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Journal articles on the topic 'Fractional regression'

Author: Grafiati

Published: 7 September 2021

Last updated: 29 July 2025

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1

Awadalla, Muath, Yves Yannick Yameni Noupoue, Yucel Tandogdu, and Kinda Abuasbeh. "Regression Coefficient Derivation via Fractional Calculus Framework." Journal of Mathematics 2022 (April 9, 2022): 1–9. http://dx.doi.org/10.1155/2022/1144296.

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This study focuses on deriving coefficients of a simple linear regression model and a quadratic regression model using fractional calculus. The work has proven that there is a smooth connection between fractional operators and classical operators. Moreover, it has also been shown that the least squares method is classically used to obtain coefficients of linear and quadratic models that are viewed as special cases of the more general fractional derivative approach which is proposed.

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2

Murteira, José M. R., and Joaquim J. S. Ramalho. "Regression Analysis of Multivariate Fractional Data." Econometric Reviews 35, no. 4 (2014): 515–52. http://dx.doi.org/10.1080/07474938.2013.806849.

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3

Chatterjee, Aditya, Subho Sankar Sarkar, and Suvas Nandi. "Petrological Mixing A Regression Approach." Calcutta Statistical Association Bulletin 50, no. 1-2 (2000): 79–94. http://dx.doi.org/10.1177/0008068320000108.

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The geochemical processes of fractional crystallization of a magma, partial fusion of a rock and assimilation or hybridization of rock(s) and/or magma(s) are generally termed as petrological mixing process. In the present paper a unified attempt has been made to describe the three processes under the purview of regression model. As the data involved are essentially compositional in nature, their suitable log-ratio transforms have been utilized and the constrained least squares principle has been applied to reach a meaningful solution. A highly accommodative procedure is suggested so as to desc

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4

Wulff, Jesper N. "Generalized two-part fractional regression with cmp." Stata Journal: Promoting communications on statistics and Stata 19, no. 2 (2019): 375–89. http://dx.doi.org/10.1177/1536867x19854017.

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Researchers who model fractional dependent variables often need to consider whether their data were generated by a two-part process. Two-part models are ideal for modeling two-part processes because they allow us to model the participation and magnitude decisions separately. While community-contributed commands currently facilitate estimation of two-part models, no specialized command exists for fitting two-part models with process dependency. In this article, I describe generalized two-part fractional regression, which allows for dependency between models’ parts. I show how this model can be

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5

Zhang, Zhongheng. "Multivariable fractional polynomial method for regression model." Annals of Translational Medicine 4, no. 9 (2016): 174. http://dx.doi.org/10.21037/atm.2016.05.01.

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6

Villadsen, Anders Ryom, and Jesper Wulff. "Fractional Regression Models in Strategic Management Research." Academy of Management Proceedings 2018, no. 1 (2018): 11217. http://dx.doi.org/10.5465/ambpp.2018.53.

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7

Schmidt-Hieber, Johannes. "Asymptotic equivalence for regression under fractional noise." Annals of Statistics 42, no. 6 (2014): 2557–85. http://dx.doi.org/10.1214/14-aos1262.

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8

Tang, Shaowu, Jong-Hyeon Jeong, and Chi Song. "Fractional logistic regression for censored survival data." Journal of Statistical Research 51, no. 2 (2018): 101–14. http://dx.doi.org/10.47302/jsr.2017510201.

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In the analysis of time-to-event data, e.g. from cancer studies, the group effect of main interest such as treatment effect of a chemo-therapy often needs to be adjusted by confounding factors (possibly continuous) such as hormonal receptor status, age at diagnosis, and pathological tumor size, when the study outcome is affected by their imbalanced distributions across the comparison groups. The median, or quantile, is a popular summary measure for censored survival data due to its robustness. In this paper, first the logistic regression is extended to fractional responses transformed from cen

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9

Paik, Minhui, and Michael D. Larsen. "Fractional Regression Hot Deck Imputation Weight Adjustment." Communications in Statistics - Simulation and Computation 42, no. 7 (2013): 1514–32. http://dx.doi.org/10.1080/03610918.2012.667475.

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10

Cang, Yuquan, Junfeng Liu, and Yan Zhang. "Nonparametric Regression with Subfractional Brownian Motion via Malliavin Calculus." Abstract and Applied Analysis 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/635917.

