Results 41 to 50 of about 56,202 (281)
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
International Journal of Science and Engineering, 2015 This paper presents Reliability Based Design Optimization (RBDO) model to deal with uncertainties involved in concrete mix design process. The optimization problem is formulated in such a way that probabilistic concrete mix input parameters showing ...
Rachna Aggarwal +3 more
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Entropy, 2023 We develop a new model for spatio-temporal data. More specifically, a graph penalty function is incorporated in the cost function in order to estimate the unknown parameters of a spatio-temporal mixed-effect model based on a generalized linear model ...
Roméo Tayewo +3 more
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Some theoretical results for generalized ridge regression estimators [PDF]
Journal of Econometrics, 1984 We examine some interpretations and theoretical properties of the ridge regression estimators. As such we (i) interpret the GRR estimator as an OLS one based on transformed explanatory variables; (ii) compare the GRR and OLS estimators using the confidence regions; (iii) prove the optimality of the OLS estimator for estimating the signs of the ...
Fourgeaud Claude +2 more
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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
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African Scientific Reports, 2022 Despite its common usage in estimating the linear regression model parameters, the ordinary least squares estimator often suffers a breakdown when two or more predictor variables are strongly correlated.
Abiola T. Owolabi +2 more
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Multinomial Logit Models with Implicit Variable Selection [PDF]
, 2010 Multinomial logit models which are most commonly used for the modeling of unordered multi-category responses are typically restricted to the use of few predictors. In the high-dimensional case maximum likelihood estimates frequently do not exist. In this
Tutz, Gerhard, Zahid, Faisal Maqbool
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Mapping wind erosion hazard with regression-based machine learning algorithms
Scientific Reports, 2020 Land susceptibility to wind erosion hazard in Isfahan province, Iran, was mapped by testing 16 advanced regression-based machine learning methods: Robust linear regression (RLR), Cforest, Non-convex penalized quantile regression (NCPQR), Neural network ...
Hamid Gholami +3 more
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Generalized Kibria-Lukman Estimator: Method, Simulation, and Application
Frontiers in Applied Mathematics and Statistics, 2022 In the linear regression model, the multicollinearity effects on the ordinary least squares (OLS) estimator performance make it inefficient. To solve this, several estimators are given. The Kibria-Lukman (KL) estimator is a recent estimator that has been
Issam Dawoud +2 more
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Orthogonalized smoothing for rescaled spike and slab models
, 2008 Rescaled spike and slab models are a new Bayesian variable selection method for linear regression models. In high dimensional orthogonal settings such models have been shown to possess optimal model selection properties.
Ishwaran, Hemant, Papana, Ariadni
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