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Statistics

@Statistics_Jour

A world-leading journal exploring all aspects of theoretical and applied statistics.

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Joined March 2020
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Paul Naish@PaulNaish78May 6
Exact optimal statistical inference for the coefficient of determination @Statistics_Jour tandfonline.com/doi/full/10.…
Exact optimal statistical inference for the coefficient of determination

Exact optimal statistical inference for the coefficient of determination

The coefficient of determination is a popular goodness-of-fit measure in linear regression analysis. Despite its widespread popularity in applied statistics, the statistical inference of the coeffi...

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Paul Naish@PaulNaish78May 12
Quantum geometric-entropic optimization for customer lifetime value prediction: convergence theory and an empirical study on transactional retail data @Statistics_Jour tandfonline.com/doi/full/10.…
Quantum geometric-entropic optimization for customer lifetime value prediction: convergence theory...

Quantum geometric-entropic optimization for customer lifetime value prediction: convergence theory...

Predicting customer churn from transactional data is a central problem in management science, with direct implications for retention strategy, revenue forecasting, and resource allocation. This pap...

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Statistics retweeted
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Paul Naish@PaulNaish78May 15
Goodness-of-fit tests or measuring contamination fractions? @Statistics_Jour tandfonline.com/doi/full/10.…
Goodness-of-fit tests or measuring contamination fractions?

Goodness-of-fit tests or measuring contamination fractions?

This study presents a new approach for assessing contaminated numerical data. We consider the divergence between an empirical distribution and the Benford or any non-Benford distribution. There exi...

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Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study tandfonline.com/doi/full/10.…
Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study

Which Imputation Fits Which Feature Selection Method? A Survey-Based Simulation Study

Tree-based learning methods, such as Random Forest and XGBoost, remain the gold standard for prediction in tabular data. Feature importance measures are frequently employed in feature selection and...

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JAS: ESDS - now online to read and submit! xitter.gq/JAppliedStats/status/1…
Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats) profile pictureJournal of Applied Statistics & JAS: Enviro Stats@JAppliedStats4 Oct 2024
𝙎𝙪𝙗𝙢𝙞𝙩 𝙔𝙤𝙪𝙧 𝙍𝙚𝙨𝙚𝙖𝙧𝙘𝙝: Journal of Applied Statistics: Environmental Statistics and Data Science tandfonline.com/journals/tje…
Image from Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats)
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New Journal alert - ASA Discoveries! New OA journal from the American Statistical Association and published by @tandfonline @tandfSTEM Check out the opening Editorial below! ⬇️⬇️⬇️⬇️⬇️⬇️⬇️ tandfonline.com/doi/full/10.…

Editorial

Published in ASA Discoveries (Vol. 1, No. 1, 2025)

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SeasCen, A Python-Based Platform for Time Series Modeling and Seasonal Adjustment tandfonline.com/doi/full/10.…
SeasCen, A Python-Based Platform for Time Series Modeling and Seasonal Adjustment

SeasCen, A Python-Based Platform for Time Series Modeling and Seasonal Adjustment

Whereas X-13ARIMA-SEATS (X-13) is widely used around the world to seasonally adjust economic time series, its continued longevity is jeopardized by the ongoing difficulty of maintaining its FORTRAN...

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Optimal group sizes for testing group mean differences using the Bayes factor tandfonline.com/doi/full/10.…
Optimal group sizes for testing group mean differences using the Bayes factor

Optimal group sizes for testing group mean differences using the Bayes factor

Determination of group sizes is an important issue when planning a study that aims to compare mean outcomes across groups. Using equal group sizes is not the best choice in the case of heterogeneou...

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Great news! Data Science in Science has been accepted into Scopus! @tandfSTEM #Scopus #DataScience
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Opening Editorial for JAS:ESDS now online! Introducing Journal of Applied Statistics: Environmental Statistics and Data Science @tandfSTEM @tandfonline #statistics #datascience #environment #climate tandfonline.com/doi/full/10.…
Introducing Journal of Applied Statistics: Environmental Statistics and Data Science

Introducing Journal of Applied Statistics: Environmental Statistics and Data Science

Published in Journal of Applied Statistics: Environmental Statistics and Data Science (Vol. 1, No. 1-4, 2025)

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Influence of the slope in percentile estimation through binary regression for dose-finding experiments @Statistics_Jour tandfonline.com/doi/full/10.…
Influence of the slope in percentile estimation through binary regression for dose-finding experi...

