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In a high dimensional regression setting in which the number of variables ($p$) is much larger than the sample size ($n$), the number of possible two-way interactions between the variables is immense. If the number of variables is in the…

Methodology · Statistics 2024-06-26 Marianne A Jonker , Luc van Schijndel , Eric Cator

Hypothesis testing of structure in correlation and covariance matrices is of broad interest in many application areas. In high dimensions and/or small to moderate sample sizes, high error rates in testing is a substantial concern. This…

Methodology · Statistics 2026-01-07 Ziyang Ding , David Dunson

Whenever possible, the efficacy of a new treatment, such as a drug or behavioral intervention, is investigated by randomly assigning some individuals to a treatment condition and others to a control condition, and comparing the outcomes…

Methodology · Statistics 2015-05-04 Patrick C. Staples , Elizabeth L. Ogburn , Jukka-Pekka Onnela

Context: Software Engineering (SE) experiments suffer from threats to validity that may impact their results. Replication allows researchers building on top of previous experiments' weaknesses and increasing the reliability of the findings.…

Software Engineering · Computer Science 2020-04-14 Adrian Santos , Sira Vegas , Fernando Uyaguari , Oscar Dieste , Burak Turhan , Natalia Juristo

Case-cohort design, an outcome-dependent sampling design for censored survival data, is increasingly used in biomedical research. The development of asymptotic theory for a case-cohort design in the current literature primarily relies on…

Statistics Theory · Mathematics 2012-04-13 Bin Nan , Jon A. Wellner

In genome-wide association (GWA) studies the goal is to detect association between one or more genetic markers and a given phenotype. The number of genetic markers in a GWA study can be in the order hundreds of thousands and therefore…

Methodology · Statistics 2016-12-22 Kari Krizak Halle , Srdjan Djurovic , Ole Andreas Andreassen , Mette Langaas

This work presents a statistically principled method for estimating the required number of instances in the experimental comparison of multiple algorithms on a given problem class of interest. This approach generalises earlier results by…

Methodology · Statistics 2019-08-06 Felipe Campelo , Elizabeth F. Wanner

I introduce a generic method for inference about a scalar parameter in research designs with a finite number of heterogeneous clusters where only a single cluster received treatment. This situation is commonplace in…

Econometrics · Economics 2020-10-09 Andreas Hagemann

Simultaneously performing variable selection and inference in high-dimensional regression models is an open challenge in statistics and machine learning. The increasing availability of vast amounts of variables requires the adoption of…

Methodology · Statistics 2025-05-08 Marco Molinari , Magne Thoresen

Prediction models for clinical outcomes may be developed using a source dataset and additionally applied to new settings. Towards model external validation and model updating in the new setting, one procedure is model modification learning…

Methodology · Statistics 2020-12-01 W Katherine Tan , Patrick J Heagerty

Factor analysis is over a century old, but it is still problematic to choose the number of factors for a given data set. The scree test is popular but subjective. The best performing objective methods are recommended on the basis of…

Methodology · Statistics 2015-11-12 A. B. Owen , J. Wang

Substudies of the Childhood Asthma Management Program [Control. Clin. Trials 20 (1999) 91-120; N. Engl. J. Med. 343 (2000) 1054-1063] seek to identify patient characteristics associated with asthma symptoms and lung function. To determine…

Response-adaptive clinical trial designs allow targeting a given objective by skewing the allocation of participants to treatments based on observed outcomes. Response-adaptive designs face greater regulatory scrutiny due to potential type…

Methodology · Statistics 2025-03-19 Stef Baas , Peter Jacko , Sofía S. Villar

Cluster randomized trials with measurements at baseline can improve power over post-test only designs by using difference in difference designs. However, subjects may be lost to follow-up between the baseline and follow-up periods. While…

Methodology · Statistics 2019-03-26 Jonathan Moyer , Ken Kleinman

Pragmatic trials evaluating health care interventions often adopt cluster randomization due to scientific or logistical considerations. Previous reviews have shown that co-primary endpoints are common in pragmatic trials but infrequently…

Methodology · Statistics 2022-05-03 Siyun Yang , Mirjam Moerbeek , Monica Taljaard , Fan Li

Partial conjunction (PC) hypothesis testing is widely used to assess the replicability of scientific findings across multiple comparable studies. In high-throughput meta-analyses, testing a large number of PC hypotheses with k-family-wise…

Methodology · Statistics 2025-08-22 Ninh Tran

The analysis of case-control studies with several subtypes of cases is increasingly common, e.g. in cancer epidemiology. For matched designs, we show that a natural strategy is based on a stratified conditional logistic regression model.…

Methodology · Statistics 2019-01-23 Nadim Ballout , Cedric Garcia , Vivian Viallon

The case-control design is often used to test associations between the case-control status and genetic variants. In addition to this primary phenotype a number of additional traits, known as secondary phenotypes, are routinely recorded and…

In many settings, as for example wind farms, multiple machines are instantiated to perform the same task, which is called a fleet. The recent advances with respect to the Internet of Things allow control devices and/or machines to connect…

Machine Learning · Computer Science 2019-11-25 Timothy Verstraeten , Pieter JK Libin , Ann Nowé

Selective control in a population is the ability to control a member of the population while leaving the other members relatively unaffected. The concept of selective control is developed using cell death or apoptosis in heterogeneous cell…

Quantitative Methods · Quantitative Biology 2014-07-29 Diego Calzolari , Giovanni Paternostro , Patrick L. Harrington , Carlo Piermarocchi , Phillip M. Duxbury
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