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Bias correction methods for spatially lagged covariates measured with errors

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Spatial Statistics 2025年

作者： Mohammad Masjkur Asep Saefuddin I. Wayan Mangku Henk Folmer Arno J. Van der Vlist Marco Grzegorczyk School of Data Science Mathematics and Informatics IPB University Bogor Indonesia Faculty of Spatial Sciences Groningen University Groningen The Netherlands Padjadjaran University Bandung Indonesia Northwest Agriculture and Forestry University China Amsterdam School of Real Estate Amsterdam The Netherlands Statistics and Probability Bernoulli Institute Groningen University Groningen The Netherlands

This paper compares three widely applied bias correction methods for spatially lagged covariates measured with error, namely, Monte Carlo expectation-maximization (MCEM), instrumental variables (IV), and Bayesian analysis (BA). We cross-compare these correction methods on simulated data for the special case of one single lagged covariate. We use the root mean squared error (RMSE) as evaluation criterion. The findings indicate that BA is the best bias correction method.

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Improving Software Engineering in Biostatistics: Challenges and Opportunities

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arXiv 2023年

作者： Bové, Daniel Sabanés Seibold, Heidi Boulesteix, Anne-Laure Manitz, Juliane Gasparini, Alessandro Günhan, Burak K. Boix, Oliver Schüler, Armin Fillinger, Sven Nahnsen, Sven Jacob, Anna E. Jaki, Thomas Hoffmann-La Roche Ltd. Product Development Data Sciences Grenzacherstrasse 124 Basel4070 Switzerland IGDORE Elsenheimerstr. 48 München80687 Germany Institute for Medical Information Processing Biometry and Epidemiology LMU Munich Munich81377 Germany EMD Serono 45A Middlesex Turnpike BillericaMA01821 United States Department of Medical Epidemiology and Biostatistics Karolinska Institutet PO Box 281 Stockholm17177 Sweden Red Door Analytics AB Dianavägen 11A Stockholm11542 Sweden Merck Healthcare KGaA Frankfurter Strasse 250 Darmstadt64293 Germany Bayer AG Aprather Weg 18a Wuppertal42113 Germany University of Tübingen Tübingen72076 Germany Faculty of Informatics and Data Science University of Regensburg Bajuwarenstraße 4 Regensburg93053 Germany MRC Biostatistics Unit University of Cambridge East Forvie Building Forvie Site Robinson Way CambridgeCB2 0SR United Kingdom Biomedical Data Science Department of Computer Science University of Tübingen Tübingen72076 Germany

Programming is ubiquitous in applied biostatistics;adopting software engineering skills will help biostatisticians do a better job. To explain this, we start by highlighting key challenges for software development and application in biostatistics. Silos between different statistician roles, projects, departments, and organizations lead to the development of duplicate and suboptimal code. Building on top of open-source software requires critical appraisal and risk-based assessment of the used modules. Code that is written needs to be readable to ensure reliable software. The software needs to be easily understandable for the user, as well as developed within testing frameworks to ensure that long term maintenance of the software is feasible. Finally, the reproducibility of research results is hindered by manual analysis workflows and uncontrolled code development. We next describe how the awareness of the importance and application of good software engineering practices and strategies can help address these challenges. The foundation is a better education in basic software engineering skills in schools, universities, and during the work life. Dedicated software engineering teams within academic institutions and companies can be a key factor for the establishment of good software engineering practices and catalyze improvements across research projects. Providing attractive career paths is important for the retainment of talents. Readily available tools can improve the reproducibility of statistical analyses and their use can be exercised in community events. Similarly, tools exist to assess the risk of R packages, and initiatives are developing shared repositories of trusted R packages. Finally, collaboration between software developers from different organizations is key to harness open-source software efficiently and optimally, while building trusted solutions. We illustrate the potential with examples of successful projects. Copyright © 2023, The Authors. All right

关键词： Open source software

Advanced Big data Solutions for Detector Calibrations for High-Energy Physics

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Electronics 2025年第10期14卷 2088-2088页

作者： Abdulameer Nour Jalal Stefan Oniga Balazs Ujvari NAPLIFE-WIGNER Institute 1121 Budapest Hungary Doctoral School of Physics University of Debrecen 4032 Debrecen Hungary Department of Electric Electronic and Computer Engineering North University Center of Baia Mare Technical University of Cluj-Napoca 430083 Baia Mare Romania Department of IT Systems and Networks Faculty of Informatics University of Debrecen 4032 Debrecen Hungary Department of Data Science and Visualization Faculty of Informatics University of Debrecen 4032 Debrecen Hungary HUN-REN Institute for Nuclear Research 4026 Debrecen Hungary

