Shirk AJ, Landguth EL, Cushman SA (2018) A comparison of regression methods for model selection in individual-based landscape genetic analysis. Mol Ecol Resour 18:55-67Shirk, A. J., Landguth, E. L., and Cushman, S. A. (2017). A comparison of regression methods for model selection in...
Model selection methods provide a way to select one model among a set of models in a statistically valid way. Such methods include tools for variable selection in regression models. Asymptotic properties such as consistency and efficiency,the specific use of the model, or properties regarding minimi...
In the context of variable selection for regression (with the goal of deciding which predictors should be included in a model), the statistical literature... P Bresciani,LUIS 被引量: 28发表: 2006年 A New Transform-Domain Regularized Recursive Least M-Estimate Algorithm for a Robust Linear Estim...
On the selection of samples for multivariate regression analysis: application to near-infrared (NIR) calibration models for the prediction of pulp yield in... The effects of using reduced calibration sets on the development of near-infrared (NIR) calibration models for the prediction of kraft pulp...
This paper studied two different regression techniques for pelvic shape prediction, i.e., the partial least square regression (PLSR) and the principal component regression (PCR). Three different predictors such as surface landmarks, morphological parameters, or surface models of neighboring structures ...
In the presence of multicollinearity, the ordinary least squares (OLS) estimator could become unstable due to their large variance, which leads to poor prediction. One of the popular solutions to this problem is Ridge Regression. Logistic Regression makes no assumption about the distribution of the...
Pricing Model Performance and the Two-Pass Cross-Sectional Regression Methodology Since Black, Jensen, and Scholes (1972) and Fama and MacBeth (1973), the two-pass cross-sectional regression (CSR) methodology has become the most popular ... R Kan,C Robotti,JA Shanken - 《Journal of Finance...
Regression analysis in R, just look at the Boston housing data and we can see a total of 506 observations and 14 variables. In this dataset, medv is the response variable, and the remaining are the predictors. We want to make a regression prediction model for medv based on other predic...
Heberger. Comparison of ridge regression, par- tial least-squares, pairwise correlation, forward- and best subset selection methods for prediction of ... O Farkas,K Héberger - 《Journal of Chemical Information & Modeling》 被引量: 56发表: 2005年 Comparison of ridge regression, partial least-...
Detection and prediction of errors in epcs of the sap reference model Data and Knowledge Engineering (2008) T.H. Fan et al. Tests and variables selection on regression analysis for massive datasets Data and Knowledge Engineering (2007) Y. Han et al. Utilizing hierarchical feature domain values ...