nonparametric


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Related to nonparametric: Nonparametric Methods

non·par·a·met·ric

(non'par-ă-met'rik),
A group of statistical maneuvers that can be applied effectively to nonnormal or nongaussian data that is nonnormal or nongaussian in distribution.

non·par·a·met·ric

(non-par'ă-met'rik)
A group of statistical maneuvers that can be applied effectively to data that are nonnormal or nongaussian in distribution.
References in periodicals archive ?
Nonparametric statistics are used in many situations to analyze data, and there are many different types of statistics for different types of data.
Tiemey (2012) found that the nonparametric exclusion-from-core inflation persistence model was able to utilize data revisions, which were small in magnitude.
The purpose of this study is to obtain a new method for the determination of hypothesis test of model conformity between multivariate nonparametric truncated spline regression influenced by spatial heterogeneity versus multivariate nonparametric truncated spline regression in general.
The nonparametric method underestimated the probability of interest in 51/54 scenarios, but the mean bias was low in general, ranging across scenarios from -0.062 to 0.001 (median: -0.006).
Roughly speaking, a nonparametric procedure is a statistical procedure that has certain desirable properties.
Furthermore, addition and deletion of one or a few observations is not as likely to cause great variation in the estimates as many of the nonparametric methods have recently been compared by others (Sabaghnia et al., 2006; Mohammadi et al., 2007a, 2007b, Farshadfar et al., 2012).
We begin with a general definition of data mining and explain how nonparametric data mining differs from the parametric method currently utilized by DoD cost estimators.
The K-NN resample approach was firstly utilized by [17] to develop a nonparametric disaggregation method.
The objective of the present study is to develop an appropriate statistical model to fit the trends and to calculate growth rates in area, production and productivity of cotton crop grown in Ahmedabad region of Gujarat state based on both parametric (Linear, non-linear and time-series) and nonparametric regression models.
To avoid a distributional and/or functional form specification error, we estimate the conditional density directly using nonparametric methods.
The power issue arises because the variation in the nonparametric CoVaR and MES estimators increases as the asymptotic tail dependence in stock returns strengthens.