Peter S. Wilson (756333), Kay Vopel (117117) · Figshare · 2015
<p>A segmented regression model identified the distance from the northern farm boundary at which the color intensities of the sediment in Awakiriapa Bay, Waiheke Island, New Zealand started to decrease when heading towards the farm. The analysis was performed on data combined from the three transects (A) and separately for each transect (B–D). The grey shaded area shows the 95% confidence interval of the model and the vertical grey dashed line denotes the mussel farm boundary. Negative distances indicate points within the farm, that is, south of the northern farm boundary.</p>
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Ridwana Maher Manna (11558862), Md Hafizur Rahman (12802815), Tasnu Ara (12357634) et al. · Figshare · 2025
<div> Introduction The global Coronavirus disease (COVID-19) pandemic disrupted healthcare systems, reducing access to medical services. In Bangladesh, strict lockdowns, healthcare worker shortages, and resource diversion further strained the system. Despite these challenges, the impact on inpatient and outpatient service utilisation in Bangladesh remains unaddressed. This study explored the levels of inpatient admissions and outpatient visits in public healthcare facilities before and during COVID-19 pandemic in Bangladesh. Methods We conducted a cross-sectional secondary analysis of inpatient and outpatient data from all public hospitals collected via District Health Information System, version 2 (DHIS2) from January 2017 to June 2021. Using 2017-2019 as the baseline, we analysed healthcare utilisation indicators (outpatient visits and inpatient admissions) with descriptive and segmented Poisson regression to assess the impact of COVID-19 in 2020 and 2021. Results In 2020, outpatient visits and inpatient admissions significantly declined to 34.1 million and 37.5 million, respectively, from 47.6 million and 56.2 million in 2019. Segmented regression analysis confirmed these drops, especially in Dhaka (IRR = 0.62, p < 0.001) and Barisal (IRR = 0.69, p < 0.002) for outpatient visits, and in Dhaka (IRR = 0.64, p < 0.000) and Khulna (IRR = 0.70, p < 0.000) for inpatient admissions. In 2021, most divisions saw an increase in outpatient visit and inpatient admission numbers, with the lowest rebound in Sylhet. Conclusion The COVID-19 pandemic significantly reduced Outpatient Department (OPD) visits and Inpatient Department (IPD) admissions in Bangladesh in 2020, with partial recovery in 2021. To ensure sustained access to care, it is crucial to strengthen healthcare facilities and equip healthcare providers to be prepared for future pandemics or emergencies. </div>
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Nabarun Dasgupta · OSF Preprints (OSF Preprints) · 2025
Stata code and results in a Jupyter notebook
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Ridwana Maher Manna (11558862), Md Hafizur Rahman (12802815), Tasnu Ara (12357634) et al. · Figshare · 2025
<div> Introduction The global Coronavirus disease (COVID-19) pandemic disrupted healthcare systems, reducing access to medical services. In Bangladesh, strict lockdowns, healthcare worker shortages, and resource diversion further strained the system. Despite these challenges, the impact on inpatient and outpatient service utilisation in Bangladesh remains unaddressed. This study explored the levels of inpatient admissions and outpatient visits in public healthcare facilities before and during COVID-19 pandemic in Bangladesh. Methods We conducted a cross-sectional secondary analysis of inpatient and outpatient data from all public hospitals collected via District Health Information System, version 2 (DHIS2) from January 2017 to June 2021. Using 2017-2019 as the baseline, we analysed healthcare utilisation indicators (outpatient visits and inpatient admissions) with descriptive and segmented Poisson regression to assess the impact of COVID-19 in 2020 and 2021. Results In 2020, outpatient visits and inpatient admissions significantly declined to 34.1 million and 37.5 million, respectively, from 47.6 million and 56.2 million in 2019. Segmented regression analysis confirmed these drops, especially in Dhaka (IRR = 0.62, p < 0.001) and Barisal (IRR = 0.69, p < 0.002) for outpatient visits, and in Dhaka (IRR = 0.64, p < 0.000) and Khulna (IRR = 0.70, p < 0.000) for inpatient admissions. In 2021, most divisions saw an increase in outpatient visit and inpatient admission numbers, with the lowest rebound in Sylhet. Conclusion The COVID-19 pandemic significantly reduced Outpatient Department (OPD) visits and Inpatient Department (IPD) admissions in Bangladesh in 2020, with partial recovery in 2021. To ensure sustained access to care, it is crucial to strengthen healthcare facilities and equip healthcare providers to be prepared for future pandemics or emergencies. </div>
