MidhingeIn statistics, the midhinge is the average of the first and third quartiles and is thus a measure of location. Equivalently, it is the 25% trimmed mid-range or 25% midsummary; it is an L-estimator. The midhinge is related to the interquartile range (IQR), the difference of the third and first quartiles (i.e. ), which is a measure of statistical dispersion. The two are complementary in sense that if one knows the midhinge and the IQR, one can find the first and third quartiles.
L-estimatorIn statistics, an L-estimator is an estimator which is a linear combination of order statistics of the measurements (which is also called an L-statistic). This can be as little as a single point, as in the median (of an odd number of values), or as many as all points, as in the mean. The main benefits of L-estimators are that they are often extremely simple, and often robust statistics: assuming sorted data, they are very easy to calculate and interpret, and are often resistant to outliers.
Robust measures of scaleIn statistics, robust measures of scale are methods that quantify the statistical dispersion in a sample of numerical data while resisting outliers. The most common such robust statistics are the interquartile range (IQR) and the median absolute deviation (MAD). These are contrasted with conventional or non-robust measures of scale, such as sample standard deviation, which are greatly influenced by outliers.
WinsorisationLa winsorisation (winsorizing en anglais) est un procédé en statistiques visant à limiter l'impact de données aberrantes dans l'estimation d'un paramètre. Le nom winsorisation a été donné par John Tukey en l'honneur du biostatisticien . Considérons un jeu de données numériques et une statistique (la moyenne ou l'écart-type de par exemple). Winsoriser cette statistique revient à la calculer non pas sur le jeu de données original , mais sur un jeu de données modifié où une proportion des valeurs les plus extrêmes sont "écrasées" sur les quantiles de niveaux et La proportion doit être choisie par le statisticien en fonction de la robustesse souhaitée (un choix courant est ).
Interquartile meanThe interquartile mean (IQM) (or midmean) is a statistical measure of central tendency based on the truncated mean of the interquartile range. The IQM is very similar to the scoring method used in sports that are evaluated by a panel of judges: discard the lowest and the highest scores; calculate the mean value of the remaining scores. In calculation of the IQM, only the data between the first and third quartiles is used, and the lowest 25% and the highest 25% of the data are discarded. assuming the values have been ordered.
Interdecile rangeIn statistics, the interdecile range is the difference between the first and the ninth deciles (10% and 90%). The interdecile range is a measure of statistical dispersion of the values in a set of data, similar to the range and the interquartile range, and can be computed from the (non-parametric) seven-number summary. Despite its simplicity, the interdecile range of a sample drawn from a normal distribution can be divided by 2.56 to give a reasonably efficient estimator of the standard deviation of a normal distribution.
Sample mean and covarianceThe sample mean (sample average) or empirical mean (empirical average), and the sample covariance or empirical covariance are statistics computed from a sample of data on one or more random variables. The sample mean is the average value (or mean value) of a sample of numbers taken from a larger population of numbers, where "population" indicates not number of people but the entirety of relevant data, whether collected or not. A sample of 40 companies' sales from the Fortune 500 might be used for convenience instead of looking at the population, all 500 companies' sales.
Median absolute deviationIn statistics, the median absolute deviation (MAD) is a robust measure of the variability of a univariate sample of quantitative data. It can also refer to the population parameter that is estimated by the MAD calculated from a sample. For a univariate data set X1, X2, ..., Xn, the MAD is defined as the median of the absolute deviations from the data's median : that is, starting with the residuals (deviations) from the data's median, the MAD is the median of their absolute values. Consider the data (1, 1, 2, 2, 4, 6, 9).
Milieu de gamme (statistique)En statistique, le milieu de gamme ou le milieu extrême d'un ensemble de valeurs de données statistiques est la moyenne arithmétique des valeurs maximales et minimales dans un ensemble de données, défini comme: Le milieu de gamme est le point médian de la gamme ; en tant que tel, c'est une mesure de la tendance centrale. Le milieu de gamme est rarement utilisé dans l'analyse statistique pratique, car il manque d'efficacité en tant qu'estimateur pour la plupart des distributions d'intérêt, car il ignore tous les points intermédiaires et manque de robustesse, car les valeurs aberrantes le modifient considérablement.
Moyenne tronquéeUne moyenne tronquée, ou moyenne réduite, est une mesure statistique de centralité, similaire à la moyenne arithmétique et à la médiane, qui consiste à calculer une moyenne arithmétique en éliminant les valeurs extrêmes. Les , ont été inventées pour pallier la sensibilité des statistiques aux valeurs aberrantes, ce qu'on appelle la robustesse statistique.
