A “multivariate interaction” in a regression model is a product of two independent variates (linear functions of the regressors) that is an additive component of the regression function E(Y |X). In many cases a substantial portion of the overall pairwise interaction structure in a regression function can be captured by a single multivariate interaction. Due to its parsimonious form, a multivari...

We propose a new method named calibrated multivariate regression (CMR) for fitting high dimensional multivariate regression models. Compared to existing methods, CMR calibrates the regularization for each regression task with respect to its noise level so that it is simultaneously tuning insensitive and achieves an improved finite-sample performance. Computationally, we develop an efficient smo...

In many high dimensional problems, the dependence structure among the variables can be quite complex. An appropriate use of the regularization techniques coupled with other classical statistical methods can often improve estimation and prediction accuracy and facilitate model interpretation, by seeking a parsimonious model representation that involves only the subset of revelent variables. We p...

As in many areas of biostatistics, oncological problems often have multivariate predictors. While assuming a linear additive model is convenient and straightforward, it is often not satisfactory when the relation between the outcome measure and the predictors is either nonlinear or nonadditive. In addition, when the number of predictors becomes (much) larger than the number of independent obser...

A multivariate normal statistical model defined by the Markov properties determined by an acyclic digraph admits a recursive factorization of its likelihood function (LF) into the product of conditional LFs, each factor having the form of a classical multivariate linear regression model (≡ MANOVA model). Here these models are extended in a natural way to normal linear regression models whose LF...

Population poverty is one of the serious problems in Indonesia. The percentage population used as a means for statistical instrument to be guidelines create standard policies and evaluations reduce poverty. aims research are determine model using MARS Bagging then understand most influence variable Central Java Province 2018. result this showed better accuracy than model. Since, GCV value 0,009...