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Multiple Comparisons in Induction Algorithms
 Machine Learning
, 1998
"... Keywords Running Head multiple comparison procedure Multiple Comparisons in Induction Algorithms David Jensen and Paul R. Cohen Experimental Knowledge Systems Laboratory Department of Computer Science Box 34610 LGRC University of Massachusetts Amherst, MA 010034610 4135453613 A single ..."
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Keywords Running Head multiple comparison procedure Multiple Comparisons in Induction Algorithms David Jensen and Paul R. Cohen Experimental Knowledge Systems Laboratory Department of Computer Science Box 34610 LGRC University of Massachusetts Amherst, MA 010034610 4135453613 A single mechanism is responsible for three pathologies of induction algorithms: attribute selection errors, overfitting, and oversearching. In each pathology, induction algorithms compare multiple items based on scores from an evaluation function and select the item with the maximum score. We call this a ( ). We analyze the statistical properties of and show how failure to adjust for these properties leads to the pathologies. We also discuss approaches that can control pathological behavior, including Bonferroni adjustment, randomization testing, and crossvalidation. Inductive learning, overfitting, oversearching, attribute selection, hypothesis testing, parameter estimation Multiple Com...
A Statistical Test For HostParasite Coevolution
 SYST. BIOL.
, 2002
"... A new method, ParaFit, has been developed to test the significance of a global hypothesis of coevolution between parasites and their hosts. Individual hostparasite association links can also be tested. The test statistics are functions of the host and parasite phylogenetic trees and of the set of ..."
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Cited by 21 (0 self)
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A new method, ParaFit, has been developed to test the significance of a global hypothesis of coevolution between parasites and their hosts. Individual hostparasite association links can also be tested. The test statistics are functions of the host and parasite phylogenetic trees and of the set of hostparasite association links. Numerical simulations are used to show that the method has correct rate of type I error and good power except under extreme error conditions. An application to real data (pocket gophers and chewing lice) is presented. [Coevolution; fourthcorner statistic; hostparasite; permutation test; phylogenetic analysis; power analysis; simulations; statistical test.]
SENSITIVITY OF MRQAP TESTS TO COLLINEARITY AND AUTOCORRELATION CONDITIONS
, 2007
"... Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among n objects. Such a data structure is typical in social network studies, where variables indicate some ty ..."
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Cited by 13 (1 self)
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Multiple regression quadratic assignment procedures (MRQAP) tests are permutation tests for multiple linear regression model coefficients for data organized in square matrices of relatedness among n objects. Such a data structure is typical in social network studies, where variables indicate some type of relation between a given set of actors. We present a new permutation method (called “double semipartialing”, or DSP) that complements the family of extant approaches to MRQAP tests. We assess the statistical bias (type I error rate) and statistical power of the set of five methods, including DSP, across a variety of conditions of network autocorrelation, of spuriousness (size of confounder effect), and of skewness in the data. These conditions are explored across three assumed data distributions: normal, gamma, and negative binomial. We find that the Freedman–Lane method and the DSP method are the most robust against a wide array of these conditions. We also find that all five methods perform better if the test statistic is pivotal. Finally, we find limitations of usefulness for MRQAP tests: All tests degrade under simultaneous conditions of extreme skewness and high spuriousness for gamma and negative binomial distributions.
A nonconservative Lagrangian framework for statistical fluid registration  SAFIRA
, 2010
"... In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3D brain images. This algorithm is named SAFIRA, acronym for StatisticallyAssisted Fluid Image Registration Algorithm. A nonstatistical version of this algorit ..."
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Cited by 2 (0 self)
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In this paper, we used a nonconservative Lagrangian mechanics approach to formulate a new statistical algorithm for fluid registration of 3D brain images. This algorithm is named SAFIRA, acronym for StatisticallyAssisted Fluid Image Registration Algorithm. A nonstatistical version of this algorithm was implemented [9], where the deformation was regularized by penalizing deviations from a zero rate of strain. In [9], the terms regularizing the deformation included the covariance of the deformation matrices (Σ) and the vector fields (q). Here we used a Lagrangian framework to reformulate this algorithm, showing that the regularizing terms essentially allow nonconservative work to occur during the flow. Given 3D brain images from a group of subjects, vector fields and their corresponding deformation matrices are computed in a first round of registrations using the nonstatistical implementation. Covariance
RESEARCH ARTICLE Open Access
"... Mortality affects adaptive allocation to growth and reproduction: field evidence from a guild of body snatchers ..."
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Mortality affects adaptive allocation to growth and reproduction: field evidence from a guild of body snatchers
c ○ 2000 Kluwer Academic Publishers. Printed in The Netherlands. Multiple Comparisons in Induction Algorithms
"... Abstract. A single mechanism is responsible for three pathologies of induction algorithms: attribute selection errors, overfitting, and oversearching. In each pathology, induction algorithms compare multiple items based on scores from an evaluation function and select the item with the maximum score ..."
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Abstract. A single mechanism is responsible for three pathologies of induction algorithms: attribute selection errors, overfitting, and oversearching. In each pathology, induction algorithms compare multiple items based on scores from an evaluation function and select the item with the maximum score. We call this a multiple comparison procedure (MCP). We analyze the statistical properties of MCPs and show how failure to adjust for these properties leads to the pathologies. We also discuss approaches that can control pathological behavior, including Bonferroni adjustment, randomization testing, and crossvalidation. Keywords: estimation inductive learning, overfitting, oversearching, attribute selection, hypothesis testing, parameter 1.
A Multivariate Groupwise Genetic Analysis of White Matter Integrity using Orientation Distribution Functions
"... Abstract. Diffusion magnetic resonance imaging has become an important tool for comparing brain white matter fiber structure between groups of subjects. While voxelwise statistical comparison are typically performed on scalar values derived from the diffusion tensors (DT), several authors have advo ..."
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Abstract. Diffusion magnetic resonance imaging has become an important tool for comparing brain white matter fiber structure between groups of subjects. While voxelwise statistical comparison are typically performed on scalar values derived from the diffusion tensors (DT), several authors have advocated applying multivariate statistics to better exploit the information contained in the tensors, as they show significant improvements over their univariate counterparts. The DTs are good approximations to the fiber orientation in regions with no fiber crossings or partial volumed voxels; however, fiber crossings are ubiquitous in the brain, and the tensor approximation fails throughout a significant portion of the image. Consequently, here we treat this issue by analyzing the raw diffusion data directly and by building orientation distribution functions (ODF s), using the modified spherical harmonic decomposition from [7]. More precisely, we first perform linear and nonlinear registrations to transform these diffusionweighted images to a common space,
NeuroImage 48 (2009) 37–49 Contents lists available at ScienceDirect
"... journal homepage: www.elsevier.com/locate/ynimg Mapping the regional influence of genetics on brain structure ..."
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journal homepage: www.elsevier.com/locate/ynimg Mapping the regional influence of genetics on brain structure
Alzheimer's Disease Neuroimaging Initiative: A oneyear follow up study using
"... journal homepage: www.elsevier.com/locate/ynimg ..."
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unknown title
"... Quantification and statistical analysis of structural similarities in dialectological areaclass maps* ..."
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Quantification and statistical analysis of structural similarities in dialectological areaclass maps*