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We study the asymptotic behavior of the sequenceSn=∑i=0n-1K(nαSiH1)(Si+1H2-SiH2),asntends to infinity, whereSH1andSH2are two independent subfractional Brownian motions with indicesH1andH2, respectively.Kis a kernel function and the bandwidth parameterαsatisfies some hypotheses in terms ofH1andH2. Its limiting distribution is a mixed normal law involving the local time of the sub-fractional Brownian motionSH1. We mainly use the techniques of Malliavin calculus with respect to sub-fractional Brownian motion.

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11

Ishola, Taiwo Abass, Olatayo Timothy Olabisi, and Adesanya Kazeem Kehinde. "Parameter Estimation of Fractional Trigonometric Polynomial Regression Model." JOURNAL OF UNIVERSITY OF BABYLON for pure and applied sciences 27, no. 1 (2019): 519–26. http://dx.doi.org/10.29196/jubpas.v27i1.2208.

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Trigonometric Polynomial Regression is a form of non-linear regression in which the relationship between the outcome variable and risk variable is Fractional modeled as 1/nth degree polynomial regression by combining the function of cos(nx) and sin(nx) on the value of natural numbers. The model was used to analyze the relationship between three continuous and periodic variables. Coefficients of the model were estimated using the Maximum Likelihood Estimate (MLE) method. From the results, the model obtained indicated that an increased in body mass index will increase the level of blood pressure

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12

Royston, Patrick, and Douglas G. Altman. "Approximating statistical functions by using fractional polynomial regression." Journal of the Royal Statistical Society: Series D (The Statistician) 46, no. 3 (1997): 411–22. http://dx.doi.org/10.1111/1467-9884.00093.

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13

Sun, Yixiao, and Peter C. B. Phillips. "Nonlinear log-periodogram regression for perturbed fractional processes." Journal of Econometrics 115, no. 2 (2003): 355–89. http://dx.doi.org/10.1016/s0304-4076(03)00115-5.

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14

Duggleby, Ronald G. "Analysis of fractional turnover rates by exponential regression." Computer Methods and Programs in Biomedicine 37, no. 1 (1992): 65–66. http://dx.doi.org/10.1016/0169-2607(92)90030-b.

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15

Gerolimetto, Margherita. "Frequency domain bootstrap for the fractional cointegration regression." Economics Letters 91, no. 3 (2006): 389–94. http://dx.doi.org/10.1016/j.econlet.2005.12.015.

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16

Kalantari, Robab, Khashayar Rahimi, and Saman Naderi Mezajin. "Multi-Fractional Gradient Descent: A Novel Approach to Gradient Descent for Robust Linear Regression." Engineering World 6 (October 14, 2024): 118–27. http://dx.doi.org/10.37394/232025.2024.6.12.

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Authors: This work introduces a novel gradient descent method by generalizing the fractional gradient descent (FGD) such that instead of the same fractional order for all variables, we assign different fractional orders to each variable depending on its characteristics and its relation to other variables. We name this method Multi-Fractional Gradient Descent (MFGD) and by using it in linear regression for minimizing loss function (residual sum of square) and apply it on four financial time series data and also tuning their hyperparameters, we can observe that unlike GD and FGD, MFGD is robust

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17

Torres-Hernandez, Anthony, Rafael Ramirez-Melendez, and Fernando Brambila-Paz. "Proposal for the Application of Fractional Operators in Polynomial Regression Models to Enhance the Determination Coefficient R2 on Unseen Data." Fractal and Fractional 9, no. 6 (2025): 393. https://doi.org/10.3390/fractalfract9060393.

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Since polynomial regression models are generally quite reliable for data that can be handled using a linear system, it is important to note that in some cases, they may suffer from overfitting during the training phase. This can lead to negative values of the coefficient of determination R2 when applied to unseen data. To address this issue, this work proposes the partial implementation of fractional operators in polynomial regression models to construct a fractional regression model. The aim of this approach is to mitigate overfitting, which could potentially improve the R2 value for unseen d

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18

Djokoto, Justice G., Francis Y. Srofenyo, and Akua A. Afrane Arthur. "Technical Inefficiency Effects in Agriculture—A Meta-Regression." Journal of Agricultural Science 8, no. 2 (2016): 109. http://dx.doi.org/10.5539/jas.v8n2p109.

