Regression - Yet Another Clustering Method 2001

: A general method is discussed, the ffi -test, which establishes functional dependencies given a table of measurements. The approach is based on calculating conditional probabilities from data densities. Imposing the requirement of continuity of the underlying function the obtained values of the conditional probabilities carry information on the variable dependencies. The power of the method is illustrated on synthetic timeseries with different time-lag dependencies and noise levels. For N data points the computational demand is N 2 . Also, th...

PLS regression is a recent technique that generalizes and combines features from principal component analysis and multiple regression. Its goal is to predict or analyze a set of dependent variables from a set of independent variables or predictors. This prediction is achieved by extracting from the predictors a set of orthogonal factors called latent variables which have the best predictive power. PLS regression is particularly useful when we need to predict a set of dependent variables from a (very) large set of independent variables (i.e., pr...

Abstract A gamma regression model which has wide application in life-testing and analysis of point processes is considered. Approximate statistical tests for the regression coefficients based on maximum likelihood and weighted least squares fits of the model are considered. The power and significance level properties of the tests are evaluated for a model with a single regressor variate when the underlying gamma distributions have the same shape parameter. The tests give good control over the actual significance levels for all values of the sh...

Abstract It is well known that the classical regression analysis, especially parametric regression analysis, is one of the important methods of extracting information from data sets. A family of regression functions, called fuzzy c-regression models (FCRM), has been presented which can be used to characterize the linear relationship to certain types of mixed data. More generally, an effective and robust method, coined regression class mixture decomposition (RCMD), has also been proposed for the mining of regression classes in large data sets. ...

Abstract It is well known that the classical regression analysis, especially parametric regression analysis, is one of the important methods of extracting information from data sets. A family of regression functions, called fuzzy c-regression models (FCRM), has been presented which can be used to characterize the linear relationship to certain types of mixed data. More generally, an effective and robust method, coined regression class mixture decomposition (RCMD), has also been proposed for the mining of regression classes in large data sets. ...

The advantage of using linear regression in the leaves of a regression tree is analysed in the paper. It is carried out how this modification affects the construction, pruning and interpretation of a regression tree. The modification is tested on artificial and real-life domains where its impact on classification error and stability of the induced trees is considered. The results show that the modification is beneficial, as it leads to smaller classification errors of induced regression trees. The Bayesian approach to estimation of class distri...

: A method for estimating nonlinear regression errors and their distributions without performing regression is presented. Assuming continuity of the modeling function the variance is given in terms of conditional probabilities extracted from the data. For N data points the computational demand is N 2 .The method is successfully illustrated with data generated by the Ikeda and Lorentz maps augmented with noise. As a by-product the embedding dimensions of these maps are extracted. Comparing the predicted residual errors with those from linear mod...

. The advantage of using linear regression in the leaves of a regression tree is analysed in the paper. It is carried out how this modification affects the construction, pruning and interpretation of a regression tree. The modification is tested on artificial and real-life domains. The results show that the modification is beneficial as it leads to smaller classification errors of induced regression trees. Keywords: machine learning, TDIDT, regression, linear regression, Bayesian approach. 1 Introduction Regression trees, similar to classificat...

Abstract The paper provides a discussion of the possibilistic regression method originally proposed by H. Tanaka. This method has the advantage of allowing the learning of an imprecise model, in the form of an interval-valued function. It may lead to an imprecise model even in presence of precise data, which is satisfactory from a learning point of view. Indeed, finding a precise model that perfectly represents the concept to be learned is illusory, due to the existence of the bias caused by the choice of a modeling representation space, the l...

In a partially linear regression model with a high dimensional un- known component we find an estimator of the parameter of the linear part based on projection pursuit methods to be considerably more efficient than the standard density weighted kernel estimator.

Dr. Bruce Goldberg holds a BA degree in Biology and Chemistry, is a Doctor of Dental Surgery, and ha...

Dr. Bruce Goldberg holds a BA degree in Biology and Chemistry, is a Doctor of Dental Surgery, and ha...

Dr. Bruce Goldberg holds a BA degree in Biology and Chemistry, is a Doctor of Dental Surgery, and ha...

Dr. Bruce Goldberg holds a BA degree in Biology and Chemistry, is a Doctor of Dental Surgery, and ha...

Dr. Bruce Goldberg holds a BA degree in Biology and Chemistry, is a Doctor of Dental Surgery, and ha...

Dr. Bruce Goldberg holds a BA degree in Biology and Chemistry, is a Doctor of Dental Surgery, and ha...

Dr. Bruce Goldberg holds a BA degree in Biology and Chemistry, is a Doctor of Dental Surgery, and ha...

Dr. Bruce Goldberg holds a BA degree in Biology and Chemistry, is a Doctor of Dental Surgery, and ha...

Dr. Bruce Goldberg holds a BA degree in Biology and Chemistry, is a Doctor of Dental Surgery, and ha...

Dr. Bruce Goldberg holds a BA degree in Biology and Chemistry, is a Doctor of Dental Surgery, and ha...

In statistics, regression toward the mean refers to the phen...

Semiparametric regression refers to regression models which ...

The kernel regression is a non-parametric technique in stati...

Regression, according to psychoanalyst Sigmund Freud, is a d...

A software regression is a software bug which makes a featur...

In statistics, binomial regression is a technique in which t...

Atavistic regression is a hypnosis-related concept introduce...