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Linear system identification from ensemble snapshot observationsMar 15 2019Developments in transcriptomics techniques have caused a large demand in tailored computational methods for modelling gene expression dynamics from experimental data. Recently, so-called single-cell experiments have revolutionised genetic studies. These ... More
High-dimensional nonparametric density estimation via symmetry and shape constraintsMar 14 2019We tackle the problem of high-dimensional nonparametric density estimation by taking the class of log-concave densities on $\mathbb{R}^p$ and incorporating within it symmetry assumptions, which facilitate scalable estimation algorithms and can mitigate ... More
Deep Distribution RegressionMar 14 2019Due to their flexibility and predictive performance, machine-learning based regression methods have become an important tool for predictive modeling and forecasting. However, most methods focus on estimating the conditional mean or specific quantiles ... More
Detecting causality in multivariate time series via non-uniform embeddingMar 14 2019Causal analysis based on non-uniform embedding schemes is an important way to detect the underlying interactions between dynamic systems. However, there are still some obstacles to estimate high-dimensional conditional mutual information and form optimal ... More
Distributionally Robust Selection of the BestMar 14 2019Specifying a proper input distribution is often a challenging task in simulation modeling. In practice, there may be multiple plausible distributions that can fit the input data reasonably well, especially when the data volume is not large. In this paper, ... More
A Multi-armed Bandit MCMC, with applications in sampling from doubly intractable posteriorMar 13 2019Markov chain Monte Carlo (MCMC) algorithms are widely used to sample from complicated distributions, especially to sample from the posterior distribution in Bayesian inference. However, MCMC is not directly applicable when facing the doubly intractable ... More
Rejoinder: "Gene Hunting with Hidden Markov Model Knockoffs"Mar 13 2019In this paper we deepen and enlarge the reflection on the possible advantages of a knockoff approach to genome wide association studies (Sesia et al., 2018), starting from the discussions in Bottolo & Richardson (2019); Jewell & Witten (2019); Rosenblatt ... More
Simultaneous Confidence Band for Stationary Covariance Function of Dense Functional DataMar 13 2019Mar 14 2019Inference via simultaneous confidence band is studied for stationary covariance function of dense functional data. A two-stage estimation procedure is proposed based on spline approximation, the first stage involving estimation of all the individual trajectories ... More
Variational Estimators for Bayesian Optimal Experimental DesignMar 13 2019Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources. Unfortunately, its applicability is hampered by the difficulty of obtaining accurate estimates of the expected information ... More
A novel Bayesian approach for variable selection in linear regression modelsMar 13 2019We propose a novel Bayesian approach to the problem of variable selection in multiple linear regression models. In particular, we present a hierarchical setting which allows for direct specification of a-priori beliefs about the number of nonzero regression ... More
Neyman-Pearson Criterion (NPC): A Model Selection Criterion for Asymmetric Binary ClassificationMar 12 2019We propose a new model selection criterion, the Neyman-Pearson criterion (NPC), for asymmetric binary classification problems such as cancer diagnosis, where the two types of classification errors have vastly different priorities. The NPC is a general ... More
Doubly Robust Inference when Combining Probability and Non-probability Samples with High-dimensional DataMar 12 2019Non-probability samples become increasingly popular in survey statistics but may suffer from selection biases that limit the generalizability of results to the target population. We consider integrating a non-probability sample with a probability sample ... More
Time-convolutionless master equation: Perturbative expansions to arbitrary order and application to quantum dotsMar 12 2019The time-convolutionless quantum master equation is an exact description of the nonequilibrium dynamics of open quantum systems, with the advantage of being local in time. We derive a perturbative expansion to arbitrary order in the system-reservoir coupling ... More
