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This work addresses the problem of missing data in time-series analysis focusing on (a) estimation of model parameters in the presence of missing data and (b) reconstruction of missing data. Standard approaches used to solve these problems…

Methodology · Statistics 2020-01-01 Dimitri Igdalov , Olga Kaiser , Ilia Horenko

Increasingly complex applications involve large datasets in combination with non-linear and high dimensional mathematical models. In this context, statistical inference is a challenging issue that calls for pragmatic approaches that take…

Data Analysis, Statistics and Probability · Physics 2013-01-31 Andreas Raue , Clemens Kreutz , Fabian Joachim Theis , Jens Timmer

Databases often contain corrupted, degraded, and noisy data with duplicate entries across and within each database. Such problems arise in citations, medical databases, genetics, human rights databases, and a variety of other applied…

Methodology · Statistics 2015-04-29 Rebecca C. Steorts

This paper builds on recent research that focuses on regression modeling of continuous bounded data, such as proportions measured on a continuous scale. Specifically, it deals with beta regression models with mixed effects from a Bayesian…

Bayesian 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…

Methodology · Statistics 2019-02-28 Leah Comment , Brent A. Coull , Corwin Zigler , Linda Valeri

We describe a new method for evaluating Bayes factors. The key idea is to introduce a hypermodel in which the competing models are components of a mixture distribution. Inference for the mixing probabilities then yields estimates of the…

Methodology · Statistics 2016-02-16 Philip D. O'Neill , Theodore Kypraios

In Bayesian hypothesis testing, evidence for a statistical model is quantified by the Bayes factor, which represents the relative likelihood of observed data under that model compared to another competing model. In general, computing Bayes…

Computation · Statistics 2021-12-07 Thomas J. Faulkenberry

Models for analyzing multivariate data sets with missing values require strong, often unassessable, assumptions. The most common of these is that the mechanism that created the missing data is ignorable - a twofold assumption dependent on…

Applications · Statistics 2020-02-17 Iavor Bojinov , Natesh Pillai , Donald Rubin

Gaussian factor models have proven widely useful for parsimoniously characterizing dependence in multivariate data. There is a rich literature on their extension to mixed categorical and continuous variables, using latent Gaussian variables…

Methodology · Statistics 2013-01-14 Jared S. Murray , David B. Dunson , Lawrence Carin , Joseph E. Lucas

A general Bayesian framework for model selection on random network models regarding their features is considered. The goal is to develop a principle Bayesian model selection approach to compare different fittable, not necessarily nested,…

Methodology · Statistics 2020-04-30 Papamichalis Marios

Selection bias arises when the probability that an observation enters a dataset depends on variables related to the quantities of interest, leading to systematic distortions in estimation and uncertainty quantification. For example, in…

The non-convexity and intractability of distributionally robust chance constraints make them challenging to cope with. From a data-driven perspective, we propose formulating it as a robust optimization problem to ensure that the…

Optimization and Control · Mathematics 2023-06-23 Zhiping Chen , Wentao Ma , Bingbing Ji

Bayesian approaches have become increasingly popular in causal inference problems due to their conceptual simplicity, excellent performance and in-built uncertainty quantification ('posterior credible sets'). We investigate Bayesian…

Machine Learning · Statistics 2019-09-27 Kolyan Ray , Botond Szabo

Economic evaluations from individual-level data are an important component of the process of technology appraisal, with a view to informing resource allocation decisions. A critical problem in these analyses is that both effectiveness and…

Applications · Statistics 2018-02-01 Andrea Gabrio , Alexina J. Mason , Gianluca Baio

For many important problems the quantity of interest is an unknown function of the parameters, which is a random vector with known statistics. Since the dependence of the output on this random vector is unknown, the challenge is to identify…

Machine Learning · Statistics 2021-04-28 Themistoklis P. Sapsis

We propose a novel Bayesian nonparametric classification model that combines a Gaussian process prior for the latent function with a Dirichlet process prior for the link function, extending the interpretative framework of de Finetti…

Methodology · Statistics 2025-08-26 Marcio Alves Diniz

We present an objective Bayes method for covariance selection in Gaussian multivariate regression models whose error term has a covariance structure which is Markov with respect to a Directed Acyclic Graph (DAG). The scope is…

Methodology · Statistics 2015-10-09 G. Consonni , L. La Rocca

In causal inference, sensitivity analysis is important to assess the robustness of study conclusions to key assumptions. We perform sensitivity analysis of the assumption that missing outcomes are missing completely at random. We follow a…

Statistics Theory · Mathematics 2023-05-12 Bart Eggen , Stéphanie L. van der Pas , Aad W. van der Vaart

We investigate methods for penalized regression in the presence of missing observations. This paper introduces a method for estimating the parameters which compensates for the missing observations. We first, derive an unbiased estimator of…

Applications · Statistics 2013-10-09 Yunjin Choi , Robert Tibshirani

Bayesian inference is a popular approach to calibrating uncertainties, but it can underpredict such uncertainties when model misspecification is present, impacting its reliability to inform decision making. Recently, the statistics and…

Computational Engineering, Finance, and Science · Computer Science 2026-01-09 Rebekah White , Rileigh Bandy , Teresa Portone
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