Fifty years have passed since the publication of the first regression tree algorithm. New techniques have added capabilities that far surpass those of the early methods. Modern classification trees can partition the data with linear splits on subsets of variables and fit nearest neighbor, kernel density, and other models in the partitions. Regression trees can fit almost every kind of tradition...

We propose an empirical likelihood test that is able to test the goodness-of-fit of a class of parametric and semiparametric multiresponse regression models. The class includes as special cases fully parametric models, semiparametric models, like the multi-index and the partially linear models, and models with shape constraints. Another feature of the test is that it allows both the response va...

This paper presents a novel, general and intelligent approach to multiresponse process optimisation, whose purpose is to obtain a single optimum setting of process parameters that meets specifications of all considered, possibly correlated, responses. The approach is based on Taguchi quality loss function, multivariate statistical methods: Principal componenet analysis and Grey relational analy...

This paper reports experimental and modeling work concerning alcoholysis reactions between butanol and ethyl butanoate, catalyzed by Lipozymee in n-hexane, using a batch stirred system at 608C. Description of the reaction kinetics was based on a postulated multisubstrate Ping Pong Bi Bi mechanism, and appropriate rate expressions were derived for all components in the reaction medium. Simplifie...

This paper presents a methodology named Optimally Pruned K-Nearest Neighbors (OP-KNNs) which has the advantage of competing with state-of-the-art methods while remaining fast. It builds a one hidden-layer feedforward neural network using K-Nearest Neighbors as kernels to perform regression. Multiresponse Sparse Regression (MRSR) is used in order to rank each kth nearest neighbor and finally Lea...

Aims to predict the Return on assets (ROA)of the company for the next year correctly and efficiently, this paper proposes a methodology called OP-KNN, which builds a one hidden-layer feedforward neural network, using nearest neighbors neurons with extremely small computational time. The main strategy is to select the most relevant variables beforehand, then to build the model using KNN kernels....

This paper presents a model of generic knowledge-based system for the selection of optimal process parameters in multiresponse processes, that consists of three modules which (1) use expert system, designed for selecting an inner and an outer orthogonal array, to design the experimental plan, (2) use Taguchi’s quality loss function and multivariate statistical methods to adequately present resp...

To examine the generality of the interreward response effects shown by rats under periodic food delivery, we presented .10 ml of water at minimum interwater intervals that ranged from 8 to 512 sec. Use of a 24-h multiresponse environment allowed evaluation-of interdrink responses with respect to their excessiveness, patterning, and functional relationship to the interwater interval. In contrast...

Wastewater recycling efficiency improvement is vital to arid regions, where crop irrigation imperative. Analyzing small, unreplicated–saturated, multiresponse, multifactorial datasets from novel wastewater electrodialysis (ED) applications requires specialized screening/optimization techniques. A new approach proposed glean information structured Taguchi-type sampling schemes (nonlinear fractio...