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Jian Liu, Shiying Wu, James V. Zidek · 1997
Abstract: This paper concerns segmented multivariate regression models, models which have different linear forms in different subdomains of the domain of an in-dependent variable. Without knowing that number and their boundaries, we first estimate the number of these subdomains using a modified Schwarz criterion. The estimated number of regions proves to be weakly consistent under fairly general conditions. We then estimate the subdomain boundaries (“thresholds”) and the regression coefficients within subdomains by minimizing the sum of squares of the residuals. We show that the threshold estimates converge (at rates, 1/n and n−1/2, respectively at the model’s threshold points of discontinuity and continuity) and that the regression coefficients as well as the residual variances are asymptotically normal. The basic condition on the error distribution required for the veracity of our asymptotic results is satisfied by any distribution with zero mean and a mo-ment generating function (having bounded second derivative around zero). As an illustration, a segmented bivariate regression model is fitted to real data and the relevance of the asymptotic results is examined via simulations. Key words and phrases: Asymptotic normality, consistency, local exponential bound-edness, rate of convergence, segmented multivariate regression. 1.
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Leslie Tricoche (21762983), Antonin Rovai (21762986), Salvatore Lo Bue (13239822) et al. · Figshare · 2025
This analysis shows the relationship between the scale scores (Z-score IE, Error-score IE, Pain score) and %Pro_disob, accounting for the Population factor (Civilians, Military). No Population effect was found, suggesting that the relationships between %Pro_disob and all scores were consistent across both populations (i.e.,a negative relationship between IE scores and %Pro_disob and a positive relationship between pain score and %Pro_disob).
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Shamia Hoque (700778), Brian J. Chen (9028709), Martin W. Schoen (9028712) et al. · Figshare · 2020
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Shamia Hoque (700778), Brian J. Chen (9028709), Martin W. Schoen (9028712) et al. · Figshare · 2020
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Xiangyu Zheng, Song Xi Chen · Acta Mathematica Sinica English Series · 2025
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Ganizani Mwale (21655886), Mpundu Makasa (173326) · Figshare · 2025
Segmented Regression Analysis - output of data analysis from the segmented regression analysis (Piecewise Regression).
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Charlie Pilgrim · The Journal of Open Source Software · 2021
Piecewise regression (also known as segmented regression, broken-line regression, or breakpoint analysis) fits a linear regression model to data that includes one or more breakpoints where the gradient changes. The piecewise-regression Python package uses the approach described by This easy-to-use package includes an automatic comprehensive statistical analysis that gives confidence intervals for all model variables and hypothesis testing for the existence of breakpoints.
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Pilgrim, Charlie · Zenodo (CERN European Organization for Nuclear Research) · 2021
What's Changed Create LICENSE.txt by @chasmani in https://github.com/chasmani/piecewise-regression/pull/1 Edit suggestions by @galessiorob in https://github.com/chasmani/piecewise-regression/pull/2 New Contributors @chasmani made their first contribution in https://github.com/chasmani/piecewise-regression/pull/1 @galessiorob made their first contribution in https://github.com/chasmani/piecewise-regression/pull/2 <strong>Full Changelog</strong>: https://github.com/chasmani/piecewise-regression/commits/v1.2.0
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Alex Kinetic · Zenodo (CERN European Organization for Nuclear Research) · 2025
This project implements the Segmented Linear Regression Model (SLRM), an alternative to traditional Artificial Neural Networks (ANNs). The SLRM models datasets with piecewise linear functions, using a neural compression process to reduce complexity without compromising precision beyond a user-defined tolerance. The core of the solution is the compression algorithm, which transforms an unordered dataset (DataFrame / X, Y) into a final, highly optimized dictionary, ready for prediction.
