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...

Optimal bandwidths for local polynomial regression usually involve functionals of the derivatives of the unknown regression function. In the multivariate case, estimates of these functionals are not readily available, primarily because estimating multivariate derivatives is complicated. In this paper, an estimator of multivariate second derivative is obtained via local quadratic regression with...

This paper contributes to classification and identification in modern finance through advanced optimization. In the last few decades, financial misalignments and, thereby, financial crises have been increasing in numbers due to the rearrangement of the financial world. In this study, as one of the most remarkable of these, countries’ debt crises, which result from illiquidity, are tried to pred...

This paper proposes the application of Gaussian process regression (GPR) as an alternative regression model to resolve the hard overlapped electrochemical signals belonging to the 2,4,6trichlorophenol/2,6-dichlorophenol (TCP/DCP) system. Gaussian process derives from the perspective of Bayesian non-parametric regression methods, in terms of the parameterization of the covariance function, resul...

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...

Local canonical correlation analysis (CCA) is a multivariate method that simultaneously analyzes the timecourses of a group of neighboring voxels and has been demonstrated to be more sensitive than the conventional univariate GLM approach. However, unlike the general linear model (GLM), an arbitrary linear contrast of the temporal regressors has not been so far incorporated in the CCA formalism...