Abstract Background and Aims Chronic kidney disease (CKD) is a severe public health burden, characterized by gradual loss of function over time. Diet modifiable lifestyle-related risk factor for CKD. However, there uncertainty about which specific dietary patterns (DPs) are more beneficial or detrimental in CKD prevention. We aimed at deriving DPs using an hybrid approach that combines priori k...

A simulation study investigating relative errors and sampling variances of reduced rank estimates of genetic covariance functions from random regression analyses estimating the leading principal components only, is presented. The example considered pertains to covariance functions for growth of beef cattle. It is demonstrated that the leading principal components are estimated most accurately, ...

Linear regression models are widely used in mental health and related health services research. However, the classic linear regression analysis assumes that the data are normally distributed, an assumption that is not met by the data obtained in many studies. One method of dealing with this problem is to use semi-parametric models, which do not require that the data be normally distributed. But...

We consider the prediction of weak effects in a multiple-output regression setup, when covariates are expected to explain a small amount, less than ≈ 1%, of the variance of the target variables. To facilitate the prediction of the weak effects, we constrain our model structure by introducing a novel Bayesian approach of sharing information between the regression model and the noise model. Furth...

Variable fertilization for crops, like corn, depends on monitoring nutrition condition. Thus, hyperspectral reflectance was used to predict chlorophyll and total nitrogen content under different N,K treatments during corn growth. The area of the experimental field was divided into 3 strips with different nitrogen treatment (N1:0 kg/ha–low, N2:314 kg/ha–normal, and N3:653 kg/ha–high). In each st...

We present a cross-benchmark comparison of learning-to-rank methods using two evaluation measures: the Normalized Winning Number and the Ideal Winning Number. Evaluation results of 87 learning-to-rank methods on 20 datasets show that ListNet, SmoothRank, FenchelRank, FSMRank, LRUF and LARF are Pareto optimal learning-to-rank methods, listed in increasing order of Normalized Winning Number and d...