This investigation examines the Dead Hot Map (DHM) method and timing calibration for Run 14 Au+Au collisions in the PHENIX experiment. The DHM method guarantees data integrity by identifying and omitting defective detector towers (nonfunctional, hot, and very hot towers) via a set of criteria and statistical evaluations. This procedure entails hit distribution analysis, pseudorapidity adjustments, and normalization, resulting in an enhanced map of functional detector components. Timing calibration mitigates the issues associated with time-of-flight measurement inaccuracies, such as slewing effects and inter-sector timing differences. Numerous corrections are implemented, encompassing slewing, tower-specific offsets, and sector-by-sector adjustments, resulting in a final resolution of 500 picoseconds for the electromagnetic calorimeter. These calibrations improve the accuracy of photon and π 0 measurements, essential for investigating quark–gluon plasma in high-energy nuclear collisions.

关键词： big data analytics high-energy physics (HEP) detector calibration data-processing pipelines dead hot map (DHM) timing calibration statistical analysis tools

Emerging Technologies for 6G Non-Terrestrial-Networks: From Academia to Industrial Applications

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arXiv 2024年

作者： Nguyen, Cong T. Saputra, Yuris Mulya Van Huynh, Nguyen Nguyen, Tan N. Hoang, Dinh Thai Nguyen, Diep N. Pham, Van-Quan Voznak, Miroslav Chatzinotas, Symeon Tran, Dinh-Hieu Institute of Fundamental and Applied Sciences Duy Tan University Ho Chi Minh City70000 Viet Nam Faculty of Information Technology Duy Tan University Da Nang50000 Viet Nam Internet Engineering Technology Department of Electrical Engineering and Informatics Vocational College Universitas Gadjah Mada Yogyakarta55281 Indonesia Department of Electrical Engineering and Electronics University of Liverpool LiverpoolL69 3GJ United Kingdom Communication and Signal Processing Research Group Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam School of Electrical and Data Engineering University of Technology Sydney SydneyNSW2007 Australia Nokia Bell Labs Murray Hill NJ07974 United States Department of Telecommunications Faculty of Electrical Engineering and Computer Science VSB-Technical University of Ostrava Ostrava708 00 Czech Republic University of Luxembourg Esch-sur-Alzette4365 Luxembourg Nokia France

Terrestrial networks form the fundamental infrastructure of modern communication systems, serving more than 4 billion users globally. However, terrestrial networks are facing a wide range of challenges, from coverage and reliability to interference and congestion. As the demands of the 6G era are expected to be much higher, it is crucial to address these challenges to ensure a robust and efficient communication infrastructure for the future. To address these problems, Non-terrestrial Network (NTN) has emerged to be a promising solution. NTNs are communication networks that leverage airborne (e.g., unmanned aerial vehicles) and spaceborne vehicles (e.g., satellites) to facilitate ultra-reliable communications and connectivity with high data rates and low latency over expansive regions. This article aims to provide a comprehensive survey on the utilization of network slicing, Artificial Intelligence/Machine Learning (AI/ML), and Open Radio Access Network (ORAN) to address diverse challenges of NTNs from the perspectives of both academia and industry. Particularly, we first provide an in-depth tutorial on NTN and the key enabling technologies including network slicing, AI/ML, and ORAN. Then, we provide a comprehensive survey on how network slicing and AI/ML have been leveraged to overcome the challenges that NTNs are facing. Moreover, we present how ORAN can be utilized for NTNs. Finally, we highlight important challenges, open issues, and future research directions of NTN in the 6G era. © 2024, CC BY.

关键词： Radio access networks

Calibration plots for multistate risk predictions models: an overview and simulation comparing novel approaches

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arXiv 2023年

作者： Pate, Alexander Sperrin, Matthew Riley, Richard D. Peek, Niels Van Staa, Tjeerd Sergeant, Jamie C. Mamas, Mamas A. Lip, Gregory Y.H. O'Flaherty, Martin Barrowman, Michael Buchan, Iain Martin, Glen P. Centre for Health Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom NIHR Manchester Biomedical Research Centre University of Manchester Manchester United Kingdom Institute of Applied Health Research University of Birmingham Birmingham United Kingdom Centre for Epidemiology Versus Arthritis Centre for Musculoskeletal Research Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Centre for Biostatistics Manchester Academic Health Science Centre University of Manchester Manchester United Kingdom Keele Cardiovascular Research Group Keele University Stoke-on-Trent United Kingdom Liverpool Centre for Cardiovascular Science University of Liverpool Liverpool John Moores University Liverpool Heart & Chest Hospital Liverpool United Kingdom Department of Clinical Medicine Aalborg University Aalborg Denmark NIHR Applied Research Collaboration NW Coast University of Liverpool Liverpool United Kingdom Institute of Population Health Faculty of Health and Life Sciences University of Liverpool Liverpool United Kingdom Manchester United Kingdom

