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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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Non-constant variance along some continuum in a random process.
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Refers to techniques for reducing a large number of variables or dimensions spanned by data to a smaller number of dimensions while preserving as much information about the data as possible. Prominent…
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An unambiguous list of computational steps involved in finding a solution to a class of problems.
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Given a random variable $X$ which arise from a parameterized distribution $F(X;θ)$, the likelihood is defined as the probability of observed data as a function of $θ: \text{L}(θ)=\text{P}(θ;X=x)$
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its deviation from the true value, which may be unobservable (e.g., regression parameters), or observable (e.g., future realizations). Use the [error-message]…
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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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only for regression models (q.v.) in which the response is a nonlinear functions of the *parameters* (not because it's a nonlinear function of the *predictors*).
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Data with categorical values that can be ordered by magnitude, but the exact distance (spacing) between categories is undefined or unknown.
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Inclusion of additional constraints (typically a penalty for complexity) in the model fitting process. Used to prevent overfitting / enhance predictive accuracy.
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For statistical topics which involve the assumption of linearity, for example, linear regression or linear mixed models, or for the discussion of linear algebra as applied to statistics.
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a subset of a population. Statistics, in general, is concerned with using samples to make inference about the parameters governing a larger (possibly infinite) population.
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AIC stands for the Akaike Information Criterion, which is one technique used to select the best model from a class of models using a penalized likelihood. A smaller AIC implies a better model.
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The difference between the expected value of a parameter estimator & the true value of the parameter. Do NOT use this tag to refer to the [bias-term] / [bias-node] (ie the [intercept]).
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A strictly stationary process (or time series) is one whose joint distribution is constant over time shifts. A weakly stationary (or covariance stationary) process or series is one whose mean and cova…
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Convergence generally means that a sequence of a certain sample quantity approaches a constant as the sample size tends to infinity. Convergence is also a property of an iterative algorithm to stabili…
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The coefficient of determination, usually symbolized by $R^2$, is the proportion of the total response variance explained by a regression model.
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a rectangular array of numbers, symbols, or expressions arranged in rows and columns. The individual items in a matrix are called its elements or entries.
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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 the covariance matrix.
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Refers to the conditions under which a statistics procedure yields valid estimates and/or inference. E.g., many statistical techniques require the assumption that the data are randomly sampled in some…
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Refers to a subset of data mining concerned with extracting information from data in the form of text by recognizing patterns. The goal of text mining is often to classify a given document into one of…
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normally distributed. Use this tag for questions about the assumption & testing of normality, or about normality as a *property*. Use [normal-distribution] for…
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The field of study concerning statistical methods that use space and spatial relationships (such as distance, area, volume, length, height, orientation, centrality and/or other spatial characteristics…
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a first-order iterative optimization algorithm. To find a local minimum of a function using gradient descent, one takes steps proportional to the negative of the gradient (or of th…
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In biostatistics, fixed-effects may mean population-average effects. In econometrics, fixed-effects may represent the observed quantities in terms of explanatory variables that are treated as if the q…
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A multivariate, discrete probability distribution used to describe the results of a random experiment where each of $n$ outcomes are placed into one of $k$ nominal categories.
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a multivariate technique popular in social sciences. It is based on formulating a set of linear relations between variables, some of which may be latent, and estimating…
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A proprietary cross-platform general-purpose statistical software package.
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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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Data organized into discrete categories or *classes* may present problems for certain analyses if the number of observations ($n$) belonging to each class is not constant across classes. Classes with …
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non-negative integers representing whole amounts.
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A set of dynamic strategies by which an algorithm can learn the structure of an environment online by adaptively taking actions associated with different rewards so as to maximize the rewards earned.
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In Bayesian statistics a prior distribution formalizes information or knowledge (often subjective), available before a sample is seen, in the form of a probability distribution. A distribution with la…
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Receiver Operating Characteristic, also known as the ROC curve.
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A discrete, univariate distribution modelling the number of ${\rm Bernoulli}(p)$ trial successes until a specified number of failures occur.
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The relationship between cause and effect.
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"a measure of the strength of a phenomenon or a sample-based estimate of that quantity" [Wikipedia].