Linear Regression Model-Guided Clustering for Training RBF Networks for Regression Problemsdoi:10.1007/10983652_17In this paper, we describe a novel approach to fuzzy clustering which organizes the data in clusters on the basis of the input data and builds a 'prototype' regression function as a ...
We propose to address the common problem of linear estimation in linear statistical models by using a model selection approach via penalization. Depending then on the framework in which the linear statistical model is considered namely the regression framework or the inverse problem framework, a data...
Up to now, we have only considered an “univariate Gauss–Markov model”. Its generalization towards a multivariate Gauss–Markov model will be given in Sect.14.1. At first, we define a multivariate linear model by Definition 14.1 by giving its...
We then consider a second optimization problem in which we construct designs achieving equality of variances of the estimators of two linear functions of the parameters. The approaches are formulated for a general regression model, and are explored through some models of interest....
The impact of model selection on inference in linear regression. American Statistician 44: 214–217. Mantel, Nathan. 1970. Why stepdown procedures in variable selection. Technometrics 12: 621–625. Roecker, Ellen B. 1991. Prediction error and its estimation for subset—selected models. Technome...
General Linear ModelSymmetric CensoringAsymptotic PropertiesApproximate InferenceIn the general linear model , the Best Lineardoi:10.1080/03610928808829750Moussa-HamoudaEFFATMarcel Dekker, Inc.Communications in StatisticsMoussa-Hamouda, E. (1988). Inference in regression problems based on order statistics. Comm...
Most of these solutions had a form that was linear in the data, mest = Md + v, where M is some matrix and v is some vector, both of which are independent of the data d. This equation indicates that the estimate of the model parameters is controlled by some matrix M operating on ...
We trained a linear model to decode position in the trial (Initiation and A/B choice/reward/no-reward) using data from one problem and tested the decoding performance on a different problem (Fig. 4f,g). Because the B and initiation ports moved and sometimes interchanged between problems, ...
However, for linear regression, there is an excellent accelerated cross-validation method called predicted R-squared. This method doesn’t require you to collect a separate sample or partition your data, and you can obtain the cross-validated results as you fit the model. Statistical software calc...
However, when varying across the effect of T on Y and keeping the other parameters constant, the bias showed a linear increasing trend after adjusting for set A2 or A3 under the logistic regression model, but was approximately to zero after adjusting for set A1. However, the biases remained...