A tag is a keyword or label that categorizes your question with other, similar questions. Using the right tags makes it easier for others to find and answer your question.

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a set of techniques from linguistics, artificial intelligence, machine learning and statistics that aim at processing and understanding human languages.
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used in machine learning to generalize linear techniques to nonlinear situations, especially SVMs, PCA, and GPs. Not to be confused with [kernel-smoothing], for kernel density estim…
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A $k\times k$ matrix of covariances between all pairs of $k$ random variables. It is also called variance-covariance matrix or simply variance matrix.
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A non-negative continuous probability distribution indexed by two strictly positive parameters.
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said to have a high reliability if it produces similar results under consistent conditions. DO NOT confuse reliability with validity (see tag wiki).
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called continuous if its set of possible values is uncountable, and the chance that it takes any particular value is zero ($\text{P}(X = x) = 0$ for every real number $x$). A …
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a simple probabilistic classifier based on applying Bayes' theorem with strong independence assumptions. A more descriptive term for the underlying probability model would …
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Skewness measures (or refers to) a degree of asymmetry in the distribution of a variable.
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Measure of distance between distributions or variables, such as Euclidean distance between points in n-space.
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used for modelling systems that are assumed to be Markov processes with hidden (i.e. unobserved) states.
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The relationship between cause and effect.
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Is a property of a hypothesis testing method: the probability of rejecting the null hypothesis given that it is false, i.e. the probability of not making a type II error. The power of a test depends o…
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Group differences broadly refer to statistics which quantify the differences between two or more subpopulations.
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really a special case of multiple linear regression, used in ANOVA-like settings with some continuous covariates in addition to the categorical ones.
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a stochastic process modelling time series, which specifies the value of the series linearly in terms of the previous values.
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Refers to any statistical complication or problem due to having few data.
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Shifting and rescaling data to assure zero mean and unit variance.
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The science of statistics applied to the analysis of biological or medical data.
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Inclusion of additional terms (typically a penalty for complexity) in the model fitting process. Used to prevent overfitting / enhance predictive accuracy.
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Combining probabilities with Bayes' Theorem, especially as used for conditional inference.
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Gaussian processes refer to stochastic processes whose realization consists of normally distributed random variables, with the additional property that any finite collection of these random variables …
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Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models are used for time series in which the residual variance changes over time. The variance of the error term is assumed to follow …
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a commercial spreadsheet program created by Microsoft.
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the process of assessing whether the results of an analysis are likely to hold outside of the original research setting. DO NOT use this tag for discussing `validity` of a measurement or…
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The quantiles of a distribution refer to points on its cumulative distribution function. Some common quantiles are quartiles and percentiles.
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decided upon after the data has been collected, as opposed to "a priori".
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Kernel smoothing techniques, such as kernel density estimation (KDE) and Nadaraya-Watson kernel regression, estimate functions by local interpolation from data points. Not to be confused with [kernel-…
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'Large data' refers to situations where the number of observations (data points) is so large that it necessitates changes in the way the data analyst thinks about or conducts the analysis. (Not to be …
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Refers to the probability distribution of parameters conditioned on data in Bayesian statistics.
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A distribution describing the time between events in a Poisson process; a continuous analogue of the geometric distribution.
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fit curves (as in linear or non-linear regression) to data.
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Robustness in general refers to a statistic's insensitivity to deviations from its underlying assumptions (Huber and Ronchetti, 2009).
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Average most often refers to the arithmetic mean, but more generally to measures of central tendency that use most, or all, of the data values. Examples include trimmed mean, Winsorized mean, harmonic…
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"VAR" stands for *vector auto-regression*, which is a multiple time-series model / method. VAR is common in econometrics, & allows each time-series to be modeled based on its own previous values, & al…
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Seasonality refers to the recurring fluctuation around the mean of a time-series for a given period of time, usually a calendar year.
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a test for goodness of fit of data to a distribution. It is often used to test whether a variable is normally distributed.