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.
methods may lack power as compared with more traditional approaches (Siegel & Castellan, 1988).
We avoid making any distributional assumptions with this nonparametric
approach, allowing for a flexible functional form.
To illustrate the shortcomings of identifying systemically risky firms as the financial firms with the largest [DELTA]CoVaR or MES estimates, we calculate the MES and [DELTA]CoVaR measures for all CRSP stocks with about 500 days of daily return data in the sample period 2006-2007 using the nonparametric
methods used in the literature.
However the things that will make this paper more interesting are that it's reviewed from different impendency: parametric and nonparametric
The operating characteristics of the nonparametric
Levene test for equal variances with assessment and evaluation data.
Stone, "Optimal global rates of convergence for nonparametric
regression," The Annals of Statistics, vol.
ERIC Descriptors: Observation; Research Methodology; Test Bias; Regression (Statistics); Nonparametric
Statistics; Scoring; Test Items; Simulation; Models; Correlation; Reliability
Non-parametric tests for comparisons between groups" shows other test statistics: Mann-Whitney as a nonparametric
alternative to the Independent-Samples T-tests, Kruskal-Wallis as a nonparametric
alternative to the One-Way ANOVA, and Wilcoxon Signed Ranks Test as a nonparametric
alternative to the Paired-Samples T-Test, which are used when samples are small or the distribution is not normal.
To observe the relationship between these two variables, we used two regression models that were then compared: the classic allometric model and a nonparametric
Regression analysis can be divided into two parts : 1) parametric regression and 2) nonparametric
He addresses the use of the multinomial model and noninformative Dirichlet priors in "model free" or nonparametric
Bayesian survey analysis, normal regression, analysis of variance, and binomial and multinomial data as well as alternatives to current frequentist nonparametric
methods and new goodness-of-fit methods for assessing parametric models and two-level variance component models and finite mixtures.