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Comparing parametric and non-parametric methods

African Journal of Midwifery and Women's Health, 2018
In this article, we will introduce the idea of parametric and non-parametric methods, which can be used to compare statistical hypotheses about population parameters
Malcolm Campbell
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Comparing Non-parametric Ensemble Methods for Document Clustering [PDF]

, 2008
The biases of individual algorithms for non-parametric document clustering can lead to non-optimal solutions. Ensemble clustering methods may overcome this limitation, but have not been applied to document collections. This paper presents a comparison of strategies for non-parametric document ensemble clustering.
González Pellicer, Edgar, Turmo Borras, Jorge   +1 more
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Non-Parametric Regression Methods

Computational Management Science, 2006
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Non-parametric Statistical Methods

1987
Basic statistics and econometrics courses stress methods based on assuming that the data or error term in regression models follow the normal distribution. Indeed, the efficiency of least squares estimates relies on the assumption of normality. In order to lessen the dependence of statistical inference on that assumption statisticians developed methods
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Non-Parametric Analysis Methods

2001
As mentioned in Chapter 6, non-parametric tests are applicable in two circumstances: (1) when parametric tests cannot be used owing to the scale of the response variable, that is, the scale is nominal or ordinal, and (2) when, even when the scale of the response variable admits the use of parametric tests, that is, it is an interval or ratio scale, the
Natalia Juristo, Ana M. Moreno
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A Non-parametric Semi-supervised Discretization Method

2008 Eighth IEEE International Conference on Data Mining, 2008
Semi-supervised classification methods aim to exploit labelled and unlabelled examples to train a predictive model. Most of these approaches make assumptions on the distribution of classes. This article first proposes a new semi-supervised discretization method which adopts very low informative prior on data.
Alexis Bondu, Marc Boullé, Vincent Lemaire 0001, Stéphane Loiseau, Béatrice Duval   +4 more
openaire   +3 more sources