Introduction There is currently no guidance on how to assess the calibration of multistate models used for risk prediction. We introduce several techniques that can be used to produce calibration plots for the transition probabilities of a multistate model, before assessing their performance in the presence of non-informative and informative censoring through a simulation. Methods We studied pseudo-values based on the Aalen-Johansen estimator, binary logistic regression with inverse probability of censoring weights (BLR-IPCW), and multinomial logistic regression with inverse probability of censoring weights (MLR-IPCW). The MLR-IPCW approach results in a calibration scatter plot, providing extra insight about the calibration. We simulated data with varying levels of censoring and evaluated the ability of each method to estimate the calibration curve for a set of predicted transition probabilities. We also developed evaluated the calibration of a model predicting the incidence of cardiovascular disease, type 2 diabetes and chronic kidney disease among a cohort of patients derived from linked primary and secondary healthcare records. Results The pseudo-value, BLR-IPCW and MLR-IPCW approaches give unbiased estimates of the calibration curves under non-informative censoring. These methods remained unbiased in the presence of informative censoring, unless the mechanism was strongly informative, with bias concentrated in the areas of predicted transition probabilities of low density. Conclusions We recommend implementing either the pseudo-value or BLR-IPCW approaches to produce a calibration curve, combined with the MLR-IPCW approach to produce a calibration scatter plot, which provides additional information over either of the other methods. © 2023, CC BY.

关键词： Calibration

Facial Emotion Detection Using Haar-Cascade Classifier and Convolutional Neural Networks

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Journal of Physics: Conference Series 2021年第1期1844卷

作者： P A Riyantoko Sugiarto K M Hindrayani Data Science Department Faculty of Computer Science UPN 'Veteran' Jawa Timur. Informatics Department Faculty of Computer Science UPN 'Veteran' Jawa Timur.

Computer vision has the challenge to detect the facial emotions of humans. Recently, in computer vision and machine learning, it's possible to detect emotion from video or image accurate. In our research will propose to classify facial emotion using Haar-Cascade Classifier and Convolutional Neural Networks. The experiment uses the FER2013 dataset which was collected for the facial expression recognition dataset, and we proposed seven classified facial expression. The CNN model gain MSE and accuracy value based on epoch variety. The results showed that with the increase in the epoch value, the smaller MSE value would be obtained, likewise, the accuracy value would be increased. Thus, the proposed algorithm of CNN is proven to be effective for facial emotion detection.

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A decomposi+on of Fisher’s informa+on to inform sample size for developing fair and precise clinical predic+on models – part 1: binary outcomes

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arXiv 2024年

作者： Riley, Richard D. Collins, Gary S. Whittle, Rebecca Archer, Lucinda Snell, Kym I.E. Dhiman, Paula Kirton, Laura Legha, Amardeep Liu, Xiaoxuan Denniston, Alastair Harrell, Frank E. Wynants, Laure MarKn, Glen P. Ensor, Joie Institute of Applied Health Research College of Medical and Dental Sciences University of Birmingham Birmingham United Kingdom Birmingham Biomedical Research Centre Birmingham United Kingdom Centre for Statistics in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford OxfordOX3 7LD United Kingdom Cancer Research UK Clinical Trials Unit Institute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham United Kingdom Department of Biostatistics Vanderbilt University School of Medicine NashvilleTN United States Maastricht University Maastricht Netherlands Department of Development and Regeneration KU Leuven Leuven Belgium Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom

Background When developing a clinical prediction model, the sample size of the development dataset is important. Smaller sample increase concerns of overfibng, instability, poor predictive performance and a lack of fairness. For models estimating the risk of a binary outcome, previous research has outlined minimum sample size calculations to minimise overfibng and precisely estimate the overall risk. However, even when meeting these criteria, the uncertainty (instability) in individual-level risk estimates may be considerable. Methods We propose a decomposition of Fisher’s information matrix to help examine and calculate the sample size required for developing a model with acceptably precise risk estimates to inform decisions and improve fairness. We outline a five-step process for use before data collection or when an existing dataset is available. It requires researchers to specify the overall risk in the target population, the (anticipated) distribution of key predictors in the model, and an assumed ‘core model’ either specified directly (i.e., a logistic regression equation is provided) or based on a specified C-statistic and relative effects of (standardised) predictors. Results We produce closed-form solutions that decompose the variance of an individual’s risk estimate into the Fisher’s unit information matrix, predictor values and the total sample size;this allows researchers to quickly calculate and examine individual-level uncertainty interval widths for specified sample sizes, alongside prediction and classification instability plots. The information can be presented to key stakeholders (e.g., health professionals, patients, grant funders) to determine a target sample size or whether an existing dataset is sufficient. Our proposal is implemented in our new soSware module pmstabilityss. We provide two real examples and emphasise the importance of clinical context, including any risk thresholds for decision making, and fairness checks. Conclusions Our appro

