scholarly journals Online Statistical Inference for Large-Scale Binary Images

2017 ◽  
pp. 729-736
Author(s):  
Moo K. Chung ◽  
Ying Ji Chuang ◽  
Houri K. Vorperian
Keyword(s):  
Statistical Inference ◽  
Large Scale ◽  
Binary Images

scholarly journals ‘Trust Us’: Open Data and Preregistration in Political Science and International Relations

2022 ◽  
Author(s):  
Bermond Scoggins ◽  
Matthew Peter Robertson
Keyword(s):  
International Relations ◽  
Political Science ◽  
Statistical Inference ◽  
Large Scale ◽  
Open Data ◽  
Population Level ◽  
Open Science ◽  
Science Practices ◽  
Level Data ◽  
Replication Crisis

The scientific method is predicated on transparency -- yet the pace at which transparent research practices are being adopted by the scientific community is slow. The replication crisis in psychology showed that published findings employing statistical inference are threatened by undetected errors, data manipulation, and data falsification. To mitigate these problems and bolster research credibility, open data and preregistration have increasingly been adopted in the natural and social sciences. While many political science and international relations journals have committed to implementing these reforms, the extent of open science practices is unknown. We bring large-scale text analysis and machine learning classifiers to bear on the question. Using population-level data -- 93,931 articles across the top 160 political science and IR journals between 2010 and 2021 -- we find that approximately 21% of all statistical inference papers have open data, and 5% of all experiments are preregistered. Despite this shortfall, the example of leading journals in the field shows that change is feasible and can be effected quickly.

scholarly journals Statistical inference in population genomics

2021 ◽  
Author(s):  
Parul Johri ◽  
Charles F. Aquadro ◽  
Mark Beaumont ◽  
Brian Charlesworth ◽  
Laurent Excoffier ◽  
...  
Keyword(s):  
Statistical Inference ◽  
Large Scale ◽  
Population Genomics ◽  
Model Fitting ◽  
Genomic Data ◽  
Statistical Population ◽  
Biologically Relevant ◽  
Sequencing Technologies ◽  
Adaptive Processes ◽  
Careful Exploration

The field of population genomics has grown rapidly with the recent advent of affordable, large-scale sequencing technologies. As opposed to the situation during the majority of the 20th century, in which the development of theoretical and statistical population-genetic insights out-paced the generation of data to which they could be applied, genomic data are now being produced at a far greater rate than they can be meaningfully analyzed and interpreted. With this wealth of data has come a tendency to focus on fitting specific (and often rather idiosyncratic) models to data, at the expense of a careful exploration of the range of possible underlying evolutionary processes. For example, the approach of directly investigating models of adaptive evolution in each newly sequenced population or species often neglects the fact that a thorough characterization of ubiquitous non-adaptive processes is a prerequisite for accurate inference. We here describe the perils of these tendencies, present our views on current best practices in population genomic data analysis, and highlight areas of statistical inference and theory that are in need of further attention. Thereby, we argue for the importance of defining a biologically relevant baseline model tuned to the details of each new analysis, of skepticism and scrutiny in interpreting model-fitting results, and of carefully defining addressable hypotheses and underlying uncertainties.

scholarly journals Contour Extraction Based on Adaptive Thresholding in Sonar Images

Information ◽  
2021 ◽  
Vol 12 (9) ◽  
pp. 354
Author(s):  
Antonios Andreatos ◽  
Apostolos Leros
Keyword(s):  
Large Scale ◽  
Extraction Methods ◽  
Automatic Target Recognition ◽  
Multimedia Databases ◽  
Contour Extraction ◽  
Data Smoothing ◽  
Side Scan Sonar ◽  
Binary Images ◽  
Polynomial Curve ◽  
Sonar Images

A common problem in underwater side-scan sonar images is the acoustic shadow generated by the beam. Apart from that, there are a number of reasons impairing image quality. In this paper, an innovative algorithm with two alternative histogram approximation methods is presented. Histogram approximation is based on automatically estimating the optimal threshold for converting the original gray scale images into binary images. The proposed algorithm clears the shadows and masks most of the impairments in side-scan sonar images. The idea is to select a proper threshold towards the rightmost local minimum of the histogram, i.e., closest to the white values. For this purpose, the histogram envelope is approximated by two alternative contour extraction methods: polynomial curve fitting and data smoothing. Experimental results indicate that the proposed algorithm produces superior results than popular thresholding methods and common edge detection filters, even after corrosion expansion. The algorithm is simple, robust and adaptive and can be used in automatic target recognition, classification and storage in large-scale multimedia databases.