Flexible Clustering with a Sparse Mixture of Generalized Hyperbolic DistributionsMar 12 2019Robust clustering of high-dimensional data is an important topic because, in many practical situations, real data sets are heavy-tailed and/or asymmetric. Moreover, traditional model-based clustering often fails for high dimensional data due to the number ... More
Predicting paleoclimate from compositional data using multivariate Gaussian process inverse predictionMar 12 2019Multivariate compositional count data arise in many applications including ecology, microbiology, genetics, and paleoclimate. A frequent question in the analysis of multivariate compositional count data is what values of a covariate(s) give rise to the ... More
Practical Multi-fidelity Bayesian Optimization for Hyperparameter TuningMar 12 2019Bayesian optimization is popular for optimizing time-consuming black-box objectives. Nonetheless, for hyperparameter tuning in deep neural networks, the time required to evaluate the validation error for even a few hyperparameter settings remains a bottleneck. ... More
Causal organic direct and indirect effects: closer to Baron and KennyMar 12 2019Baron and Kenny (1986, 80,433 Google Scholar citations) proposed estimators of direct and indirect effects: the part of a treatment effect that is mediated by a covariate and the part that is not. Subsequent work on natural direct and indirect effects ... More
Generalized Sparse Additive ModelsMar 11 2019We present a unified framework for estimation and analysis of generalized additive models in high dimensions. The framework defines a large class of penalized regression estimators, encompassing many existing methods. An efficient computational algorithm ... More
Bayesian Allocation Model: Inference by Sequential Monte Carlo for Nonnegative Tensor Factorizations and Topic Models using Polya UrnsMar 11 2019We introduce a dynamic generative model, Bayesian allocation model (BAM), which establishes explicit connections between nonnegative tensor factorization (NTF), graphical models of discrete probability distributions and their Bayesian extensions, and ... More
Diffusion $K$-means clustering on manifolds: provable exact recovery via semidefinite relaxationsMar 11 2019We introduce the {\it diffusion $K$-means} clustering method on Riemannian submanifolds, which maximizes the within-cluster connectedness based on the diffusion distance. The diffusion $K$-means constructs a random walk on the similarity graph with vertices ... More
Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing ApproachMar 11 2019For a better understanding of the molecular causes of lung cancer, the Boston Lung Cancer Study (BLCS) has generated comprehensive molecular data from both lung cancer cases and controls. It has been challenging to model such high dimensional data with ... More
Estimating Individualized Decision Rules with Tail ControlsMar 11 2019With the emergence of precision medicine, estimating optimal individualized decision rules (IDRs) has attracted tremendous attentions in many scientific areas. Most existing literature has focused on finding optimal IDRs that can maximize the expected ... More
The Shortest Confidence Interval for the Ratio of Quantiles of the Dagum DistributionMar 11 2019J\k{e}drzejczak et al. (2018) constructed a confidence interval for a ratio of quantiles coming from the Dagum distribution, which is frequently applied as a theoretical model in numerous income distribution analyses. The proposed interval is symmetric ... More
Confidence Interval for Quantile Ratio of the Dagum DistributionMar 11 2019In economic research inequality measures based on ratios of quantiles are frequently applied to the analysis of income distributions. In the paper, we construct a confidence interval for such measures under the Dagum distribution which has been widely ... More
A synthetic likelihood-based Laplace approximation for efficient design of biological processesMar 11 2019Complex models used to describe biological processes in epidemiology and ecology often have computationally intractable or expensive likelihoods. This poses significant challenges in terms of Bayesian inference but more significantly in the design of ... More
Extreme events of higher-order Markov chains: hidden tail chains and extremal Yule-Walker equationsMar 10 2019We derive some key extremal features for kth order Markov chains, which can be used to understand how the process moves to and fro between the body of the process and an extreme state. The chains are studied given that there is an exceedance of a threshold, ... More
Streamlined Variational Inference for Higher Level Group-Specific Curve ModelsMar 10 2019A two-level group-specific curve model is such that the mean response of each member of a group is a separate smooth function of a predictor of interest. The three-level extension is such that one grouping variable is nested within another one, and higher ... More