关键词： Prediction models

Grain segmentation in atomistic simulations using orientation-based iterative self-organizing data analysis

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arXiv 2021年

作者： Vimal, M. Sandfeld, S. Prakash, Arun Institute of Mechanics and Fluid Dynamcis TU Bergakademie Freiberg Lampadiusstraße 4 Freiberg09599 Germany Institute for Advanced Simulation - IAS-9 Materials Data Science and Informatics Forschungszentrum Juelich GmbH Juelich52425 Germany Faculty 5 - Georesources and Materials Engineering RWTH Aachen University Aachen52056 Germany

Atomistic simulations have now established themselves as an indispensable tool in understanding deformation mechanisms of materials at the atomic scale. Large scale simulations are regularly used to study the behavior of polycrystalline materials at the nanoscale. In this work, we propose a method for grain segmentation of an atomistic configuration using an unsupervised machine learning algorithm that clusters atoms into individual grains based on their orientation. The proposed method, called the Orisodata algorithm, is based on the iterative self-organizing data analysis technique and is modified to work in the orientation space. The working of the algorithm is demonstrated on a 122 grain nanocrystalline thin film sample in both undeformed and deformed states. The Orisodata algorithm is also compared with two other grain segmentation algorithms available in the open-source visualization tool Ovito. The results show that the Orisodata algorithm is able to correctly identify deformation twins as well as regions separated by low angle grain boundaries. The model parameters have intuitive physical meaning and relate to similar thresholds used in experiments, which not only helps obtain optimal values but also facilitates easy interpretation and validation of *** Codes 70-08 © 2021, CC BY-NC-SA.

关键词： Clustering algorithms

A decomposition of Fisher’s information to inform sample size for developing fair and precise clinical prediction models - Part 2: time-to-event outcomes

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arXiv 2025年

作者： Riley, Richard D. Collins, Gary S. Archer, Lucinda While, Rebecca Legha, Amardeep Kirton, Laura Dhiman, Paula Sadatsafavi, Mohsen Adderley, Nicola J. Alderman, Joseph MarGn, Glen P. Ensor, Joie Department of Applied Health Sciences School of Health Sciences College of Medicine and Health University of Birmingham Birmingham United Kingdom Birmingham Biomedical Research Centre Birmingham United Kingdom Centre for Sta?s?cs in Medicine Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences University of Oxford OxfordOX3 7LD United Kingdom Cancer Research UK Clinical Trials Unit Ins?tute of Cancer and Genomic Sciences College of Medical and Dental Sciences University of Birmingham Birmingham United Kingdom Respiratory Evalua?on Sciences Program Faculty of Pharmaceu?cal Sciences The University of Bri?sh Columbia Vancouver Canada Ins?tute of Inflamma?on and Ageing College of Medicine and Health University of Birmingham Birmingham United Kingdom Queen Elizabeth Hospital University Hospitals Birmingham NHS Founda?on Trust Birmingham United Kingdom Division of Informatics Imaging and Data Science Faculty of Biology Medicine and Health University of Manchester Manchester Academic Health Science Centre Manchester United Kingdom

Background When developing a clinical predicGon model using Gme-to-event data (i.e., with censoring and different lengths of follow-up), previous research focuses on the sample size needed to minimise overfifng and precisely esGmaGng the overall risk. However, instability of individual-level risk esGmates may sGll be large. Methods We propose using a decomposiGon of Fisher’s informaGon matrix to help examine and calculate the sample size required for developing a model that aims for precise and fair risk esGmates. We propose a six-step process which can be used before data collecGon or when an exisGng dataset is available. Steps (1) to (5) require researchers to specify the overall risk in the target populaGon at a key Gme-point of interest;an assumed pragmaGc ‘core model’ in the form of an exponenGal regression model;the (anGcipated) joint distribuGon of core predictors included in that model;and the distribuGon of any censoring. The ‘core model’ can be specified directly or based on a specified C-index and relaGve effects of (standardised) predictors. The joint distribuGon of predictors may be available directly in an exisGng dataset, in a pilot study, or in a syntheGc dataset provided by other researchers. Results We derive closed-form soluGons that decompose the variance of an individual’s esGmated event rate into Fisher’s unit informaGon matrix, predictor values and total sample size;this allows researchers to calculate and examine uncertainty distribuGons around individual risk esGmates and misclassificaGon probabiliGes for specified sample sizes. We provide an illustraGve example in breast cancer and emphasise the importance of clinical context, including any risk thresholds for decision making, and examine fairness concerns for pre- and post-menopausal women. Lastly, in two empirical evaluaGons, we provide reassurance that uncertainty interval widths based on our exponenGal approach are close to using more flexible parametric models. Conclusions Our approach

关键词： Fisher information matrix