31 relations: Bias of an estimator, Cauchy distribution, Consistent estimator, Data, Efficiency (statistics), Estimation theory, Estimator, Expected value, Heavy-tailed distribution, Indicator function, Interdecile range, Interquartile range, L-estimator, Level of measurement, Location parameter, Median, Median absolute deviation, Mixture distribution, Normal distribution, Outlier, Percentile, Range (statistics), Robust statistics, Scale factor, Scale parameter, Skewness, Standard deviation, Statistical dispersion, Statistics, Trimmed estimator, Variance.
In statistics, the bias (or bias function) of an estimator is the difference between this estimator's expected value and the true value of the parameter being estimated.
The Cauchy distribution, named after Augustin Cauchy, is a continuous probability distribution.
In statistics, a consistent estimator or asymptotically consistent estimator is an estimator—a rule for computing estimates of a parameter θ0—having the property that as the number of data points used increases indefinitely, the resulting sequence of estimates converges in probability to θ0.
Data is a set of values of qualitative or quantitative variables.
In the comparison of various statistical procedures, efficiency is a measure of quality of an estimator, of an experimental design, or of a hypothesis testing procedure.
Estimation theory is a branch of statistics that deals with estimating the values of parameters based on measured empirical data that has a random component.
In statistics, an estimator is a rule for calculating an estimate of a given quantity based on observed data: thus the rule (the estimator), the quantity of interest (the estimand) and its result (the estimate) are distinguished.
In probability theory, the expected value of a random variable, intuitively, is the long-run average value of repetitions of the experiment it represents.
In probability theory, heavy-tailed distributions are probability distributions whose tails are not exponentially bounded: that is, they have heavier tails than the exponential distribution.
In mathematics, an indicator function or a characteristic function is a function defined on a set X that indicates membership of an element in a subset A of X, having the value 1 for all elements of A and the value 0 for all elements of X not in A. It is usually denoted by a symbol 1 or I, sometimes in boldface or blackboard boldface, with a subscript specifying the subset.
In statistics, the interdecile range is the difference between the first and the ninth deciles (10% and 90%).
In descriptive statistics, the interquartile range (IQR), also called the midspread or middle 50%, or technically H-spread, is a measure of statistical dispersion, being equal to the difference between 75th and 25th percentiles, or between upper and lower quartiles, IQR.
In statistics, an L-estimator is an estimator which is an L-statistic – a linear combination of order statistics of the measurements.
Level of measurement or scale of measure is a classification that describes the nature of information within the values assigned to variables.
In statistics, a location family is a class of probability distributions that is parametrized by a scalar- or vector-valued parameter x_0, which determines the "location" or shift of the distribution.
The median is the value separating the higher half of a data sample, a population, or a probability distribution, from the lower half.
In statistics, the median absolute deviation (MAD) is a robust measure of the variability of a univariate sample of quantitative data.
In probability and statistics, a mixture distribution is the probability distribution of a random variable that is derived from a collection of other random variables as follows: first, a random variable is selected by chance from the collection according to given probabilities of selection, and then the value of the selected random variable is realized.
In probability theory, the normal (or Gaussian or Gauss or Laplace–Gauss) distribution is a very common continuous probability distribution.
In statistics, an outlier is an observation point that is distant from other observations.
A percentile (or a centile) is a measure used in statistics indicating the value below which a given percentage of observations in a group of observations fall.
In statistics, the range of a set of data is the difference between the largest and smallest values.
Robust statistics are statistics with good performance for data drawn from a wide range of probability distributions, especially for distributions that are not normal.
A scale factor is a number which scales, or multiplies, some quantity.
In probability theory and statistics, a scale parameter is a special kind of numerical parameter of a parametric family of probability distributions.
In probability theory and statistics, skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean.
In statistics, the standard deviation (SD, also represented by the Greek letter sigma σ or the Latin letter s) is a measure that is used to quantify the amount of variation or dispersion of a set of data values.
In statistics, dispersion (also called variability, scatter, or spread) is the extent to which a distribution is stretched or squeezed.
Statistics is a branch of mathematics dealing with the collection, analysis, interpretation, presentation, and organization of data.
In statistics, a trimmed estimator is an estimator derived from another estimator by excluding some of the extreme values, a process called truncation.
In probability theory and statistics, variance is the expectation of the squared deviation of a random variable from its mean.