Influence of the slope in percentile estimation through binary regression for dose-finding experi...

Dose-finding experiments aim to estimate the dose having a specified proportion of positive responses by collecting data in the vicinity of this unknown target dose. The importance of estimating th...

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New Article Collection (free to read!): 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗮𝗻𝗱 𝗦𝘁𝗮𝘁𝗶𝘀𝘁𝗶𝗰𝗮𝗹 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 tandfonline.com/journals/gst…
Machine learning and Statistical learning

Machine learning and Statistical learning

Explore the article collection: Machine learning and Statistical learning. Published in Statistics.

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I asked Prof. Badi Baltagi to interview Prof. Essie Maasoumi in recognition of his remarkable 37 years of service as editor. Thank you, Badi, for conducting such a wonderful interview, and thank you, Essie, for your unwavering dedication. tandfonline.com/doi/full/10.…
ER interview: Essie Maasoumi, editor of Econometric Reviews (1987–2024)

ER interview: Essie Maasoumi, editor of Econometric Reviews (1987–2024)

Published in Econometric Reviews (Vol. 44, No. 1, 2025)

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Evaluating SAFE AI principles using Wasserstein distance: a comparative study of Machine Learning models tandfonline.com/doi/full/10.…
Evaluating SAFE AI principles using Wasserstein distance: a comparative study of Machine Learning...

Evaluating SAFE AI principles using Wasserstein distance: a comparative study of Machine Learning...

Modern data-driven Artificial Intelligence (AI), powered by advanced Machine Learning (ML) models, is transforming financial technologies by enhancing financial inclusion, transparency and reducing...

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Five or Ten New Proofs of the Pythagorean Theorem @maanow @tandfSTEM tandfonline.com/doi/full/10.…
Five or Ten New Proofs of the Pythagorean Theorem

Five or Ten New Proofs of the Pythagorean Theorem

We present five trigonometric proofs of the Pythagorean theorem, and our method for finding proofs (Section 5) yields at least five more.

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Evaluating SAFE AI principles using Wasserstein distance: a comparative study of Machine Learning models tandfonline.com/doi/full/10.…
Evaluating SAFE AI principles using Wasserstein distance: a comparative study of Machine Learning...

Evaluating SAFE AI principles using Wasserstein distance: a comparative study of Machine Learning...

Modern data-driven Artificial Intelligence (AI), powered by advanced Machine Learning (ML) models, is transforming financial technologies by enhancing financial inclusion, transparency and reducing...

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Spend the day with one of our Portfolio Managers! @MCM_tf is one of our PMs in our Physical Science & Engineering team, handling 20 different statistics and data science journals🧑‍🔬 Here's what a typical day looks like for her 😊🐶☕💻 #AcadmicChatter #AcademicPublishing
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New article! 𝗦𝗼𝗹𝗮𝗿 𝗜𝗺𝗮𝗴𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀: 𝗔 𝗦𝗲𝗹𝗲𝗰𝘁𝗶𝘃𝗲 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄 𝗼𝗳 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 𝗮𝗻𝗱 𝗢𝗽𝗽𝗼𝗿𝘁𝘂𝗻𝗶𝘁𝗶𝗲𝘀 #Space #Data #Imaging tandfonline.com/doi/full/10.…
Solar Imaging Data Analytics: A Selective Overview of Challenges and Opportunities

Solar Imaging Data Analytics: A Selective Overview of Challenges and Opportunities

We give a gentle introduction to solar imaging data, focusing on the challenges and opportunities of data-driven approaches for solar eruptions. We present various solar phenomena prediction proble...

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Developing predictive precision medicine models by exploiting real-world data using machine learning methods tandfonline.com/doi/full/10.…
Developing predictive precision medicine models by exploiting real-world data using machine...

Developing predictive precision medicine models by exploiting real-world data using machine...

Computational Medicine encompasses the application of Statistical Machine Learning and Artificial Intelligence methods on several traditional medical approaches, including biochemical testing which...

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𝙎𝙪𝙗𝙢𝙞𝙩 𝙔𝙤𝙪𝙧 𝙍𝙚𝙨𝙚𝙖𝙧𝙘𝙝: Journal of Applied Statistics: Environmental Statistics and Data Science tandfonline.com/journals/tje…
Image from Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats)
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