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Regression II. Development through regression
Journal of Analytical Psychology, 2020AbstractAs shown in our previous paper (‘Regression I. Experimental approaches to regression’,JAP,65, 2, 345‐65), the common mechanism of regression can be described as reversible dedifferentiation, which is understood as a relative increase of the proportion of low‐differentiated (older) systems in actualized experience.
Yuri, Alexandrov +7 more
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Regression Calibration in Failure Time Regression
Biometrics, 1997In this paper we study a regression calibration method for failure time regression analysis when data on some covariates are missing or mismeasured. The method estimates the missing data based on the data structure estimated from a validation data set, a random subsample of the study cohort in which covariates are always observed.
Wang, C. Y. +3 more
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2007
The Medical Subject Headings (MeSH) thesaurus used by the National Library of Medicine defines logistic regression models as "statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable ...
Todd G, Nick, Kathleen M, Campbell
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The Medical Subject Headings (MeSH) thesaurus used by the National Library of Medicine defines logistic regression models as "statistical models which describe the relationship between a qualitative dependent variable (that is, one which can take only certain discrete values, such as the presence or absence of a disease) and an independent variable ...
Todd G, Nick, Kathleen M, Campbell
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Annals of Operations Research, 1995
We take a regression-based approach to the problem of induction, which is the problem of inferring general rules from specific instances. Whereas traditional regression analysis fits a numerical formula to data, we fit a logical formula to boolean data. We can, for instance, construct an expert system for fitting rules to an expert's observed behavior.
Boros, E., Hammer, P. L., Hooker, J. N.
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We take a regression-based approach to the problem of induction, which is the problem of inferring general rules from specific instances. Whereas traditional regression analysis fits a numerical formula to data, we fit a logical formula to boolean data. We can, for instance, construct an expert system for fitting rules to an expert's observed behavior.
Boros, E., Hammer, P. L., Hooker, J. N.
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2004
The main problem with localized discriminant techniques is the curse of dimensionality, which seems to restrict their use to the case of few variables. This restriction does not hold if localization is combined with a reduction of dimension. In particular it is shown that localization yields powerful classifiers even in higher dimensions if ...
Tutz, Gerhard, Binder, Harald
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The main problem with localized discriminant techniques is the curse of dimensionality, which seems to restrict their use to the case of few variables. This restriction does not hold if localization is combined with a reduction of dimension. In particular it is shown that localization yields powerful classifiers even in higher dimensions if ...
Tutz, Gerhard, Binder, Harald
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ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik, 1998
AbstractA new approach in nonparametric regression is to use the signs of the residuals ri = yi ‐ θ (xi) in the regression modell yi = θ (xi) + ϵi instead of the residuals itself. It turns out, that with a suitable definition of complexity of the noise ϵi we are able to determine the minimum number of local extrema and turning points for the regression
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AbstractA new approach in nonparametric regression is to use the signs of the residuals ri = yi ‐ θ (xi) in the regression modell yi = θ (xi) + ϵi instead of the residuals itself. It turns out, that with a suitable definition of complexity of the noise ϵi we are able to determine the minimum number of local extrema and turning points for the regression
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Biostatistics, 2023
SummaryAssessing how brain functional connectivity networks vary across individuals promises to uncover important scientific questions such as patterns of healthy brain aging through the lifespan or dysconnectivity associated with disease. In this article we introduce a general regression framework, Connectivity Regression (ConnReg), for regressing ...
Neel Desai +3 more
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SummaryAssessing how brain functional connectivity networks vary across individuals promises to uncover important scientific questions such as patterns of healthy brain aging through the lifespan or dysconnectivity associated with disease. In this article we introduce a general regression framework, Connectivity Regression (ConnReg), for regressing ...
Neel Desai +3 more
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Multiple Regression—Regression Diagnostics
2004In Chapter 9 we show how to set up and produce an initial analysis of a regression model with several predictors. In the present chapter we discuss ways to investigate whether the model assumptions are met and, when the assumptions are not met, ways to revise the model to better conform with the assumptions. We also examine ways to assess the effect on
Richard M. Heiberger, Burt Holland
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