Lasso tuning through the flexible-weighted bootstrapMar 10 2019Regularized regression approaches such as the Lasso have been widely adopted for constructing sparse linear models in high-dimensional datasets. A complexity in fitting these models is the tuning of the parameters which control the level of introduced ... More
Two paradoxical results in linear models: the variance inflation factor and the analysis of covarianceMar 09 2019A result from a standard linear model course is that the variance of the ordinary least squares (OLS) coefficient of a variable will never decrease if we add additional covariates. The variance inflation factor (VIF) measures the increase of the variance. ... More
Distributed Feature Screening via Componentwise DebiasingMar 09 2019Feature screening is a powerful tool in the analysis of high dimensional data. When the sample size $N$ and the number of features $p$ are both large, the implementation of classic screening methods can be numerically challenging. In this paper, we propose ... More
Adaptive-to-model hybrid of tests for regressionsMar 09 2019In model checking for regressions, nonparametric estimation-based tests usually have tractable limiting null distributions and are sensitive to oscillating alternative models, but suffer from the curse of dimensionality. In contrast, empirical process-based ... More
Transporting stochastic direct and indirect effects to new populationsMar 08 2019Transported mediation effects may contribute to understanding how and why interventions may work differently when applied to new populations. However, we are not aware of any estimators for such effects. Thus, we propose several different estimators of ... More
A Potential Outcomes Calculus for Identifying Conditional Path-Specific EffectsMar 08 2019The do-calculus is a well-known deductive system for deriving connections between interventional and observed distributions, and has been proven complete for a number of important identifiability problems in causal inference. Nevertheless, as it is currently ... More
Imputation estimators for unnormalized models with missing dataMar 08 2019We propose estimation methods for unnormalized models with missing data. The key concept is to combine a modern imputation technique with estimators for unnormalized models including noise contrastive estimation and score matching. Further, we derive ... More
Consistent Bayesian Sparsity Selection for High-dimensional Gaussian DAG Models with Multiplicative and Beta-mixture PriorsMar 08 2019Estimation of the covariance matrix for high-dimensional multivariate datasets is a challenging and important problem in modern statistics. In this paper, we focus on high-dimensional Gaussian DAG models where sparsity is induced on the Cholesky factor ... More
Computer code validation via mixture model estimationMar 08 2019When computer codes are used for modeling complex physical systems, their unknown parameters are tuned by calibration techniques. A discrepancy function may be added to the computer code in order to capture its discrepancy with the real physical process. ... More
Connecting Bayes factor and the Region of Practical Equivalence (ROPE) Procedure for testing interval null hypothesisMar 07 2019There has been strong recent interest in testing interval null hypothesis for improved scientific inference. For example, Lakens et al (2018) and Lakens and Harms (2017) use this approach to study if there is a pre-specified meaningful treatment effect ... More
Relaxing the Assumptions of Knockoffs by ConditioningMar 07 2019The recent paper Cand\`es et al. (2018) introduced model-X knockoffs, a method for variable selection that provably and non-asymptotically controls the false discovery rate with no restrictions or assumptions on the dimensionality of the data or the conditional ... More
Simultaneous Prediction Intervals for Small Area ParameterMar 07 2019In this paper we address the construction of simultaneous prediction intervals for small area parameters in linear mixed models. Simultaneous intervals are necessary to compare areas, or to look at several areas at a time, as the presently available intervals ... More
A comment on "New non-parametric inferences for low-income proportions" by Shan Luo and Gengsheng QinMar 06 2019Shan Luo and Gengsheng Qin published the article "New non-parametric inferences for low-income proportions" Ann Inst Stat Math, 69, 599-626. In the note their approach is compared to Zieli\'nski 2009 approach.
