Results 31 to 40 of about 52,512 (299)
The efficiency of modified jackknife and ridge type regression estimators: a comparison [PDF]
Surveys in Mathematics and its Applications, 2008 A common problem in multiple regression models is multicollinearity, which produces undesirable effects on the least squares estimator. To circumvent this problem, two well known estimation procedures are often suggested in the literature.
Sharad Damodar Gore +2 more
doaj
Ridge Estimation for Multinomial Logit Models with Symmetric Side Constraints [PDF]
, 2009 In multinomial logit models, the identifiability of parameter estimates is typically obtained by side constraints that specify one of the response categories as reference category.
Tutz, Gerhard, Zahid, Faisal Maqbool
core +1 more source
Metode Regresi Ridge Untuk Mengatasi Kasus Multikolinear
ComTech, 2011 Multicolinear is a case that occurs in multi-linear regression analysis. Using multicolinear, it will be difficult to separate the influence of each independent variable towards the response variables.
Margaretha Ohyver
doaj +1 more source
Robust weighted ridge regression based on S – estimator
African Scientific Reports, 2023 Ordinary least squares (OLS) estimator performance is seriously threatened by correlated regressors often called multicollinearity. Multicollinearity is a situation when there is strong relationship between any two exogenous variables.
Taiwo Stephen Fayose +3 more
doaj +1 more source
Benign overfitting in ridge regression
J. Mach. Learn. Res., 2020 In many modern applications of deep learning the neural network has many more parameters than the data points used for its training. Motivated by those practices, a large body of recent theoretical research has been devoted to studying overparameterized models. One of the central phenomena in this regime is the ability of the model to interpolate noisy
Alexander Tsigler, Peter L. Bartlett
openaire +4 more sources
Anomalies in the Foundations of Ridge Regression [PDF]
International Statistical Review, 2008 SummaryErrors persist in ridge regression, its foundations, and its usage, as set forth inHoerl & Kennard (1970)and elsewhere. Ridge estimators need not be minimizing, nor a prospective ridge parameter be admissible. Conventional estimators are not LaGrange's solutions constrained to fixed lengths, as claimed, since such solutions are singular.
Jensen, Donald R., Ramirez, Donald E.
openaire +3 more sources
Penalized Regression with Ordinal Predictors [PDF]
, 2008 Ordered categorial predictors are a common case in regression modeling. In contrast to the case of ordinal response variables, ordinal predictors have been largely neglected in the literature. In this article penalized regression techniques are proposed.
Gertheiss, Jan, Tutz, Gerhard
core +1 more source
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
doaj +1 more source
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.
openaire +2 more sources
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
doaj +1 more source