Kashid: Variable selection in linear regression based on ridge estimator. Journal of Statistical Computation and Simulation, 80 (2010), 1211-1224.Dorugade and Kashid (2010), Variable Selection in Linear regression Based on Ridge Estimator , Journal of Statistical Computations and Simulations (To ...
17. Piecewise-Linear Manifolds for Deep Metric Learning 作者:Shubhang Bhatnagar, Narendra Ahuja 关键词:Deep metric learning, Unsupervised representation learning 18. Sparse Activations with Correlated Weights in Cortex-Inspired Neural Networks 作者:Chanwoo Chun, Daniel Lee 关键词:Correlated weights, Biolog...
Consider Bayesian variable selection in normal linear regression models based on Zellner’s \(g\)-prior. We study theoretical properties of this method when the sample size \(n\) grows and consider the cases when the number of regressors, \(p\) is fixed and when it grows with \(n\). ...
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The Retinex algorithm effectively dealt with the variable illumination, outperforming the other methods in the estimation of chlorophyll content. The \(r^2\) of the multivariable linear model based on illumination-corrected consistent VIs was 0.61 under the variable illumination condition. Conclusions ...
In the above selection model we assume a linear relationship between the logit of the probability of nonresponse and the outcome. Different values of the parameter λ posit different assumptions on how strongly the likelihood of nonresponse depends on the outcome. When λ = 0, we have the ...
The Lasso of Tibshirani (1996) is a useful method for estimation and implicit selection of predictors in a linear regression model, by using a `1-penalty, if the number of observations is not markedly larger than the number of possible predictors.We apply the Lasso to a predictive linear reg...
An approximate multinomial deviance statistic can be computed in closed form (formula provided in the “Methods” section). We compared gene ranks for all three feature selection methods (deviance, highly expressed, and highly variable genes) on the 8eq dataset (Table 1). We found a strong ...
In parametric regression, the underlying link function betweenY(the response variable) andX(thep-dimensional covariate) relies on a finite number of parameters to be estimated. The most popular parametric regression model is the linear regression model\(Y={\beta }_{0}+\sum _{j=1}^{p}\,{...
In classical regression analysis, the error of independent variable is usually not taken into account in regression analysis. This paper presents two solution methods for the case that both the independent and the dependent variables have errors. These methods are derived from the condition-adjustment...