Threshold Selection in Univariate Extreme Value AnalysisMar 06 2019Threshold selection plays a key role for various aspects of statistical inference of rare events. Most classical approaches tackling this problem for heavy-tailed distributions crucially depend on tuning parameters or critical values to be chosen by the ... More
Economic variable selectionMar 06 2019Regression plays a key role in many research areas and its variable selection is a classic and major problem. This study emphasizes cost of predictors to be purchased for future use, when we select a subset of them. Its economic aspect is naturally formalized ... More
Economic variable selectionMar 06 2019Mar 08 2019Regression plays a key role in many research areas and its variable selection is a classic and major problem. This study emphasizes cost of predictors to be purchased for future use, when we select a subset of them. Its economic aspect is naturally formalized ... More
Graph-aware linear mixed effects models for brain connectivity networksMar 06 2019Neuroimaging data on functional connections in the brain are frequently represented by weighted networks. These networks share the same set of labeled nodes corresponding to a fixed atlas of the brain, while each subject's network has their own edge weights. ... More
Emulating computer models with step-discontinuous outputs using Gaussian processesMar 05 2019In many real-world applications, we are interested in approximating functions that are analytically unknown. An emulator provides a "fast" approximation of such functions relying on a limited number of evaluations. Gaussian processes (GPs) are commonplace ... More
Spike-and-Slab Group Lassos for Grouped Regression and Sparse Generalized Additive ModelsMar 05 2019Mar 10 2019We introduce the spike-and-slab group lasso (SSGL) for Bayesian estimation and variable selection in linear regression with grouped variables. We further extend the SSGL to sparse generalized additive models (GAMs), thereby introducing the first nonparametric ... More
Spike-and-Slab Group Lassos for Grouped Regression and Sparse Generalized Additive ModelsMar 05 2019We introduce the spike-and-slab group lasso (SSGL) for Bayesian estimation and variable selection in linear regression with grouped variables. We further extend the SSGL to sparse generalized additive models (GAMs), thereby introducing the first nonparametric ... More
Tutorial: Deriving The Efficient Influence Curve for Large ModelsMar 05 2019This paper aims to provide a tutorial for upper level undergraduate and graduate students in statistics and biostatistics on deriving influence functions for non-parametric and semi-parametric models. The author will build on previously known efficiency ... More
Tutorial: Deriving The Efficient Influence Curve for Large ModelsMar 05 2019Mar 09 2019This paper aims to provide a tutorial for upper level undergraduate and graduate students in statistics, biostatistics and epidemiology on deriving influence functions for non-parametric and semi-parametric models. The author will build on previously ... More
Convex Covariate Clustering for ClassificationMar 05 2019Clustering, like covariate selection for classification, is an important step to understand and interpret the data. However, clustering of covariates is often performed independently of the classification step, which can lead to undesirable clustering ... More
A multinomial Asymptotic Representation of Zenga's Discrete Index, its Influence Function and Data-driven ApplicationsMar 05 2019In this paper, we consider the Zenga index, one of the most recent inequality index. We keep the finite-valued original form and address the asymptotic theory. The asymptotic normality is established through a multinomial representation. The Influence ... More
Change-point detection for multivariate and non-Euclidean data with local dependencyMar 05 2019In a sequence of multivariate observations or non-Euclidean data objects, such as networks, local dependence is common and could lead to false change-point discoveries. We propose a new way of permutation -- circular block permutation with a random starting ... More
Statistical approach to detection of signals by Monte Carlo singular spectrum analysis: Multiple testingMar 04 2019The statistical approach to detection of a signal in noisy series is considered in the framework of Monte Carlo singular spectrum analysis. This approach contains a technique to control both type I and type II errors and also compare criteria. For simultaneous ... More
Simulation study of estimating between-study variance and overall effect in meta-analysis of standardized mean differenceMar 04 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
On genetic correlation estimation with summary statistics from genome-wide association studiesMar 04 2019Genome-wide association studies (GWAS) have been widely used to examine the association between single nucleotide polymorphisms (SNPs) and complex traits, where both the sample size n and the number of SNPs p can be very large. Recently, cross-trait polygenic ... More