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<p>A number of studies have examined the effect of study characteristics on mean technical efficiency as the dependent variable. This article departs from these earlier studies by using second-stage inefficiency covariates as key exploratory variables and study characteristics as control variables in a meta-regression. Unlike the vote count method of quantitative review, the parameters of the key variables have desirable properties and enable statistical inferences to be drawn. Additionally, the dependent variable employed is mean technical inefficiency. This is demonstrated using data o

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19

Sauerbrei, W., and P. Royston. "Building Multivariable Regression Models with Continuous Covariates in Clinical Epidemiology." Methods of Information in Medicine 44, no. 04 (2005): 561–71. http://dx.doi.org/10.1055/s-0038-1634008.

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Summary Objectives: In fitting regression models, data analysts must often choose a model based on several candidate predictor variables which may influence the outcome. Most analysts either assume a linear relationship for continuous predictors, or categorize them and postulate step functions. By contrast, we propose to model possible non-linearity in the relationship between the outcome and several continuous predictors by estimating smooth functions of the predictors. We aim to demonstrate that a structured approach based on fractional polynomials can give a broadly satisfactory practical s

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20

Barsnes, Harald, Ingvar Eidhammer, Véronique Cruciani, and Svein-Ole Mikalsen. "Protease-Dependent Fractional Mass and Peptide Properties." European Journal of Mass Spectrometry 14, no. 5 (2008): 311–17. http://dx.doi.org/10.1255/ejms.934.

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Mass spectrometric analyses of peptides mainly rely on cleavage of proteins with proteases that have a defined specificity. The specificities of the proteases imply that there is not a random distribution of amino acids in the peptides. The physico–chemical effects of this distribution have been partly analyzed for tryptic peptides, but to a lesser degree for other proteases. Using all human proteins in Swiss-Prot, the relationships between peptide fractional mass, pI and hydrophobicity were investigated. The distribution of the fractional masses and the average regression lines for the fracti

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21

KOÇ, TUBA, EMRE DÜNDER, and HAYDAR KOÇ. "FRACTIONAL REGRESSION MODEL FOR INVESTIGATING THE DETERMINANTS OF THE UNEMPLOYMENT RATES IN OECD COUNTRIES." Journal of Science and Arts 21, no. 2 (2021): 423–28. http://dx.doi.org/10.46939/j.sci.arts-21.2-a09.

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Unemployment is a serious problem for all over the world. It is a crucial task to endeavor with the unemployment for the welfare of the world. Once, the potential factors should be known to accomplish this task. The aim of this study is to investigate the determinants of the unemployment rates using fractional regression models for the 35 OECD (Organization for Economic Co-operation and Development) countries over the periods 2000-2017. We determined the factor affecting the unemployment rate by the fractional regression model using GMMbgw and GMMpre estimators for panel data. The empirical re

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22

Walker, Esteban. "Influence diagnostics for fractional principal components estimators in regression." Communications in Statistics - Simulation and Computation 19, no. 3 (1990): 919–33. http://dx.doi.org/10.1080/03610919008812898.

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23

Bourguin, Solesne, and Ciprian A. Tudor. "Asymptotic Theory for Fractional Regression Models via Malliavin Calculus." Journal of Theoretical Probability 25, no. 2 (2010): 536–64. http://dx.doi.org/10.1007/s10959-010-0302-y.

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24

Ramalho, Esmeralda A., Joaquim J. S. Ramalho, and Pedro D. Henriques. "Fractional regression models for second stage DEA efficiency analyses." Journal of Productivity Analysis 34, no. 3 (2010): 239–55. http://dx.doi.org/10.1007/s11123-010-0184-0.

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25

Liu, Juxin, and Paul Gustafson. "Regression with fractional polynomials when interactions are erroneously omitted." Journal of Statistical Planning and Inference 142, no. 6 (2012): 1348–55. http://dx.doi.org/10.1016/j.jspi.2011.12.012.

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26

Ding, Qile, Yiren Wang, Yu Zheng, et al. "Subsurface Geological Profile Interpolation Using a Fractional Kriging Method Enhanced by Random Forest Regression." Fractal and Fractional 8, no. 12 (2024): 717. https://doi.org/10.3390/fractalfract8120717.

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Analyzing geological profiles is of great importance for various applications such as natural resource management, environmental assessment, and mining engineering projects. This study presents a novel geostatistical approach for subsurface geological profile interpolation using a fractional kriging method enhanced by random forest regression. Using bedrock elevation data from 49 boreholes in a study area in southeast China, we first use random forest regression to predict and optimize variogram parameters. We then use the fractional kriging method to interpolate the data and analyze the varia

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27

Adams, Larry G., David J. Polzin, Carl A. Osborne, and Timothy D. O’Brien. "Comparison of fractional excretion and 24-hour urinary excretion of sodium and potassium in clinically normal cats and cats with induced chronic renal failure." American Journal of Veterinary Research 52, no. 5 (1991): 718–22. http://dx.doi.org/10.2460/ajvr.1991.52.05.718.

