Stable data adaptive methods for matched field array processing in acoustic waveguides

2003 ◽  
Author(s):  
C. Byrne ◽  
R. Brent ◽  
C. Feuillade ◽  
D. Del Balzo
Keyword(s):  
Array Processing ◽  
Adaptive Methods ◽  
Acoustic Waveguides ◽  
Data Adaptive

A stable data adaptive method for matched‐field array processing in acoustic waveguides

1989 ◽  
Vol 85 (S1) ◽  
pp. S17-S17
Author(s):  
C. L. Byrne ◽  
R. I. Brent ◽  
C. Feuillade ◽  
D. R. DelBalzo
Keyword(s):  
Array Processing ◽  
Adaptive Method ◽  
Acoustic Waveguides ◽  
Data Adaptive

A stable data‐adaptive method for matched‐field array processing in acoustic waveguides

1990 ◽  
Vol 87 (6) ◽  
pp. 2493-2502 ◽  
Author(s):  
Charles L. Byrne ◽  
Ronald T. Brent ◽  
Christopher Feuillade ◽  
Donald R. DelBalzo
Keyword(s):  
Array Processing ◽  
Adaptive Method ◽  
Acoustic Waveguides ◽  
Data Adaptive

Principled Machine Learning Using the Super Learner: An Application to Predicting Prison Violence

2018 ◽  
Vol 48 (3) ◽  
pp. 698-721 ◽  
Author(s):  
Valerio Baćak ◽  
Edward H. Kennedy
Keyword(s):  
Machine Learning ◽  
Social Science ◽  
Research Question ◽  
Social Science Research ◽  
Science Research ◽  
Adaptive Methods ◽  
Prison Violence ◽  
Data Set ◽  
Super Learner ◽  
Data Adaptive

A rapidly growing number of algorithms are available to researchers who apply statistical or machine learning methods to answer social science research questions. The unique advantages and limitations of each algorithm are relatively well known, but it is not possible to know in advance which algorithm is best suited for the particular research question and the data set at hand. Typically, researchers end up choosing, in a largely arbitrary fashion, one or a handful of algorithms. In this article, we present the Super Learner—a powerful new approach to statistical learning that leverages a variety of data-adaptive methods, such as random forests and spline regression, and systematically chooses the one, or a weighted combination of many, that produces the best forecasts. We illustrate the use of the Super Learner by predicting violence among inmates from the 2005 Census of State and Federal Adult Correctional Facilities. Over the past 40 years, mass incarceration has drastically weakened prisons’ capacities to ensure inmate safety, yet we know little about the characteristics of prisons related to inmate victimization. We discuss the value of the Super Learner in social science research and the implications of our findings for understanding prison violence.

Generalizing Benfordʼs Law

Benford's Law ◽  
2015 ◽  
Author(s):  
Joanne Lee ◽  
Wendy K. Tam Cho ◽  
George Judge
Keyword(s):  
Scientific Misconduct ◽  
Adaptive Methods ◽  
Data Sets ◽  
Federal Grants ◽  
Information Theoretic ◽  
Data Adaptive ◽  
The University ◽  
University Of Vermont ◽  
Case Data ◽  
Information Theoretic Methods

This chapter examines and searches for evidence of fraud in two clinical data sets from a highly publicized case of scientific misconduct. In this case, data were falsified by Eric Poehlman, a faculty member at the University of Vermont, who pleaded guilty to fabricating more than a decade of data, some connected to federal grants from the National Institutes of Health. Poehlman had authored influential studies on many topics; including obesity, menopause, lipids, and aging. The chapter's classical Benford analysis along with a presentation of a more general class of Benford-like distributions highlights interesting insights into this and similar cases. In addition, this chapter demonstrates how information-theoretic methods and other data-adaptive methods are promising tools for generating benchmark distributions of first significant digits (FSDs) and examining data sets for departures from expectations.

scholarly journals Image Reconstruction: From Sparsity to Data-Adaptive Methods and Machine Learning

2020 ◽  
Vol 108 (1) ◽  
pp. 86-109 ◽  
Author(s):  
Saiprasad Ravishankar ◽  
Jong Chul Ye ◽  
Jeffrey A. Fessler
Keyword(s):  
Machine Learning ◽  
Image Reconstruction ◽  
Adaptive Methods ◽  
Data Adaptive

scholarly journals Understanding and diagnosing the potential for bias when using machine learning methods with doubly robust causal estimators

2018 ◽  
Vol 28 (6) ◽  
pp. 1637-1650 ◽  
Author(s):  
Asma Bahamyirou ◽  
Lucie Blais ◽  
Amélie Forget ◽  
Mireille E Schnitzer
Keyword(s):  
Propensity Score ◽  
Treatment Effect ◽  
Diagnostic Tool ◽  
Estimation Procedure ◽  
Adaptive Methods ◽  
Outcome Data ◽  
Average Treatment Effect ◽  
Simulation Scenario ◽  
Data Adaptive ◽  
Doubly Robust