Multiscale clustering of nonparametric regression curvesMar 04 2019In a wide range of modern applications, we observe a large number of time series rather than only a single one. It is often natural to suppose that there is some group structure in the observed time series. When each time series is modelled by a nonparametric ... More
Spectral Density-Based and Measure-Preserving ABC for partially observed diffusion processes. An illustration on Hamiltonian SDEsMar 04 2019Approximate Bayesian Computation (ABC) has become one of the major tools of likelihood-free statistical inference in complex mathematical models. Simultaneously, stochastic differential equations (SDEs) have developed to an established tool for modelling ... More
A functional-model-adjusted spatial scan statisticMar 04 2019This paper introduces a new spatial scan statistic designed to adjust cluster detection for longitudinal confounding factors indexed in space. The functional-model-adjusted statistic was developed using generalized functional linear models in which longitudinal ... More
Detection of latent heteroscedasticity and group-based regression effects in linear models via Bayesian model selectionMar 04 2019Standard linear modeling approaches make potentially simplistic assumptions regarding the structure of categorical effects that may obfuscate more complex relationships governing data. For example, recent work focused on the two-way unreplicated layout ... More
Crossover from compact to branched films in electrodeposition with surface diffusionMar 03 2019We study a model for thin film electrodeposition in which instability development by preferential adsorption and reduction of cations at surface peaks competes with surface relaxation by diffusion of the adsorbates. The model considers cations moving ... More
Empirical priors for prediction in sparse high-dimensional linear regressionMar 03 2019Often the primary goal of fitting a regression model is prediction, but the majority of work in recent years focuses on inference tasks, such as estimation and feature selection. In this paper we adopt the familiar sparse, high-dimensional linear regression ... More
Heavy Tailed Horseshoe PriorsMar 03 2019Locally adaptive shrinkage in the Bayesian framework is achieved through the use of local-global prior distributions that model both the global level of sparsity as well as individual shrinkage parameters for mean structure parameters. The most popular ... More
Scalable optimization-based sampling on function spaceMar 03 2019Optimization-based samplers provide an efficient and parallellizable approach to solving large-scale Bayesian inverse problems. These methods solve randomly perturbed optimization problems to draw samples from an approximate posterior distribution. "Correcting" ... More
Goodness-of-Fit Testing for Time Series Models via Distance CovarianceMar 02 2019In many statistical modeling frameworks, goodness-of-fit tests are typically administered to the estimated residuals. In the time series setting, whiteness of the residuals is assessed using the sample autocorrelation function. For many time series models, ... More
Sequential estimation for GEE with adaptive variables and subject selectionMar 02 2019Modeling correlated or highly stratified multiple-response data becomes a common data analysis task due to modern data monitoring facilities and methods. Generalized estimating equations (GEE) is one of the popular statistical methods for analyzing this ... More
Metropolized Knockoff SamplingMar 01 2019Model-X knockoffs is a wrapper that transforms essentially any feature importance measure into a variable selection algorithm, which discovers true effects while rigorously controlling the expected fraction of false positives. A frequently discussed challenge ... More
A Framework for Covariate Balance using Bregman DistancesMar 01 2019A common goal in observational research is to estimate marginal causal effects in the presence of confounding variables. One solution is to use the covariate distribution to weight the outcomes such that the data appear randomized. The propensity score ... More
Distance-Based Independence Screening for Canonical AnalysisFeb 28 2019This paper introduces a new method named Distance-based Independence Screening for Canonical Analysis (DISCA) to reduce dimensions of two random vectors with arbitrary dimensions. The objective of our method is to identify the low dimensional linear projections ... More
Deductive semiparametric estimation in Double-Sampling Designs with application to PEPFARFeb 28 2019Robust estimators in missing data problems often use semiparametric estimation. Such estimation usually requires the analytic form of the efficient influence function (EIF), the derivation of which can be ad hoc and difficult. Recent work has shown how ... More
SAFE ML: Surrogate Assisted Feature Extraction for Model LearningFeb 28 2019Complex black-box predictive models may have high accuracy, but opacity causes problems like lack of trust, lack of stability, sensitivity to concept drift. On the other hand, interpretable models require more work related to feature engineering, which ... More