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SUMMARY The influence of induced chronic renal failure on 24- hour urinary excretion and fractional excretion of sodium and potassium was studied in cats. Induction of chronic renal failure significantly increased fractional excretion of potassium (P < 0.0001) and sodium (P < 0.05); however, 24-hour urinary excretion of sodium and potassium decreased slightly following induction of chronic renal failure. Fractional excretion and 24-hour urinary excretion of sodium and potassium were compared by linear regression in clinically normal cats, cats with chronic renal failure, and clinically n

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28

Gerke, Oke, and Sören Möller. "Modeling Bland–Altman Limits of Agreement with Fractional Polynomials—An Example with the Agatston Score for Coronary Calcification." Axioms 12, no. 9 (2023): 884. http://dx.doi.org/10.3390/axioms12090884.

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Bland–Altman limits of agreement are very popular in method comparison studies on quantitative outcomes. However, a straightforward application of Bland–Altman analysis requires roughly normally distributed differences, a constant bias, and variance homogeneity across the measurement range. If one or more assumptions are violated, a variance-stabilizing transformation (e.g., natural logarithm, square root) may be sufficient before Bland–Altman analysis can be performed. Sometimes, fractional polynomial regression has been used when the choice of variance-stabilizing transformation was unclear

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29

Cheng, Jinpeng, Guijun Yang, Weimeng Xu, et al. "Improving the Estimation of Apple Leaf Photosynthetic Pigment Content Using Fractional Derivatives and Machine Learning." Agronomy 12, no. 7 (2022): 1497. http://dx.doi.org/10.3390/agronomy12071497.

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As a key functional trait, leaf photosynthetic pigment content (LPPC) plays an important role in the health status monitoring and yield estimation of apples. Hyperspectral features including vegetation indices (VIs) and derivatives are widely used in retrieving vegetation biophysical parameters. The fractional derivative spectral method shows great potential in retrieving LPPC. However, the performance of fractional derivatives and machine learning (ML) for retrieving apple LPPC still needs to be explored. The objective of this study is to test the capacity of using fractional derivative and M

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30

Royston, Patrick, and Douglas G. Altman. "Regression Using Fractional Polynomials of Continuous Covariates: Parsimonious Parametric Modelling." Applied Statistics 43, no. 3 (1994): 429. http://dx.doi.org/10.2307/2986270.

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31

Lee, Wonwoo, and Jeffrey B. Birch. "Fractional principal components regression: a general approach to biased estimators." Communications in Statistics - Simulation and Computation 17, no. 3 (1988): 713–27. http://dx.doi.org/10.1080/03610918808812689.

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32

Ramalho, Esmeralda A., Joaquim J. S. Ramalho, and José M. R. Murteira. "ALTERNATIVE ESTIMATING AND TESTING EMPIRICAL STRATEGIES FOR FRACTIONAL REGRESSION MODELS." Journal of Economic Surveys 25, no. 1 (2011): 19–68. http://dx.doi.org/10.1111/j.1467-6419.2009.00602.x.

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33

Abry, Patrice, and Gustavo Didier. "Wavelet eigenvalue regression for n-variate operator fractional Brownian motion." Journal of Multivariate Analysis 168 (November 2018): 75–104. http://dx.doi.org/10.1016/j.jmva.2018.06.007.

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34

Demetrescu, Matei, and Adina I. Tarcolea. "Bias correction for the regression-based LM fractional integration test." AStA Advances in Statistical Analysis 92, no. 1 (2008): 91–99. http://dx.doi.org/10.1007/s10182-008-0058-1.

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35

Thavaneswaran, A., and S. Peiris. "Recursive estimation for regression with infinite variance fractional ARIMA noise." Mathematical and Computer Modelling 34, no. 9-11 (2001): 1133–37. http://dx.doi.org/10.1016/s0895-7177(01)00121-2.

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36

Bayes, Cristian L., and Luis Valdivieso. "A beta inflated mean regression model for fractional response variables." Journal of Applied Statistics 43, no. 10 (2016): 1814–30. http://dx.doi.org/10.1080/02664763.2015.1120711.

