| We introduce the skyline plot, a graphical, nonparametric estimate of demographic history. |
|
| Also, we used nonparametric correlation tests in bivariate analyses that included these variables. |
|
| He applied mathematical statistics to economics, using nonparametric methods. |
|
| It is only when nonparametric functions are used that the GAM bias is introduced. |
|
| The GAM issue only affects studies of short-term acute health effects based on nonparametric time-series models. |
|
| A nonparametric kernel density estimate is constructed by representing each sojourn by a kernel function positioned at the actual value of the sampled dwell time. |
|
| Statistical analysis was performed using parametric and nonparametric tests, depending on whether the data distribution could be assumed to be gaussian or not. |
|
| Unfortunately all three of these can be used to refer both to parametric and nonparametric modelling. |
|
| Another nonparametric test for detecting differences between two populations is the Mann-Whitney-Wilcoxon test. |
|
| In most cases, the nonparametric tests are much easier to apply and provide clearer interpretation than traditional parametric tests. |
|
| When forecast errors are correlated across models, the nonparametric weighting scheme yields the lowest meansquared errors. |
|
| In this model, the explanatory variables are represented separately as a nonparametric part and a parametric linear part. |
|
| The method is applied to a hospital graft failure dataset for selecting a nonparametric Fay-Herriot type model. |
|
| To avoid misspecifying such a model, we consider nonparametric estimation of the response probabilities by local polynomial regression. |
|
| Thus, nonparametric Spearman's ranking correlation matrix can be used to check the correlation of rankings between pairs of techniques. |
|
| The alternative of using classical polynomial regression is also considered and a criterion is proposed to decide whether the nonparametric approach to modeling should be preferred over the classical approach. |
|
| In cases where both parametric and nonparametric methods are applicable, statisticians usually recommend using parametric methods because they tend to provide better precision. |
|
| A few of the more commonly used nonparametric methods are described below. |
|
| Furthermore, the Kruskal-Wallis nonparametric test can be used to test the null hypothesis that different techniques generate the same distribution of efficiency scores. |
|
| It is difficult to assess exactly how strong this evidence is, however, and evidence not based on nonparametric GAM analysis should certainly be given more weight. |
|
|
|
| There are therefore numerous examples where it is impossible to tell if a model being described is actually a nonparametric GAM or just a GLM fit with the gam function. |
|
| The nonparametric distribution-free property of these new tests makes them robust to the presence of conditional heteroscedasticity, heavy tails, and outliers in the loss-differential series. |
|
| We propose an imputation method by first deriving some regression imputation models according to the nonresponse mechanism and then applying nonparametric regression imputation. |
|
| My aim is to present both parametric and nonparametric models, using tools such as copulas and interacting urn models. |
|
| A Kruskal-Wallis nonparametric test was used to test for differences in all characters with heterogenetic variances. |
|
