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Kernel Density Estimated Linear Regression
Proceedings of the International Florida Artificial Intelligence Research Society ConferenceRegression analysis is a cornerstone of predictive modeling, with linear regression and kernel regression standing as two of its most prominent paradigms.
Roshan Kalpavruksha +3 more
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Química Nova, 1997 The least square method is analyzed. The basic aspects of the method are discussed. Emphasis is given in procedures that allow a simple memorization of the basic equations associated with the linear and non linear least square method, polinomial ...
Rogério Custodio +2 more
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LINEAR REGRESSION WITH R AND HADOOP [PDF]
Challenges of the Knowledge Society, 2015 In this paper we present a way to solve the linear regression model with R and Hadoop using the Rhadoop library. We show how the linear regression model can be solved even for very large models that require special technologies.
Bogdan OANCEA
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Improved Penalty Strategies in Linear Regression Models
Revstat Statistical Journal, 2017 We suggest pretest and shrinkage ridge estimation strategies for linear regression models. We investigate the asymptotic properties of suggested estimators.
Bahadır Yüzbaşı +2 more
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OPLS statistical model versus linear regression to assess sonographic predictors of stroke prognosis
Neuropsychiatric Disease and Treatment, 2012 Kianoush Fathi Vajargah,1 Homayoun Sadeghi-Bazargani,2,3 Robab Mehdizadeh-Esfanjani,4 Daryoush Savadi-Oskouei,4 Mehdi Farhoudi41Department of Statistics, Islamic Azad University, Tehran, North Branch, 2Neuroscience Research Center, Department of ...
Fathi Vajargah K +4 more
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Ellipsoid Method for Linear Regression Parameters Determination
Кібернетика та комп'ютерні технології, 2020 Introduction. Linear regression parameters determination can be formulated as a non-smooth function minimization problem, which is Lp-norm of residual of the linear equations system.
V. Stovba
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Investigating the relationship between community pharmacy and GP Emergency Hormonal Contraception (EHC) provision: a linear regression analysis [PDF]
, 2022 Nick Thayer, Simon White, Martin Frisher
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Piecewise Linear Regression Based on Plane Clustering
IEEE Access, 2019 Piecewise linear regressions have shown many successful applications in image denoising, signal process, and data mining fields. In essence, they attempt to seek multiple linear functions (piecewise/stepwise function) to fit the given scatter data points
Xubing Yang +6 more
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Linear and Non-Linear Regression: Powerful and Very Important Forecasting Methods [PDF]
Expert Journal of Business and Management, 2015 Regression Analysis is at the center of almost every Forecasting technique, yet few people are comfortable with the Regression methodology. We hope to improve the level of comfort with this article.
Athanasios VASILOPOULOS
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Imputing missing values using cumulative linear regression
CAAI Transactions on Intelligence Technology, 2019 The concept of missing data is important to apply statistical methods on the dataset. Statisticians and researchers may end up to an inaccurate illation about the data if the missing data are not handled properly.
Samih M. Mostafa
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