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37

Shen, Guangjun, and Litan Yan. "Asymptotic behavior for bi-fractional regression models via Malliavin calculus." Frontiers of Mathematics in China 9, no. 1 (2013): 151–79. http://dx.doi.org/10.1007/s11464-013-0312-z.

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38

Mai, The Tien. "On properties of fractional posterior in generalized reduced-rank regression." Journal of Multivariate Analysis 210 (November 2025): 105481. https://doi.org/10.1016/j.jmva.2025.105481.

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39

Valerii, Tytiuk, Chornyi Oleksii, Baranovskaya Mila та ін. "SYNTHESIS OF A FRACTIONAL-ORDER PIΛDΜ-CONTROLLER FOR A CLOSED SYSTEM OF SWITCHED RELUCTANCE MOTOR CONTROL". Eastern-European Journal of Enterprise Technologies 2, № 2 (98) (2019): 35–42. https://doi.org/10.15587/1729-4061.2019.160946.

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The relevance of creating high-quality control systems for electric drives with a switched reluctance motor (SRM) was substantiated. Using methods of mathematical modeling, transient characteristics of the process of turn-on of SRMs with various moments of inertia were obtained. Based on analysis of the obtained transient characteristics, features of the SRM turn-on process determined by dynamic change of parameters of the SRM during its turn-on were shown. Low accuracy of SRM identification using a fractionally rational function of rat34 class was shown. Regression coefficient of the resultin

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40

Badík, A., and M. Fečkan. "Applying fractional calculus to analyze final consumption and gross investment influence on GDP." Journal of Applied Mathematics, Statistics and Informatics 17, no. 1 (2021): 65–72. http://dx.doi.org/10.2478/jamsi-2021-0004.

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Abstract This paper points out the possibility of suitable use of Caputo fractional derivative in regression model. Fitting historical data using a regression model seems to be useful in many fields, among other things, for the short-term prediction of further developments in the state variable. Therefore, it is important to fit the historical data as accurately as possible using the given variables. Using Caputo fractional derivative, this accuracy can be increased in the model described in this paper.

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41

Feng, Ruyi, Lizhe Wang, and Yanfei Zhong. "Least Angle Regression-Based Constrained Sparse Unmixing of Hyperspectral Remote Sensing Imagery." Remote Sensing 10, no. 10 (2018): 1546. http://dx.doi.org/10.3390/rs10101546.

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Sparse unmixing has been successfully applied in hyperspectral remote sensing imagery analysis based on a standard spectral library known in advance. This approach involves reformulating the traditional linear spectral unmixing problem by finding the optimal subset of signatures in this spectral library using the sparse regression technique, and has greatly improved the estimation of fractional abundances in ubiquitous mixed pixels. Since the potentially large standard spectral library can be given a priori, the most challenging task is to compute the regression coefficients, i.e., the fractio

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42

Arezza, N. J. J., D. H. Y. Tse, and C. A. Baron. "Rapid microscopic fractional anisotropy imaging via an optimized linear regression formulation." Magnetic Resonance Imaging 80 (July 2021): 132–43. http://dx.doi.org/10.1016/j.mri.2021.04.015.

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43

Stavrunova, Olena, and Oleg Yerokhin. "Two-part fractional regression model for the demand for risky assets†." Applied Economics 44, no. 1 (2012): 21–26. http://dx.doi.org/10.1080/00036846.2010.498366.

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44

Arakaki, Lorilee S. L., and David H. Burns. "Multispectral Analysis for Quantitative Measurements of Myoglobin Oxygen Fractional Saturation in the Presence of Hemoglobin Interference." Applied Spectroscopy 46, no. 12 (1992): 1919–28. http://dx.doi.org/10.1366/0003702924123412.

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Quantitative values for myoglobin oxygen fractional saturation were extracted from visible absorption spectra of myoglobin and hemoglobin solutions by analysis with three algorithms: classical least-squares, partial least-squares, and stagewise multiple linear regression. In an effort to mimic in vivo conditions, oxygen tensions and concentrations of myoglobin and hemoglobin solutions in separate cuvettes were varied independently. Transmission measurements were made through both cuvettes so that spectra contained contributions from both myoglobin and hemoglobin. Oxygen tensions in the myoglob

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45

Royston, Patrick. "Model Selection for Univariable Fractional Polynomials." Stata Journal: Promoting communications on statistics and Stata 17, no. 3 (2017): 619–29. http://dx.doi.org/10.1177/1536867x1701700305.

