23,593 research outputs found
A Multi-objective Exploratory Procedure for Regression Model Selection
Variable selection is recognized as one of the most critical steps in
statistical modeling. The problems encountered in engineering and social
sciences are commonly characterized by over-abundance of explanatory variables,
non-linearities and unknown interdependencies between the regressors. An added
difficulty is that the analysts may have little or no prior knowledge on the
relative importance of the variables. To provide a robust method for model
selection, this paper introduces the Multi-objective Genetic Algorithm for
Variable Selection (MOGA-VS) that provides the user with an optimal set of
regression models for a given data-set. The algorithm considers the regression
problem as a two objective task, and explores the Pareto-optimal (best subset)
models by preferring those models over the other which have less number of
regression coefficients and better goodness of fit. The model exploration can
be performed based on in-sample or generalization error minimization. The model
selection is proposed to be performed in two steps. First, we generate the
frontier of Pareto-optimal regression models by eliminating the dominated
models without any user intervention. Second, a decision making process is
executed which allows the user to choose the most preferred model using
visualisations and simple metrics. The method has been evaluated on a recently
published real dataset on Communities and Crime within United States.Comment: in Journal of Computational and Graphical Statistics, Vol. 24, Iss.
1, 201
Disentangling causal webs in the brain using functional Magnetic Resonance Imaging: A review of current approaches
In the past two decades, functional Magnetic Resonance Imaging has been used
to relate neuronal network activity to cognitive processing and behaviour.
Recently this approach has been augmented by algorithms that allow us to infer
causal links between component populations of neuronal networks. Multiple
inference procedures have been proposed to approach this research question but
so far, each method has limitations when it comes to establishing whole-brain
connectivity patterns. In this work, we discuss eight ways to infer causality
in fMRI research: Bayesian Nets, Dynamical Causal Modelling, Granger Causality,
Likelihood Ratios, LiNGAM, Patel's Tau, Structural Equation Modelling, and
Transfer Entropy. We finish with formulating some recommendations for the
future directions in this area
A Mathematical Programming Approach for Integrated Multiple Linear Regression Subset Selection and Validation
Subset selection for multiple linear regression aims to construct a
regression model that minimizes errors by selecting a small number of
explanatory variables. Once a model is built, various statistical tests and
diagnostics are conducted to validate the model and to determine whether the
regression assumptions are met. Most traditional approaches require human
decisions at this step. For example, the user adding or removing a variable
until a satisfactory model is obtained. However, this trial-and-error strategy
cannot guarantee that a subset that minimizes the errors while satisfying all
regression assumptions will be found. In this paper, we propose a fully
automated model building procedure for multiple linear regression subset
selection that integrates model building and validation based on mathematical
programming. The proposed model minimizes mean squared errors while ensuring
that the majority of the important regression assumptions are met. We also
propose an efficient constraint to approximate the constraint for the
coefficient t-test. When no subset satisfies all of the considered regression
assumptions, our model provides an alternative subset that satisfies most of
these assumptions. Computational results show that our model yields better
solutions (i.e., satisfying more regression assumptions) compared to the
state-of-the-art benchmark models while maintaining similar explanatory power
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