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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.

A stochastic process with the property that the future is conditionally independent of the past, given the present.
1239 questions
Factor analysis is a dimensionality reduction latent variable technique which replaces inter-correlating variables with a smaller number of continuous latent variables called factors. The factors are …
1221 questions
Situation when there is strong linear relationship among predictor variables, so that their correlation matrix becomes (almost) singular. This "ill condition" makes it hard to determine the unique rol…
A proportion is the fraction of some total that is of a particular kind, either (i) as a count of one type of thing out of a total count, or (ii) as a component of a continuous variable.
An instrument used to collect a sample from a population. Surveying often refers to sampling of human populations and is primarily done by administering questionnaires or interviewing individuals.
1207 questions
only for regression models in which the response is a nonlinear function of the parameters. Do not use this tag for nonlinear data transformation; use [data-transformation] for that inste…
Data with categorical values that can be ordered by magnitude, but the exact distance (spacing) between categories is undefined or unknown.
Data mining uses methods from artificial intelligence in a database context to discover previously unknown patterns. As such, the methods are usually unsupervised. It is closely related but not identi…
1184 questions
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…
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…
Non-constant variance along some continuum in a random process.
1179 questions
Goodness of fit tests indicate whether or not it is reasonable to assume that a random sample comes from a specific distribution.
1178 questions
A function used to quantify the difference between observed data and predicted values according to a model. Minimization of loss functions is a way to estimate the parameters of the model.
1167 questions
Structural Equation Modeling is a multivariate technique. It is based on formulating a set of linear relations between variables, some of which may be latent, and estimating the whole system, typicall…
The error of an estimate or prediction is its deviation from the true value, which may be unobservable (e.g., regression parameters), or observable (e.g., future realizations). Use the [error-message]…
1152 questions
Natural Language Processing is a set of techniques from linguistics, artificial intelligence, machine learning and statistics that aim at processing and understanding human languages.
1151 questions
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.
1136 questions
On evaluating models, either in-sample or out-of-sample.
1120 questions
The coefficient of determination, usually symbolized by $R^2$, is the proportion of the total response variance explained by a regression model. Can also be used for various pseudo R-squared proposed…
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 methods i…
1105 questions
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]).
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 …
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.
1075 questions
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 …
Generalized Linear Mixed (effects) Models are typically used for modeling non-independent non-normal data (eg, longitudinal binary data).
Many statistical methods assume data or a model's residuals are normally distributed. Use this tag for questions about the assumption & testing of normality, or about normality as a *property*. Use [n…
1053 questions
k-means is a method to partition data into clusters by finding a specified number of means, k, s.t. when data are assigned to clusters w/ the nearest mean, the w/i cluster sum of squares is minimized
1048 questions
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.
1037 questions
a sequence of objects or individuals collected from a larger (possibly infinite) population or process.
Generalized additive models (GAMs) are regressions that estimate nonlinear patterns in data. This tag should not be used with the `glm` tag unless the question explicitly deals with comparison of the …
In Bayesian statistics, the term 'posterior' refers to the probability distribution of a parameter conditioned on the observed data.
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…
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…
1002 questions
The autoregressive (AR) model is a stochastic process modelling time series, which specifies the value of the series linearly in terms of the previous values.
997 questions
Modeling error (especially sampling error) instead of replicable and informative relationships among variables improves model fit statistics, but reduces parsimony, and worsens explanatory and predict…
Gradient descent is 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…
986 questions
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