Adaptive ridge is a special form of ridge regression, balancing the quadratic penalization on each parameter of the model. This paper shows the equivalence between adaptive ridge and lasso (least absolute shrinkage and selection operator). This equivalence states that both procedures produce the same estimate. Least absolute shrinkage can thus be viewed as a particular quadratic penalization. F...

This article examines the relationships between interaction (product) terms and curvilinear (quadratic) terms in regression models in which the independent variables are correlated. The author uses 2 substantive examples to demonstrate the following outcomes: (a) If the appropriate quadratic terms are not added to the estimated model, then the observed interaction may indicate a synergistic (of...

A procedure for constructing a vector of regression weights is considered. Under the regression superpopulation model, the ridge regression estimator that has minimum model mean squared error is derived. Through a simulation study, the ridge regression weights, regression weights, quadratic programming weights and raking ratio weights are compared. The ridge regression procedure with weights bo...

the aim of the present study was the estimation of (co) variance components and genetic parameters for body weight of moghani sheep, using random regression models based on b-splines functions. the data set included 9165 body weight records from 60 to 360 days of age from 2811 moghani sheep, collected between 1994 to 2013 from jafar-abad animal research and breeding institute, ardabil province,...

selection of an appropriate model is important for quantifying response of germination rate to temperature and determination of cardinal temperatures. this study was done to evaluate different nonlinear regression models to describe response of germination rate to temperature in medicinal pumpkin, borago and black cumin. the regression models were dent-like, segmented, beta, curvilinear, quadra...