Synonyms for nonparametric_skew or Related words with nonparametric_skew
skewness excess_kurtosis kurtosis regression_coefficient kullback_leibler_divergence hyperbolic_tangent_function sample_covariance_matrix bispectrum quantile_function bessel_correction ωl δr ricci_scalar hellinger_distance shannon_entropy point_biserial_correlation chi_squared maximum_likelihood_estimator rényi_entropy chernoff_inequality σb pearson_correlation_coefficient normalised_impedance kl_divergence bivariate azuma_inequality spearman_correlation mean_squared_error monotone_likelihood leptokurtic multipole_moment biased_estimator pareto_distribution fisher_noncentral_hypergeometric_distribution cosine_similarity σ² studentized_range kolmogorov_smirnov_test weighted_sum infinitesimal_strain_tensor mahalanobis_distance deltap glaisher_kinkelin_constant homoscedastic asymptotic_variance cokurtosis cramér_theorem correlation_coefficient differential_entropy δijExamples of "nonparametric_skew" |
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The latter is a simple multiple of the nonparametric skew. |
The middle bound limits the nonparametric skew of a unimodal distribution to approximately ±0.775. |
For symmetric probability distributions the value of the nonparametric skew is 0. |
where "a" ≠ 0 and "b" are constants and "S"( "X" ) is the nonparametric skew of the variable "X". |
The denominator is a measure of dispersion. Replacing the denominator with the standard deviation we obtain the nonparametric skew. |
In the older notion of nonparametric skew, defined as formula_1 where "µ" is the mean, "ν" is the median, and "σ" is the standard deviation, the skewness is defined in terms of this relationship: positive/right nonparametric skew means the mean is greater than (to the right of) the median, while negative/left nonparametric skew means the mean is less than (to the left of) the median. However, the modern definition of skewness and the traditional nonparametric definition do not in general have the same sign: while they agree for some families of distributions, they differ in general, and conflating them is misleading. |
In 1964 van Zwet proposed a series of axioms for ordering measures of skewness. The nonparametric skew does not satisfy these axioms. |
where "ν" is the median of the distribution. Bowley dropped the factor 3 is from this formula in 1901 leading to the nonparametric skew statistic. |
Doodson in 1917 proved that the median lies between the mode and the mean for moderately skewed distributions with finite fourth moments. This relationship holds for all the Pearson distributions and all of these distributions have a positive nonparametric skew. |
Benford's law is an empirical law concerning the distribution of digits in a list of numbers. It has been suggested that random variates from distributions with a positive nonparametric skew will obey this law. |
The nonparametric skew is one third of the Pearson 2 skewness coefficient and lies between −1 and +1 for any distribution. This range is implied by the fact that the mean lies within one standard deviation of any median. |
Analyses have been made of some of the relationships between the mean, median, mode and standard deviation. and these relationships place some restrictions of the sign and magnitude of the nonparametric skew. |
where "F" is the cumulative distribution function of the distribution. These conditions have since been generalised and extended to discrete distributions. Any distribution for which this holds has either a zero or a positive nonparametric skew. |
A simple example illustrating these relationships is the binomial distribution with "n" = 10 and "p" = 0.09. This distribution when plotted has a long right tail. The mean (0.9) is to the left of the median (1) but the skew (0.906) as defined by the third standardized moment is positive. In contrast the nonparametric skew is -0.110. |
In statistics and probability theory, the nonparametric skew is a statistic occasionally used with random variables that take real values. It is a measure of the skewness of a random variable's distribution—that is, the distribution's tendency to "lean" to one side or the other of the mean. Its calculation does not require any knowledge of the form of the underlying distribution—hence the name nonparametric. It has some desirable properties: it is zero for any symmetric distribution; it is |
For example, when estimating a location parameter, for a symmetric distribution a symmetric L-estimator (such as the median or midhinge) will be unbiased. However, if the distribution has skew, symmetric L-estimators will generally be biased and require adjustment. For example, in a skewed distribution, the nonparametric skew (and Pearson's skewness coefficients) measure the bias of the median as an estimator of the mean. |
For example, when estimating a location parameter for a symmetric distribution, a trimmed estimator will be unbiased (assuming the original estimator was unbiased), as it removes the same amount above and below. However, if the distribution has skew, trimmed estimators will generally be biased and require adjustment. For example, in a skewed distribution, the nonparametric skew (and Pearson's skewness coefficients) measure the bias of the median as an estimator of the mean. |