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Journal of Applied Statistics & JAS: Enviro Stats

@JAppliedStats

Journal of Applied Statistics + Journal of Applied Statistics: Environmental Statistics and Data Science (new sister journal).

tandfonline.com/cjas
Joined September 2017
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Photo by Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats)Photo by Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats)Photo by Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats)Photo by Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats)Photo by Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats)Photo by Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats)Photo by Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats)Photo by Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats)Photo by Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats)Photo by Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats)
𝙎𝙪𝙗𝙢𝙞𝙩 𝙔𝙤𝙪𝙧 𝙍𝙚𝙨𝙚𝙖𝙧𝙘𝙝: Journal of Applied Statistics: Environmental Statistics and Data Science tandfonline.com/journals/tje…
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Short-term forecasting of human-caused wildland fire occurrence: a case study for two regions in Ontario, Canada @tandfSTEM @tandfenviro tandfonline.com/doi/full/10.…
Short-term forecasting of human-caused wildland fire occurrence: a case study for two regions in...

Short-term forecasting of human-caused wildland fire occurrence: a case study for two regions in...

Wildfire occurrence prediction (FOP) models are used as decision support tools by fire management agencies. However, there appears to be limited research on making predictions beyond the current da...

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Extreme temperatures on the Greenland ice sheet: a spatial model @tandfSTEM @tandfenviro #Greenland #SDG #Ice #ClimateChange #Statistics tandfonline.com/doi/full/10.…

Extreme temperatures on the Greenland ice sheet: a spatial model

Whilst surface ice melt from the Greenland ice sheet is one of the largest contributors to sea level rise, our understanding of this phenomenon arises mostly from case studies of specific melt even...

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Journal of Applied Statistics & JAS: Enviro Stats retweeted
Data Science in Science (@DataSciScience) profile picture
Data Science in Science@DataSciScienceMay 13
An Efficient, Accurate, and Interpretable Machine Learning Method for Computing Probability of Failure @tandfSTEM tandfonline.com/doi/full/10.…
An Efficient, Accurate, and Interpretable Machine Learning Method for Computing Probability of...

An Efficient, Accurate, and Interpretable Machine Learning Method for Computing Probability of...

We introduce a novel machine learning method called the Penalized Profile Support Vector Machine based on the Gabriel edited set for the computation of the probability of failure for a complex syst...

tandfonline.com
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Non-parametric analysis of transient data: a pseudo-competing event approach tandfonline.com/doi/full/10.…
Image from Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats)
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Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework tandfonline.com/doi/full/10.…
Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework

Modelling the term-structure of default risk under IFRS 9 within a multistate regression framework

The lifetime behaviour of loans is notoriously difficult to model, which can compromise a bank's financial reserves against future losses, if modelled poorly. Therefore, we present a data-driven co...

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Controlling FDR in selecting group-level simultaneous signals from multiple data sources with application to the National COVID Collaborative Cohort data tandfonline.com/doi/full/10.…
Controlling FDR in selecting group-level simultaneous signals from multiple data sources with...

Controlling FDR in selecting group-level simultaneous signals from multiple data sources with...

One challenge in exploratory association studies using observational data is that the associations between the predictors and the outcome are potentially weak and rare, and the candidate predictors...

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Journal of Applied Statistics & JAS: Enviro Stats retweeted
Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats) profile picture
Journal of Applied Statistics & JAS: Enviro Stats@JAppliedStats10 Sep 2025
The impact of AI-generated responses on environmental survey data from MTurk @tandfSTEM @tandfenviro #AI #data #survey #environment tandfonline.com/doi/full/10.…
The impact of AI-generated responses on environmental survey data from MTurk

The impact of AI-generated responses on environmental survey data from MTurk

This study investigates the impact of nonhuman, AI-generated responses on data quality in a climate change survey conducted via Amazon’s Mechanical Turk (MTurk). As MTurk remains a popular platform...

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Journal of Applied Statistics & JAS: Enviro Stats retweeted
Paul Naish (@PaulNaish78) profile picture
Paul Naish@PaulNaish7817 Oct 2025
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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Bayesian partially-protected regularization as a model selection tool tandfonline.com/doi/full/10.…
Bayesian partially-protected regularization as a model selection tool

Bayesian partially-protected regularization as a model selection tool

This work first describes Bayesian Partially-Protected Lasso (BPL), which combines the power of Bayesian Lasso with the ability to protect key theoretical explanatory variables from shrinkage to a ...

