Probablistic Model: We start with the assumption that prior to starting a sequence of experiments we have a family of random variables with means that vary linearly with respect to some deterministic independent variable. That is, there exist an intercept β0 and a slope β1 such that for each value of the independent variable x, we have a random variable Y with mean β0 + β1x. We are then given p...

Dalam kertas ini, prestasi ralat piawai bootstrap bagi anggaran berpemberat MM (WMM) dibandingkan dengan ralat piawai Monte Carlo dan ralat piawai Berasimptot. Sifat-sifat selang keyakinan bootstrap bagi anggaran berpemberat WMM seperti 'Percentile' (PB), 'Bias-corrected Percentile' (BCP), 'Bias and Accelerated' (BC.), 'Studentzed Percentile' (SPB) dan 'Symmetric' (SB) te1ah diperiksa dan diban...

We consider linear models where d potential causes X1, . . . , Xd are correlated with one target quantity Y and propose a method to infer whether the association is causal or whether it is an artifact caused by overfitting or hidden common causes. We employ the idea that in the former case the vector of regression coefficients has ‘generic’ orientation relative to the covariance matrix ΣXX of X...

Indian summer monsoon rainfall (ISMR) is an important metric to quantify the Asian monsoon system. Artificial Neural Networks, ANNs, are being increasingly used for nonlinear regression and classification problems in meteorology. The issues raised for this study can be summarized as the problem of simulation of the ISMR time series with the ANN model to get away with the need of external parame...

In this chapter recent research in the area of multi-objective optimisation of regression models is presented and combined. Evolutionary multi-objective optimisation techniques are described for training a population of regression models to optimise the recently defined Regression Error Characteristic Curves (REC). A method which meaningfully compares across regressors and against benchmark mod...

Endowing robots with the capability to learn is an important goal for the robotics research community. One part of this research is focused on learning skills, where usually two learning paradigms are used sequentially. First, a robot learns a motor primitive by demonstration (or imitation). Then, it improves this motor primitive with respect to some externally defined criterion. In this paper,...

Text regression has traditionally been tackled using linear models. Here we present a non-linear method based on a deep convolutional neural network. We show that despite having millions of parameters, this model can be trained on only a thousand documents, resulting in a 40% relative improvement over sparse linear models, the previous state of the art. Further, this method is flexible allowing...

Estimators for differential entropy are proposed. The estimators are based on the second order expansion of the probability mass around the inspection point with respect to the distance from the point. Simple linear regression is utilized to estimate the values of density function and its second derivative at a point. After estimating the values of the probability density function at each of th...

fuzzy linear regression models are used to obtain an appropriate linear relation between a dependent variable and several independent variables in a fuzzy environment. several methods for evaluating fuzzy coefficients in linear regression models have been proposed. the first attempts at estimating the parameters of a fuzzy regression model used mathematical programming methods. in this the...

introduction: the development of an appropriate model for the quality control of an industrial wastewater treatment system can save the time as well as the cost. this study was performed to determine an appropriate model in order to predict the cod and tkn parameters by bod5 and nh4+ in the meybod industrial estate waste water treatment plant (wwtp). methods: this descriptive – analytical study...