit{polar expansion},$ a general approach to Monte Carlo simulation from probability distributions on the Stiefel manifold. To bypass ... More
Variational Inference with Numerical Derivatives: variance reduction through couplingJun 17 2019The Black Box Variational Inference (Ranganath et al. (2014)) algorithm provides a universal method for Variational Inference, but taking advantage of special properties of the approximation family or of the target can improve the convergence speed significantly. ... More
(f)RFCDE: Random Forests for Conditional Density Estimation and Functional DataJun 17 2019Random forests is a common non-parametric regression technique which performs well for mixed-type unordered data and irrelevant features, while being robust to monotonic variable transformations. Standard random forests, however, do not efficiently handle ... More
Hierarchical Total Variations and Doubly Penalized ANOVA Modeling for Multivariate Nonparametric RegressionJun 16 2019For multivariate nonparametric regression, functional analysis-of-variance (ANOVA) modeling aims to capture the relationship between a response and covariates by decomposing the unknown function into various components, representing main effects, two-way ... More
A tunable multiresolution smoother for scattered data with application to particle filteringJun 16 2019A smoothing algorithm is presented that can reduce the small-scale content of data observed at scattered locations in a spatially extended domain. The smoother works by forming a Gaussian interpolant of the input data, and then convolving the interpolant ... More
Linear regression with stationary errors : the R package slmJun 15 2019Jul 08 2019This paper introduces the R package slm which stands for Stationary Linear Models. The package contains a set of statistical procedures for linear regression in the general context where the error process is strictly stationary with short memory. We work ... More
Linear regression with stationary errors : the R package slmJun 15 2019This paper introduces the R package slm which stands for Stationary Linear Models. The package contains a set of statistical procedures for linear regression in the general context where the error process is strictly stationary with short memory. We work ... More
Adaptive Variable Selection for Sequential Prediction in Multivariate Dynamic ModelsJun 15 2019We discuss Bayesian model uncertainty analysis and forecasting in sequential dynamic modeling of multivariate time series. The perspective is that of a decision-maker with a specific forecasting objective that guides thinking about relevant models. Based ... More
lpdensity: Local Polynomial Density Estimation and InferenceJun 15 2019Density estimation and inference methods are widely used in empirical work. When the data has compact support, as all empirical applications de facto do, conventional kernel-based density estimators are inapplicable near or at the boundary because of ... More
Linear Aggregation in Tree-based EstimatorsJun 15 2019Regression trees and their ensemble methods are popular methods for non-parametric regression --- combining strong predictive performance with interpretable estimators. In order to improve their utility for smooth response surfaces, we study regression ... More
Linear Aggregation in Tree-based EstimatorsJun 15 2019Jun 18 2019Regression trees and their ensemble methods are popular methods for non-parametric regression --- combining strong predictive performance with interpretable estimators. In order to improve their utility for smooth response surfaces, we study regression ... More
A New Family of Tractable Ising ModelsJun 14 2019We present a new family of zero-field Ising models over N binary variables/spins obtained by consecutive "gluing" of planar and $O(1)$-sized components along with subsets of at most three vertices into a tree. The polynomial time algorithm of the dynamic ... More
Robustly estimating the marginal likelihood for cognitive models via importance samplingJun 14 2019Recent advances in Markov chain Monte Carlo (MCMC) extend the scope of Bayesian inference to models for which the likelihood function is intractable. Although these developments allow us to estimate model parameters, other basic problems such as estimating ... More
A stochastic alternating minimizing method for sparse phase retrievalJun 14 2019Sparse phase retrieval plays an important role in many fields of applied science and thus attracts lots of attention. In this paper, we propose a \underline{sto}chastic alte\underline{r}nating \underline{m}inimizing method for \underline{sp}arse ph\underline{a}se ... More
Statistical Inference for Generative Models with Maximum Mean DiscrepancyJun 13 2019While likelihood-based inference and its variants provide a statistically efficient and widely applicable approach to parametric inference, their application to models involving intractable likelihoods poses challenges. In this work, we study a class ... More
Post-Processing of High-Dimensional DataJun 13 2019Jun 17 2019Scientific computations or measurements may result in huge volumes of data. Often these can be thought of representing a real-valued function on a high-dimensional domain, and can be conceptually arranged in the format of a tensor of high degree in some ... More
Post-Processing of High-Dimensional DataJun 13 2019Scientific computations or measurements may result in huge volumes of data. Often these can be thought of representing a real-valued function on a high-dimensional domain, and can be conceptually arranged in the format of a tensor of high degree in some ... More
A review of available software for adaptive clinical trial designJun 13 2019Background/Aims: The increasing expense of the drug development process has seen interest in the use of adaptive designs (ADs) grow substantially in recent years. Accordingly, much research has been conducted to identify potential barriers to increasing ... More
Direct Sampling of Bayesian Thin-Plate Splines for Spatial SmoothingJun 13 2019Radial basis functions are a common mathematical tool used to construct a smooth interpolating function from a set of data points. A spatial prior based on thin-plate spline radial basis functions can be easily implemented resulting in a posterior that ... More
Dynamic Time Scan ForecastingJun 12 2019The dynamic time scan forecasting method relies on the premise that the most important pattern in a time series precedes the forecasting window, i.e., the last observed values. Thus, a scan procedure is applied to identify similar patterns, or best matches, ... More
Optimal low rank tensor recoveryJun 12 2019We investigate the sample size requirement for exact recovery of a high order tensor of low rank from a subset of its entries. In the Tucker decomposition framework, we show that the Riemannian optimization algorithm with initial value obtained from a ... More