scholarly journals Statistical Analysis of Single-Trial Granger Causality Spectra

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Andrea Brovelli
Keyword(s):  
Statistical Analysis ◽  
Statistical Inference ◽  
Granger Causality ◽  
Large Scale ◽  
Linear Models ◽  
Causality Analysis ◽  
Single Trial ◽  
Experimental Conditions ◽  
Unidirectional Coupling ◽  
Visuomotor Task

Granger causality analysis is becoming central for the analysis of interactions between neural populations and oscillatory networks. However, it is currently unclear whether single-trial estimates of Granger causality spectra can be used reliably to assess directional influence. We addressed this issue by combining single-trial Granger causality spectra with statistical inference based on general linear models. The approach was assessed on synthetic and neurophysiological data. Synthetic bivariate data was generated using two autoregressive processes with unidirectional coupling. We simulated two hypothetical experimental conditions: the first mimicked a constant and unidirectional coupling, whereas the second modelled a linear increase in coupling across trials. The statistical analysis of single-trial Granger causality spectra, based ont-tests and linear regression, successfully recovered the underlying pattern of directional influence. In addition, we characterised the minimum number of trials and coupling strengths required for significant detection of directionality. Finally, we demonstrated the relevance for neurophysiology by analysing two local field potentials (LFPs) simultaneously recorded from the prefrontal and premotor cortices of a macaque monkey performing a conditional visuomotor task. Our results suggest that the combination of single-trial Granger causality spectra and statistical inference provides a valuable tool for the analysis of large-scale cortical networks and brain connectivity.

scholarly journals STATISTICAL INFERENCE FOR COPULAS IN HIGH DIMENSIONS: A SIMULATION STUDY

2013 ◽  
Vol 43 (2) ◽  
pp. 81-95 ◽  
Author(s):  
Paul Embrechts ◽  
Marius Hofert
Keyword(s):  
Statistical Inference ◽  
Large Scale ◽  
Goodness Of Fit ◽  
Point Of View ◽  
Simulation Studies ◽  
High Dimensions ◽  
Research Papers ◽  
Goodness Of Fit Tests ◽  
Interval Estimators ◽  
Theoretical Results

AbstractStatistical inference for copulas has been addressed in various research papers. Due to the complicated theoretical results, studies have been carried out mainly in the bivariate case, be it properties of estimators or goodness-of-fit tests. However, from a practical point of view, higher dimensions are of interest. This work presents the results of large-scale simulation studies with particular focus on the question to what extent dimensionality influences point and interval estimators.

scholarly journals SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks

2018 ◽  
Vol 14 (8) ◽  
pp. e1006369 ◽  
Author(s):  
Rong Zhang ◽  
Zhao Ren ◽  
Wei Chen
Keyword(s):  
Statistical Inference ◽  
Gene Networks ◽  
Large Scale ◽  
R Package

scholarly journals Large scale statistical inference of signaling pathways from RNAi and microarray data

2007 ◽  
Vol 8 (1) ◽  
Author(s):  
Holger Froehlich ◽  
Mark Fellmann ◽  
Holger Sueltmann ◽  
Annemarie Poustka ◽  
Tim Beissbarth
Keyword(s):  
Signaling Pathways ◽  
Statistical Inference ◽  
Microarray Data ◽  
Large Scale

Some comments about observations and image processing of comet 29P/Schwassmann-Wachmann 1

1999 ◽  
Vol 173 ◽  
pp. 243-248
Author(s):  
D. Kubáček ◽  
A. Galád ◽  
A. Pravda
Keyword(s):  
Image Processing ◽  
Large Scale ◽  
Harvard College ◽  
Oak Ridge ◽  
Large Scale Structures ◽  
Short Period Comet ◽  
Short Period ◽  
Scale Structures ◽  
Harvard College Observatory ◽  
Period Comet

AbstractUnusual short-period comet 29P/Schwassmann-Wachmann 1 inspired many observers to explain its unpredictable outbursts. In this paper large scale structures and features from the inner part of the coma in time periods around outbursts are studied. CCD images were taken at Whipple Observatory, Mt. Hopkins, in 1989 and at Astronomical Observatory, Modra, from 1995 to 1998. Photographic plates of the comet were taken at Harvard College Observatory, Oak Ridge, from 1974 to 1982. The latter were digitized at first to apply the same techniques of image processing for optimizing the visibility of features in the coma during outbursts. Outbursts and coma structures show various shapes.

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