Results 41 to 50 of about 1,234,185 (150)
ANALYSIS OF DISCRETE DATA USING LINEAR REGRESSION
Ecosistemas y Recursos Agropecuarios, 2014 Maximum likelihood estimates are obtained for a linear regressio model where the dependent variable is a linear transformation of mutinomially distributed random variables.
Robert J. Flowers
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A Study on the Linkage between the Workplace Variations and Lawyers’ CSR Meanings in Chinese Legal Service Market Using Ordinary Linear Regression Model [PDF]
, 2023 Jin Sheng Dong
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Bridge Estimation for Linear Regression Models with Mixing Properties [PDF]
, 2014 Taewook Lee +2 more
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Robust inference for non‐linear regression models from the Tsallis score: Application to coronavirus disease 2019 contagion in Italy [PDF]
, 2020 Paolo Girardi +6 more
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ON THE GASTALDI – D’URSO FUZZY LINEAR REGRESSION [PDF]
Challenges of the Knowledge Society, 2011 In the crisp regression models, the differences between observed values and calculates ones are suspected to be caused by random distributed errors, although these are due to observation errors and an unappropriate model structure.
DANA-FLORENTA SIMION +2 more
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Structural Change Analysis in Linear Regression Model.
Revista de Matemática: Teoría y Aplicaciones, 2010 Assuming that the observations are from normal distribution we obtain de distribution of the maximum likelihood ratio test if there is a change in the parameters at an unknown time and we find the maximum likehood estimators of the time change too.
Blanca Rosa Pérez Salvador +1 more
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On identification methods of linear regression models
Lietuvos Matematikos Rinkinys, 1999 The aim of this paper is to discus the efficiency of the least squares and least absolute values estimation methods, in such situations, then the sample is with outliers, the variance of the residuals changes in the time and the residuals are correlated.
Romualdas Salėtis
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Fast Minimum Error Entropy for Linear Regression
AlgorithmsThe minimum error entropy (MEE) criterion finds extensive utility across diverse applications, particularly in contexts characterized by non-Gaussian noise.
Qiang Li +5 more
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An Entropic Approach to Constrained Linear Regression
MathematicsWe introduce a novel entropy minimization approach for the solution of constrained linear regression problems. Rather than minimizing the quadratic error, our method minimizes the Fermi–Dirac entropy, with the problem data incorporated as constraints. In
Argimiro Arratia, Henryk Gzyl
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