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Fisher score is one of the most widely used supervised feature selection methods. However, it selects each feature independently according to their scores under the Fisher criterion, which leads to a suboptimal subset of features. In this…

Machine Learning · Computer Science 2012-02-20 Quanquan Gu , Zhenhui Li , Jiawei Han

Global expression analyses using microarray technologies are becoming more common in genomic research, therefore, new statistical challenges associated with combining information from multiple studies must be addressed. In this paper we…

Applications · Statistics 2013-01-29 Jia Li , George C. Tseng

Replicability is a lynchpin for credible discoveries. The partial conjunction (PC) p-value, which combines individual base p-values from multiple similar studies, can gauge whether a feature of interest exhibits replicated signals across…

Methodology · Statistics 2025-07-29 Ninh Tran , Dennis Leung

This is the first of a pair of papers which address the problem of measuring the unredshifted power spectrum in optimal fashion from a survey of galaxies, with arbitrary geometry, for Gaussian or non-Gaussian fluctuations, in real or…

Astrophysics · Physics 2015-06-24 A. J. S. Hamilton

Gravitational-wave astronomers often wish to characterize the expected parameter-estimation accuracy of future observations. The Fisher matrix provides a lower bound on the spread of the maximum-likelihood estimator across noise…

General Relativity and Quantum Cosmology · Physics 2011-11-08 Michele Vallisneri

Auxiliary particle filters (APFs) are a class of sequential Monte Carlo (SMC) methods for Bayesian inference in state-space models. In their original derivation, APFs operate in an extended state space using an auxiliary variable to improve…

Computation · Statistics 2021-06-17 Nicola Branchini , Víctor Elvira

The Behrens-Fisher Problem is a classical statistical problem. It is to test the equality of the means of two normal populations using two independent samples, when the equality of the population variances is unknown. Linnik (1968) has…

Statistics Theory · Mathematics 2022-11-09 Chang Wang , Jinzhu Jia

In this paper we consider estimation of sparse covariance matrices and propose a thresholding procedure which is adaptive to the variability of individual entries. The estimators are fully data driven and enjoy excellent performance both…

Methodology · Statistics 2011-02-14 Tony Cai , Weidong Liu

An accelerated class of adaptive scheme of iterative thresholding algorithms is studied analytically and empirically. They are based on the feedback mechanism of the null space tuning techniques (NST+HT+FB). The main contribution of this…

Information Theory · Computer Science 2020-05-15 Ningning Han , Shidong Li , Zhanjie Song

In TDD reciprocity-based massive MIMO it is essential to be able to compute the downlink precoding matrix over all OFDM resource blocks within a small fraction of the uplink-downlink slot duration. Early implementation of massive MIMO are…

Information Theory · Computer Science 2017-11-15 Andreas Benzin , Giuseppe Caire , Yonatan Shadmi , Antonia Tulino

Over the past decades, there has been a surge of interest in studying low-dimensional structures within high-dimensional data. Statistical factor models $-$ i.e., low-rank plus diagonal covariance structures $-$ offer a powerful framework…

Machine Learning · Statistics 2025-05-20 Daniel Cederberg

Threshold selection is a critical issue for extreme value analysis with threshold-based approaches. Under suitable conditions, exceedances over a high threshold have been shown to follow the generalized Pareto distribution (GPD)…

Methodology · Statistics 2018-06-13 Brian Bader , Jun Yan , Xuebin Zhang

Randomization inference is a widely-used and appealing approach for analyzing treatment effects in randomized experiments, as it is finite-sample valid and does not require any distributional assumptions. However, naive application of…

Econometrics · Economics 2026-05-12 Xinran Li , Peizan Sheng , Zeyang Yu

Threshold-free cluster enhancement (TFCE) is widely used for cluster-based inference in neuroimaging, but existing implementations typically rely on discretized approximations that may introduce numerical variability. We present eTFCE, an…

Methodology · Statistics 2026-04-28 Xu Chen , Wouter D. Weeda , Thomas E. Nichols , Jelle J. Goeman

Averaging the parameters of models that have the same architecture and initialization can provide a means of combining their respective capabilities. In this paper, we take the perspective that this "merging" operation can be seen as…

Machine Learning · Computer Science 2022-08-29 Michael Matena , Colin Raffel

This paper considers the problem of estimation of the Fisher information for location from a random sample of size $n$. First, an estimator proposed by Bhattacharya is revisited and improved convergence rates are derived. Second, a new…

Information Theory · Computer Science 2020-05-08 Wei Cao , Alex Dytso , Michael Fauß , H. Vincent Poor , Gang Feng

Combining p-values from independent statistical tests is a popular approach to meta-analysis, particularly when the data underlying the tests are either no longer available or are difficult to combine. A diverse range of p-value combination…

Methodology · Statistics 2017-12-15 Nicholas Heard , Patrick Rubin-Delanchy

A novel fully-connected (FC) tri-hybrid beamforming (THB) architecture is proposed for pinching antenna systems (PASS). In contrast to conventional sub-connected (SC) PASS, the proposed FC architecture employs a tunable phase-shifter…

Signal Processing · Electrical Eng. & Systems 2025-11-19 Cheng-Jie Zhao , Zhaolin Wang , Hyundong Shin , Yuanwei Liu

In confirmatory clinical trials with small sample sizes, hypothesis tests based on asymptotic distributions are often not valid and exact non-parametric procedures are applied instead. However, the latter are based on discrete test…

Methodology · Statistics 2018-02-22 Robin Ristl , Dong Xi , Ekkehard Glimm , Martin Posch

To address the challenges of imbalanced multi-class datasets typically used for rare event detection in critical cyber-physical systems, we propose an optimal, efficient, and adaptable mixed integer programming (MIP) ensemble weighting…

Machine Learning · Computer Science 2025-04-16 Georgios Tertytchny , Georgios L. Stavrinides , Maria K. Michael