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Alex Kinetic · Zenodo (CERN European Organization for Nuclear Research) · 2025
This project implements the Segmented Linear Regression Model (SLRM), an alternative to traditional Artificial Neural Networks (ANNs). The SLRM models datasets with piecewise linear functions, using a neural compression process to reduce complexity without compromising precision beyond a user-defined tolerance.
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Alex Kinetic · Zenodo (CERN European Organization for Nuclear Research) · 2025
This project implements the Segmented Linear Regression Model (SLRM), an alternative to traditional Artificial Neural Networks (ANNs). The SLRM models datasets with piecewise linear functions, using a neural compression process to reduce complexity without compromising precision beyond a user-defined tolerance. The core of the solution is the compression algorithm, which transforms an unordered dataset (DataFrame / X, Y) into a final, highly optimized dictionary, ready for prediction.
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Bucci, A., Faragalli, A., Skrami, E. et al. · U-PAD Unimc - Open Digital Publications (University of Macerata) · 2019
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Brian G. Healey · Memorial University Research Repository (Memorial University) · 2004
Robust estimators are developed for the segmented regression model, a model \nconsisting of two linear segments separated by a change-point. Julious (2001) \nintroduced a method to estimate parameters in the case of unknown changepoint. \nThe focus of this practicum is on robustifying the Julious algorithm \nvia iteratively re-weighting, extending the work of Julious (2001). Simulation \nstudies are conducted to assess the performance of the iterative re-weighting. \nThe methods are applied to a physiological data set studied by Julious, and \nto two stock-recruit data sets from fisheries science .
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Rare & Special e-Zone (The Hong Kong University of Science and Technology) · 2011
Elsevier’s Scopus, the largest abstract and citation database of peer-reviewed literature. Search and access research from the science, technology, medicine, social sciences and arts and humanities fields.
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Leslie Tricoche (21762983), Antonin Rovai (21762986), Salvatore Lo Bue (13239822) et al. · Figshare · 2025
A: Segmented regression model analysis between the subjective feelings (feeling of responsibility, feeling of guilt, feeling of sadness, perception of shock’s painfulness) and %Pro_disob, taking the Population factor (Civilians, Military) into account. Except for the feeling of responsibility, a Population effect was found for the majority of the investigated models suggesting that the positive relationship between %Pro_disob and the subjective feelings, was stronger in military participants than civilians. These plots concern the Subjective measure ~ %Pro_disob x Population model. B: Segmented regression model analysis between the disobedience criteria (morality, sensitivity, education) and %Pro_disob, taking the Population factor (Civilians, Military) into account. A Population effect was only found for sensitivity criterion suggesting that the positive relationship between %Pro_disob and the sensitivity criterion was stronger in military than civilians. Morality and education criteria appeared to be positively associated with %Pro_disob in a similar way between civilians and military participants. These plots concern the Subjective measure ~ %Pro_disob x Population model. (DOCX)
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Chuanwen Yu (18692960), Mengmeng Liu (501348), Hong Yang (37298) et al. · Figshare · 2024
<div><p>How to improve the success of new project development through the collection of resources in the preliminary stages of new project development is a new area of research. Given the speed and magnitude of changes in the folk sports market environment, this study analyses the impact of tapping folk tourism resources on sports projects at the project development stage. Through stratified regression analyses of 600 Chinese firms engaged in folk sports project development, this paper finds that tapping internal tourism resources positively affects the Fuzzy Front-End Performance of incremental innovative project development. In contrast, tapping external tourism resources positively affects the Fuzzy Front-End Performance of breakthrough innovation projects. The study also indicates that the speed of environmental change (SEC) positively moderates the relationship between exploring external tourism resources (ERS) and Fuzzy Front-End Performance of incremental innovation projects. In contrast, the magnitude of environmental change (MEC) negatively moderated the relationship between internal tourism resource exploration (IRS) and the FFE-P of similar projects.</p></div>
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