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Electrical impedance tomography (EIT) is a noninvasive imaging method whereby electrical measurements on the boundary of a conductive medium (the data) are taken according to a prescribed protocol set and inverted to map the internal…

This paper considers two-sided tests for the parameter of an endogenous variable in an instrumental variable (IV) model with heteroskedastic and autocorrelated errors. We develop the finite-sample theory of weighted-average power (WAP)…

Statistics Theory · Mathematics 2015-05-26 Humberto Moreira , Marcelo J. Moreira

Expected improvement (EI) is one of the most widely used acquisition functions in Bayesian optimization (BO). Despite its proven success in applications for decades, important open questions remain on the theoretical convergence behaviors…

Machine Learning · Statistics 2025-02-13 Jingyi Wang , Haowei Wang , Nai-Yuan Chiang , Cosmin G. Petra

Selection bias can hinder accurate estimation of association parameters in binary disease risk models using non-probability samples like electronic health records (EHRs). The issue is compounded when participants are recruited from multiple…

Multivariable Mendelian randomization (MVMR) uses genetic variants as instrumental variables to infer the direct effects of multiple exposures on an outcome. However, unlike univariable Mendelian randomization, MVMR often faces greater…

Methodology · Statistics 2025-08-19 Yinxiang Wu , Hyunseung Kang , Ting Ye

This study aimed to evaluate the potential of 3D echo-planar imaging (EPI) for improving the reliability of $T_2^*$-weighted ($T_2^*w$) data and quantification of $\textit{R}_2^*$ decay rate and susceptibility ($\chi$) compared to…

Medical Physics · Physics 2024-01-01 Yujia Huang , Lin Chen , Xu Li , Jiaen Liu

Patients suffering from pulmonary diseases typically exhibit pathological lung ventilation in terms of homogeneity. Electrical Impedance Tomography (EIT) is a non-invasive imaging method that allows to analyze and quantify the distribution…

Multivariable Mendelian randomization estimates the causal effect of multiple exposures on an outcome, typically using summary statistics of genetic variant associations. However, exposures of interest in Mendelian randomization…

Methodology · Statistics 2022-03-17 Jiazheng Zhu , Stephen Burgess , Andrew J. Grant

Electroencephalography (EEG) offers detailed access to neural dynamics but remains constrained by noise and trial-by-trial variability, limiting decoding performance in data-restricted or complex paradigms. Data augmentation is often…

Signal Processing · Electrical Eng. & Systems 2025-11-18 Woojae Jeong , Wenhui Cui , Kleanthis Avramidis , Takfarinas Medani , Shrikanth Narayanan , Richard Leahy

Magnetoencephalography (MEG) allows the non-invasive detection of interictal epileptiform discharges (IEDs). Clinical MEG analysis in epileptic patients traditionally relies on the visual identification of IEDs, which is time consuming and…

Two recently introduced model based bias corrected estimators for proportion of true null hypotheses ($\pi_0$) under multiple hypotheses testing scenario have been restructured for exponentially distributed random observations available for…

Statistics Theory · Mathematics 2020-07-28 Aniket Biswas , Gaurangadeb Chattopadhyay , Aditya Chatterjee

This article explores the estimation of unknown parameters and reliability characteristics under the assumption that the lifetimes of the testing units follow an Inverted Exponentiated Pareto (IEP) distribution. Here, both point and…

Statistics Theory · Mathematics 2025-01-22 Rajendranath Mondal , Aditi Kar Gangopadhyay , Raju Bhakta , Kousik Maiti

Mendelian randomization is a powerful tool for inferring the presence, or otherwise, of causal effects from observational data. However, the nature of genetic variants is such that pleiotropy remains a barrier to valid causal effect…

Methodology · Statistics 2021-08-04 Andrew J. Grant , Stephen Burgess

Electrical impedance tomography (EIT) is a non-invasive imaging method with diverse applications, including medical imaging and non-destructive testing. The inverse problem of reconstructing internal electrical conductivity from boundary…

Image and Video Processing · Electrical Eng. & Systems 2025-07-08 Sara Sippola , Siiri Rautio , Andreas Hauptmann , Takanori Ide , Samuli Siltanen

Mendelian randomization (MR) is a statistical method exploiting genetic variants as instrumental variables to estimate the causal effect of modifiable risk factors on an outcome of interest. Despite wide uses of various popular two-sample…

Methodology · Statistics 2021-11-17 Anqi Wang , Zhonghua Liu

Parameters defined via General Estimating Equations (GEE) can be estimated by maximizing the Empirical Likelihood (EL). Newey and Smith (2004) have recently shown that this EL estimator exhibits desirable higher-order asymptotic properties,…

Statistics Theory · Mathematics 2013-07-18 Susanne M. Schennach

Mendelian randomization (MR) is a popular method in genetic epidemiology to estimate the effect of an exposure on an outcome by using genetic instruments. These instruments are often selected from a combination of prior knowledge from…

Methodology · Statistics 2019-11-12 Nan Bi , Hyunseung Kang , Jonathan Taylor

Purpose: Point-of-care MRI requires operation outside of a faraday shielded room normally used to block image-degrading electromagnetic Interference (EMI). To address this, we introduce the EDITER method, an external sensor based dynamic…

Weighting methods are used in observational studies to adjust for covariate imbalances between treatment and control groups. Entropy balancing (EB) is an alternative to inverse probability weighting with an estimated propensity score. The…

Methodology · Statistics 2022-04-25 David Källberg , Ingeborg Waernbaum

In Mendelian randomization (MR) studies, genetic variants are used as instrumental variables (IVs) to investigate causal relationships between exposures and outcomes based on observational data. However, numerous genetic studies have shown…

Methodology · Statistics 2026-04-10 Julien St-Pierre , Archer Y. Yang , Mireille E. Schnitzer , Marc-André Legault