Granger Causality Testing in High-Dimensional VARs: a Post-Double-Selection ProcedureFeb 28 2019In this paper we develop an LM test for Granger causality in high-dimensional VAR models based on penalized least squares estimations. To obtain a test which retains the appropriate size after the variable selection done by the lasso, we propose a post-double-selection ... More
General framework for testing Poisson-Voronoi assumption for real microstructuresFeb 28 2019Modeling microstructures is an interesting problem not just in Materials Science but also in Mathematics and Statistics. The most basic model for steel microstructure is the Poisson-Voronoi diagram. It has mathematically attractive properties and it has ... More
Generalized ballistic-conductive heat conduction in isotropic materialsFeb 28 2019The general isotropic constitutive equations of heat conduction with second sound and ballistic propagation in isotropic materials is given using Non-Equilibrium Thermodynamics with Internal Variables (NET-IV). The consequences of Onsager reciprocity ... More
Learning partially ranked data based on graph regularizationFeb 28 2019Ranked data appear in many different applications, including voting and consumer surveys. There often exhibits a situation in which data are partially ranked. Partially ranked data is thought of as missing data. This paper addresses parameter estimation ... More
Linear mixed models under endogeneity: modeling sequential treatment effects with application to a mobile health studyFeb 28 2019Mobile health is a rapidly developing field in which behavioral treatments are delivered to individuals via wearables or smartphones to facilitate health-related behavior change. Micro-randomized trials (MRT) are an experimental design for developing ... More
Nonnegative Bayesian nonparametric factor models with completely random measures for community detectionFeb 27 2019We present a Bayesian nonparametric Poisson factorization model for modeling network data with an unknown and potentially growing number of overlapping communities. The construction is based on completely random measures and allows the number of communities ... More
Prediction for two spatially modulated superfluids: $^4$He on fluorographene and on hexagonal BNFeb 27 2019We have derived the adsorption potential of $^4$He atoms on fluorographene (GF), on graphane and on hexagonal boron nitride (hBN) by a recently developed ab initio method that incorporates the van der Waals interaction. The $^4$He monolayer on GF and ... More
Quasi-Bayes properties of a recursive procedure for mixturesFeb 27 2019Bayesian methods are attractive and often optimal, yet nowadays pressure for fast computations, especially with streaming data and online learning, brings renewed interest in faster, although possibly sub-optimal, solutions. To what extent these algorithms ... More
Bayesian data fusion for unmeasured confoundingFeb 27 2019Bayesian causal inference offers a principled approach to policy evaluation of proposed interventions on mediators or time-varying exposures. We outline a general approach to the estimation of causal quantities for settings with time-varying confounding, ... More
Profile and Globe Tests of Mean Surfaces for Two-Sample Bivariate Functional DataFeb 27 2019Multivariate functional data has received considerable attention but testing for equality of mean surfaces and its profile has limited progress. The existing literature has tested equality of either mean curves of univariate functional samples directly, ... More
Profile and Globe Tests of Mean Surfaces for Two-Sample Bivariate Functional DataFeb 27 2019Mar 06 2019Multivariate functional data has received considerable attention but testing for equality of mean surfaces and its profile has limited progress. The existing literature has tested equality of either mean curves of univariate functional samples directly, ... More
A Good-Turing estimator for feature allocation modelsFeb 27 2019Feature allocation models generalize species sampling models by allowing every observation to belong to more than one species, now called features. Under the popular Bernoulli product model for feature allocation, given $n$ samples, we study the problem ... More
Adaptive Gaussian Copula ABCFeb 27 2019Approximate Bayesian computation (ABC) is a set of techniques for Bayesian inference when the likelihood is intractable but sampling from the model is possible. This work presents a simple yet effective ABC algorithm based on the combination of two classical ... More
Bayesian Effect Selection in Structured Additive Distributional Regression ModelsFeb 27 2019We propose a novel spike and slab prior specification with scaled beta prime marginals for the importance parameters of regression coefficients to allow for general effect selection within the class of structured additive distributional regression. This ... More
Using prior expansions for prior-data conflict checkingFeb 27 2019Any Bayesian analysis involves combining information represented through different model components, and when different sources of information are in conflict it is important to detect this. Here we consider checking for prior-data conflict in Bayesian ... More
