Results 31 to 40 of about 177,424 (286)
Study of Some Kinds of Ridge Regression Estimators in Linear Regression Model
Tikrit Journal of Pure Science, 2020 In linear regression model, the biased estimation is one of the most commonly used methods to reduce the effect of the multicollinearity. In this paper, a simulation study is performed to compare the relative efficiency of some kinds of biased ...
Mustafa Nadhim Lattef, Mustafa I ALheety
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On Multivariate Ridge Regression [PDF]
Biometrika, 1987 A multivariate linear regression model with q responses as a linear function of p independent variables is considered with a \(p\times q\) parameter matrix B. The least-squares or normal-theory maximum likelihood estimate of B is deficient in that it takes no account of the `across regression' correlations, and ignores the Stein effect.
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Responsibility division method of harmonic sources in coal mine power system
Gong-kuang zidonghua, 2018 In view of the problem of collinearity of harmonic emission level evaluating method of coal mine power system based on multivariate linear regression, which leaded to the problem that evaluation result was greatly affected by abnormal value problem ...
GAO Yun, SU Jingwei
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Adaptive Multivariate Ridge Regression
The Annals of Statistics, 1980 A multivariate version of the Hoerl-Kennard ridge regression rule is introduced. The choice from among a large class of possible generalizations is guided by Bayesian considerations; the result is implicitly in the work of Lindley and Smith although not actually derived there.
Brown, P. J., Zidek, J. V.
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Econometric ridge regression models of risk-sensitive sunflower yield
Arquivo Brasileiro de Medicina Veterinária e Zootecnia, 2021 The article considers econometric ridge regression models of the risk-sensitive sunflower yield on the example of an export-oriented agricultural crop.
M.I. Slozhenkina +6 more
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Crosslingual Document Embedding as Reduced-Rank Ridge Regression [PDF]
, 2019 There has recently been much interest in extending vector-based word representations to multiple languages, such that words can be compared across languages.
Jaggi, Martin +4 more
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Continuum Regression and Ridge Regression
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1993 SUMMARY We demonstrate the close relationship between first-factor continuum regression and standard ridge regression. The difference is that continuum regression inserts a scalar compensation factor for that part of the shrinkage in ridge regression that has no connection with tendencies towards collinearity.
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KINERJA JACKKNIFE RIDGE REGRESSION DALAM MENGATASI MULTIKOLINEARITAS
E-Jurnal Matematika, 2014 Ordinary least square is a parameter estimations for minimizing residual sum of squares. If the multicollinearity was found in the data, unbias estimator with minimum variance could not be reached.
HANY DEVITA +2 more
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Modified Ridge Estimator for Poisson Regression
Cumhuriyet Science JournalPoisson regression is a statistical model used to model the relationship between a count-valued-dependent variable and one or more independent variables. A frequently encountered problem when modeling such relationships is multicollinearity, which occurs
Shuaib Mursal Ibrahim, Aydın Karakoca
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Bayesian Estimation Improves Prediction of Outcomes After Epilepsy Surgery
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT We estimated the statistical power of studies predicting seizure freedom after epilepsy surgery. We extracted data from a Cochrane meta‐analysis. The median power across all studies was 14%. Studies with a median sample size or less (n ≤ 56) and a statistically significant result exaggerated the true effect size by a factor of 5.4, while the ...
Adam S. Dickey +4 more
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