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Since Royston and Altman's 1994 publication ( Journal of the Royal Statistical Society, Series C 43: 429–467), fractional polynomials have steadily gained popularity as a tool for flexible parametric modeling of regression relationships. In this article, I present fp_select, a postestimation tool for fp that allows the user to select a parsimonious fractional polynomial model according to a closed test procedure called the fractional polynomial selection procedure or function selection procedure. I also give a brief introduction to fractional polynomial models and provide examples of using fp

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46

Schmitt, Joseph M. "Fractional Derivative Analysis of Diffuse Reflectance Spectra." Applied Spectroscopy 52, no. 6 (1998): 840–46. http://dx.doi.org/10.1366/0003702981944580.

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Fractional differentiation is introduced as a mathematical tool for analysis of diffuse-reflectance spectra. The quantity —log10 e/R dqR/ dλ q, where R is the measured reflectance and q is a real number greater than zero, is defined and shown to have properties analogous to those of the integer-order derivatives of log10(1/ R) that are commonly employed in near-infrared spectroscopy. Like conventional derivative spectroscopy, fractional derivative spectroscopy (FDS) is effective for reducing baseline variations and separating overlapping peaks. FDS has the additional benefit that it enables th

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47

Wadood, Abdul, Hani Albalawi, Aadel Mohammed Alatwi, Hafeez Anwar, and Tariq Ali. "Design of a Novel Fractional Whale Optimization-Enhanced Support Vector Regression (FWOA-SVR) Model for Accurate Solar Energy Forecasting." Fractal and Fractional 9, no. 1 (2025): 35. https://doi.org/10.3390/fractalfract9010035.

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This study presents a novel Fractional Whale Optimization Algorithm-Enhanced Support Vector Regression (FWOA-SVR) framework for solar energy forecasting, addressing the limitations of traditional SVR in modeling complex relationships within data. The proposed framework incorporates fractional calculus in the Whale Optimization Algorithm (WOA) to improve the balance between exploration and exploitation during hyperparameter tuning. The FWOA-SVR model is comprehensively evaluated against traditional SVR, Long Short-Term Memory (LSTM), and Backpropagation Neural Network (BPNN) models using traini

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48

Fu, Chengbiao, Heigang Xiong, and Anhong Tian. "Fractional Modeling for Quantitative Inversion of Soil-Available Phosphorus Content." Mathematics 6, no. 12 (2018): 330. http://dx.doi.org/10.3390/math6120330.

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The study of field spectra based on fractional-order differentials has rarely been reported, and traditional integer-order differentials only perform the derivative calculation for 1st-order or 2nd-order spectrum signals, ignoring the spectral transformation details between 0th-order to 1st-order and 1st-order to 2nd-order, resulting in the problem of low-prediction accuracy. In this paper, a spectral quantitative analysis model of soil-available phosphorus content based on a fractional-order differential is proposed. Firstly, a fractional-order differential was used to perform a derivative ca

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49

Corder, Nathan, and Shu Yang. "Estimating Average Treatment Effects Utilizing Fractional Imputation when Confounders are Subject to Missingness." Journal of Causal Inference 8, no. 1 (2020): 249–71. http://dx.doi.org/10.1515/jci-2019-0024.

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Abstract The problem of missingness in observational data is ubiquitous. When the confounders are missing at random, multiple imputation is commonly used; however, the method requires congeniality conditions for valid inferences, which may not be satisfied when estimating average causal treatment effects. Alternatively, fractional imputation, proposed by Kim 2011, has been implemented to handling missing values in regression context. In this article, we develop fractional imputation methods for estimating the average treatment effects with confounders missing at random. We show that the fracti

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50

Saran, Nurdan Ayse, and Fatih Nar. "Fast binary logistic regression." PeerJ Computer Science 11 (January 30, 2025): e2579. https://doi.org/10.7717/peerj-cs.2579.

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This study presents a novel numerical approach that improves the training efficiency of binary logistic regression, a popular statistical model in the machine learning community. Our method achieves training times an order of magnitude faster than traditional logistic regression by employing a novel Soft-Plus approximation, which enables reformulation of logistic regression parameter estimation into matrix-vector form. We also adopt the Lf-norm penalty, which allows using fractional norms, including the L2-norm, L1-norm, and L0-norm, to regularize the model parameters. We put Lf-norm formulati

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