In this paper we present a technique for extending generalized linear models (GLM) to the situation where some of the predictor variables are observations from a curve or function. The technique is particularly useful when only fragments of each curve have been observed. We demonstrate, on both simulated and real world data sets, how this approach can be used to perform linear, logistic and cen...

Generalized linear models are one of the most widely used tools of the data analyst. However, the model assumes that the structure of the regression relationship between the response and the covariates is linear on a known transformed scale. We focus here on diierent methods to perform the same type of analyses. These involve using nonparametric models to determine the relationship between the ...

The use of blasting cost (BC) prediction to achieve optimal fragmentation is necessary in order to control the adverse consequences of blasting such as fly rock, ground vibration, and air blast in open-pit mines. In this research work, BC is predicted through collecting 146 blasting data from six limestone mines in Iran using the artificial neural networks (ANNs), gene expression programming (G...

We demonstrate a binary classification problem in which standard supervised learning algorithms such as linear and kernel SVM, naive Bayes, ridge regression, k-nearest neighbors, shrunken centroid, multilayer perceptron and decision trees perform in an unusual way. On certain data sets they classify a randomly sampled training subset nearly perfectly, but systematically perform worse than rando...

Pavement rehabilitation could affect the accident severity index (ASI) since restoration measures means more safety for road users. No research or project has been carried out to identify hazard points to build a linear model based on crash severity index. One of the very popular accident severity index models used in all countries is based on linear models to rehabilitate pavements and this pa...

Small Area estimation is a technique used to estimate parameters of subpopulations with small sample sizes.  Small area estimation is needed  in obtaining information on a small area, such as sub-district or village.  Generally, in some cases, small area estimation uses parametric modeling.  But in fact, a lot of models have no linear relationship between the small area average and the covariat...

This research aims to predict PV output power by using different neuro-evolutionary methods. The proposed approach was evaluated by a data set, which was collected at 5-minute intervals in the photovoltaic laboratory of Niroo Research Institute of Iran (Tehran). The data has been divided into three intervals based on the amount of solar irradiation, and different neural networks were used for p...