Data-adaptive methods have been proposed to estimate nuisance parameters when using doubly robust semiparametric methods for estimating marginal causal effects. However, in the presence of near practical positivity violations, these methods can produce a separation of the two exposure groups in terms of propensity score densities which can lead to biased estimates of the treatment effect. To motivate the problem, we evaluated the Targeted Minimum Loss-based Estimation procedure using a simulation scenario to estimate the average treatment effect. We highlight the divergence in estimates obtained when using parametric and data-adaptive methods to estimate the propensity score. We then adapted an existing diagnostic tool based on a bootstrap resampling of the subjects and simulation of the outcome data in order to show that the estimation using data-adaptive methods for the propensity score in this study may lead to large bias and poor coverage. The adapted bootstrap procedure is able to identify this instability and can be used as a diagnostic tool.

scholarly journals Data-adaptive pipeline for filtering and normalizing metabolomics data

2018 ◽  
Author(s):  
Courtney Schiffman ◽  
Lauren Petrick ◽  
Kelsi Perttula ◽  
Yukiko Yano ◽  
Henrik Carlsson ◽  
...  
Keyword(s):  
Missing Values ◽  
Nearest Neighbor ◽  
Correlation Coefficients ◽  
Adaptive Methods ◽  
Untargeted Metabolomics ◽  
K Nearest Neighbor ◽  
Metabolomics Data ◽  
Biological Phenomena ◽  
Data Adaptive ◽  
Normalization Scheme

AbstractIntroductionUntargeted metabolomics datasets contain large proportions of uninformative features and are affected by a variety of nuisance technical effects that can bias subsequent statistical analyses. Thus, there is a need for versatile and data-adaptive methods for filtering and normalizing data prior to investigating the underlying biological phenomena.ObjectivesHere, we propose and evaluate a data-adaptive pipeline for metabolomics data that are generated by liquid chromatography-mass spectrometry platforms.MethodsOur data-adaptive pipeline includes novel methods for filtering features based on blank samples, proportions of missing values, and estimated intra-class correlation coefficients. It also incorporates a variant of k-nearest-neighbor imputation of missing values. Finally, we adapted an RNA-Seq approach and R package, scone, to select an appropriate normalization scheme for removing unwanted variation from metabolomics datasets.ResultsUsing two metabolomics datasets that were generated in our laboratory from samples of human blood serum and neonatal blood spots, we compared our data-adaptive pipeline with a traditional filtering and normalization scheme. The data-adaptive approach outperformed the traditional pipeline in almost all metrics related to removal of unwanted variation and maintenance of biologically relevant signatures. The R code for running the data-adaptive pipeline is provided with an example dataset at https://github.com/courtneyschiffman/Data-adaptive-metabolomics.ConclusionOur proposed data-adaptive pipeline is intuitive and effectively reduces technical noise from untargeted metabolomics datasets. It is particularly relevant for interrogation of biological phenomena in data derived from complex matrices associated with biospecimens.

scholarly journals 314Data-adaptive methods for high-dimensional mediation analysis: Application to a randomised trial of tuberculosis vaccination

2021 ◽  
Vol 50 (Supplement_1) ◽  
Author(s):  
Margarita Moreno-Betancur ◽  
Nicole L Messina ◽  
Kaya Gardiner ◽  
Nigel Curtis ◽  
Stijn Vansteelandt
Keyword(s):  
Machine Learning ◽  
Statistical Inference ◽  
Mediation Analysis ◽  
Learning Algorithms ◽  
Adaptive Methods ◽  
Machine Learning Algorithms ◽  
High Dimensional ◽  
Data Adaptive ◽  
Randomised Controlled ◽  
Doubly Robust

Abstract Focus of Presentation Statistical methods for causal mediation analysis are useful for understanding the pathways by which a certain treatment or exposure impacts health outcomes. Existing methods necessitate modelling of the distribution of the mediators, which quickly becomes infeasible when mediators are high-dimensional (e.g., biomarkers). We propose novel data-adaptive methods for estimating the indirect effect of a randomised treatment that acts via a pathway represented by a high-dimensional set of measurements. This work was motivated by the Melbourne Infant Study: BCG for Allergy and Infection Reduction (MIS BAIR), a randomised controlled trial investigating the effect of neonatal tuberculosis vaccination on clinical allergy and infection outcomes, and its mechanisms of action. Findings The proposed methods are doubly robust, which allows us to achieve (uniformly) valid statistical inference, even when machine learning algorithms are used for the two required models. We illustrate these in the context of the MIS BAIR study, investigating the mediating role of immune pathways represented by a high-dimensional vector of cytokine responses under various stimulants. We confirm adequate performance of the proposed methods in an extensive simulation study. Conclusions/Implications The proposed methods provide a feasible and flexible analytic strategy for examining high-dimensional mediators in randomised controlled trials. Key messages Data-adaptive methods for mediation analysis are desirable in the context of high-dimensional mediators, such as biomarkers. We propose novel doubly robust methods, which enable valid statistical inference when using machine learning algorithms for estimation.

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