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Journal of Applied Statistics & JAS: Enviro Stats retweeted
Paul Naish (@PaulNaish78) profile picture
Paul Naish@PaulNaish7830 Sep 2025
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

Editorial

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

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Empirical determination of baseball eras: multivariate change point analysis in major league baseball tandfonline.com/doi/full/10.…
Empirical determination of baseball eras: multivariate change point analysis in major league...

Empirical determination of baseball eras: multivariate change point analysis in major league...

We use multivariate change point analysis methods to identify not only mean shifts but also changes in variance across a wide array of statistical time series. Our primary objective is to empirical...

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APCanalysis: an R package for identifying active factors using the APC method tandfonline.com/doi/full/10.…
APCanalysis: an R package for identifying active factors using the APC method

APCanalysis: an R package for identifying active factors using the APC method

Unreplicated two-level designs play an important role in screening experiments. Notably, Plackett–Burman designs (PBDs) and regular fractional factorial designs ( 2k−p) are commonly used for their ...

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The impact of AI-generated responses on environmental survey data from MTurk @tandfSTEM @tandfenviro #AI #data #survey #environment tandfonline.com/doi/full/10.…
The impact of AI-generated responses on environmental survey data from MTurk

The impact of AI-generated responses on environmental survey data from MTurk

This study investigates the impact of nonhuman, AI-generated responses on data quality in a climate change survey conducted via Amazon’s Mechanical Turk (MTurk). As MTurk remains a popular platform...

tandfonline.com
2
3
877
Non-parametric treatment effect bounds for left-censored outcomes: estimating the effect of herbicide use on 2,4-D exposure tandfonline.com/doi/full/10.…
Non-parametric treatment effect bounds for left-censored outcomes: estimating the effect of...

Non-parametric treatment effect bounds for left-censored outcomes: estimating the effect of...

Causal inference is concerned with defining and estimating the effect of a exposure on an outcome. For example, the Average Treatment Effect (ATE), a causal inference concept, is defined as the pop...

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Journal of Applied Statistics & JAS: Enviro Stats retweeted
Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats) profile picture
Journal of Applied Statistics & JAS: Enviro Stats@JAppliedStats4 Feb 2025
Clustering of recurrent events data tandfonline.com/doi/full/10.…
Clustering of recurrent events data

Clustering of recurrent events data

Nowadays data are often timestamped, thus, when analysing the events which may occur several times (recurrent events), it is desirable to model the whole dynamics of the counting process rather tha...

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Journal of Applied Statistics & JAS: Enviro Stats retweeted
Journal of Applied Statistics & JAS: Enviro Stats (@JAppliedStats) profile picture
Journal of Applied Statistics & JAS: Enviro Stats@JAppliedStats25 Jul 2025
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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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...

tandfonline.com
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Efficient spline orthogonal basis for representation of density functions tandfonline.com/doi/full/10.…
Efficient spline orthogonal basis for representation of density functions

Efficient spline orthogonal basis for representation of density functions

Probability density functions form a specific class of functional data objects with intrinsic properties of scale invariance and relative scale characterized by the unit integral constraint. The Ba...

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Bridging mechanistic and data-driven models: a novel framework for epidemic forecasting tandfonline.com/doi/full/10.…
Bridging mechanistic and data-driven models: a novel framework for epidemic forecasting

Bridging mechanistic and data-driven models: a novel framework for epidemic forecasting

Accurate epidemic prediction is vital for public health interventions. Traditional models like the Susceptible-Infected-Recovered (SIR) model offer mechanistic insights but struggle with complex ep...

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Forecasting hourly foodservice sales during geopolitical and economical disruption using zero-inflated mixed effects models tandfonline.com/doi/full/10.…
Forecasting hourly foodservice sales during geopolitical and economical disruption using zero-inf...

Forecasting hourly foodservice sales during geopolitical and economical disruption using zero-inf...

Accurate predictions of product sales are essential to the foodservice sector, for planning and saving of resources. In this paper, a zero-inflated negative binomial mixed-effects model with severa...

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