ABCD-Strategy: Budgeted Experimental Design for Targeted Causal Structure DiscoveryFeb 27 2019Determining the causal structure of a set of variables is critical for both scientific inquiry and decision-making. However, this is often challenging in practice due to limited interventional data. Given that randomized experiments are usually expensive ... More
Machine learning for subgroup discovery under treatment effectFeb 27 2019In many practical tasks it is needed to estimate an effect of treatment on individual level. For example, in medicine it is essential to determine the patients that would benefit from a certain medicament. In marketing, knowing the persons that are likely ... More
Multivariate analysis of covariance when standard assumptions are violatedFeb 26 2019In applied research, it is often sensible to account for one or several covariates when testing for differences between multivariate means of several groups. However, the "classical" parametric multivariate analysis of covariance (MANCOVA) tests (e.g., ... More
A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete DistributionsFeb 26 2019The objective of goodness-of-fit testing is to assess whether a dataset of observations is likely to have been drawn from a candidate probability distribution. This paper presents a rank-based family of goodness-of-fit tests that is specialized to discrete ... More
Parameter Redundancy and the Existence of Maximum Likelihood Estimates in Log-linear ModelsFeb 26 2019In fitting log-linear models to contingency table data, the presence of zero cell entries can have an adverse effect on the estimability of parameters, due to parameter redundancy. We describe a general approach for determining whether a given log-linear ... More
Semiparametric estimation of heterogeneous treatment effects under the nonignorable assignment conditionFeb 26 2019We propose a semiparametric two-stage least square estimator for the heterogeneous treatment effects (HTE). HTE is the solution to certain integral equation which belongs to the class of Fredholm integral equations of the first kind, which is known to ... More
Paths to annihilation of first and second-order (anti)skyrmions via (anti)meron nucleation on the frustrated square latticeFeb 26 2019We study annihilation mechanisms of small first- and second-order skyrmions and antiskyrmions on the frustrated $J_1-J_2-J_3$ square lattice with broken inversion symmetry (DMI). We find that annihilation happens via the injection of the opposite topological ... More
Doubly stochastic distributions of extreme eventsFeb 26 2019The distribution of block maxima of sequences of independent and identically-distributed random variables is used to model extreme values in many disciplines. The traditional extreme value (EV) theory derives a closed-form expression for the distribution ... More
Dirac wave transmission in Lévy disordered systemsFeb 26 2019We investigate the propagation of electronic waves described by the Dirac equation subject to a L\'evy-type disorder distribution. Our numerical calculations, based on the transfer matrix method, in a system with a distribution of potential barriers show ... More
Estimating Atmospheric Motion Winds from Satellite Image Data using Space-time Drift ModelsFeb 25 2019Geostationary satellites collect high-resolution weather data comprising a series of images which can be used to estimate wind speed and direction at different altitudes. The Derived Motion Winds (DMW) Algorithm is commonly used to process these data ... More
A Dynamic Model for Double Bounded Time Series With Chaotic Driven Conditional AveragesFeb 25 2019In this work we introduce a class of dynamic models for time series taking values on the unit interval. The proposed model follows a generalized linear model approach where the random component, conditioned on the past information, follows a beta distribution, ... More
On BinscatterFeb 25 2019Binscatter is very popular in applied microeconomics. It provides a flexible, yet parsimonious way of visualizing and summarizing large data sets in regression settings, and it is often used for informal evaluation of substantive hypotheses such as linearity ... More
Logistic principal component analysis via non-convex singular value thresholdingFeb 25 2019Multivariate binary data is becoming abundant in current biological research. Logistic principal component analysis (PCA) is one of the commonly used tools to explore the relationships inside a multivariate binary data set by exploiting the underlying ... More
Multiscale quantile segmentationFeb 25 2019Mar 07 2019We introduce a new methodology for analyzing serial data by quantile regression assuming that the underlying quantile function consists of constant segments. The procedure does not rely on any distributional assumption besides serial independence. It ... More
Multiscale quantile regressionFeb 25 2019Feb 26 2019We introduce a new methodology for analyzing serial data by quantile regression assuming that the underlying quantile function consists of constant segments. The procedure does not rely on any distributional assumption besides serial independence. It ... More