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.

Type to find tags:
× 302
A non-negative continuous probability distribution indexed by two strictly positive parameters.
× 299
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 …
× 299
a set of techniques from linguistics, artificial intelligence, machine learning and statistics that aim at processing and understanding human languages.
× 299
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…
× 296
Generalized Linear Mixed (effects) Models are typically used for modeling non-independent non-normal data (eg, longitudinal binary data).
× 294
used for modelling systems that are assumed to be Markov processes with hidden (i.e. unobserved) states.
× 293
really a special case of multiple linear regression, used in ANOVA-like settings with some continuous covariates in addition to the categorical ones.
× 292
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.
× 291
Measure of distance between distributions or variables, such as Euclidean distance between points in n-space.
× 291
said to have a high reliability if it produces similar results under consistent conditions. DO NOT confuse reliability with validity (see tag wiki).
× 290
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…
× 290
Group differences broadly refer to statistics which quantify the differences between two or more subpopulations.
× 290
The relationship between cause and effect.
× 289
Skewness measures (or refers to) a degree of asymmetry in the distribution of a variable.
× 288
a stochastic process modelling time series, which specifies the value of the series linearly in terms of the previous values.
× 288
Combining probabilities with Bayes' Theorem, especially as used for conditional inference.
× 287
Refers to any statistical complication or problem due to having few data.
× 281
a commercial spreadsheet program created by Microsoft.
× 278
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 …
× 277
The science of statistics applied to the analysis of biological or medical data.
× 276
Shifting and rescaling data to assure zero mean and unit variance.
× 276
Inclusion of additional terms (typically a penalty for complexity) in the model fitting process. Used to prevent overfitting / enhance predictive accuracy.
× 275
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 …
× 272
Refers to the probability distribution of parameters conditioned on data in Bayesian statistics.
× 272
decided upon after the data has been collected, as opposed to "a priori".
× 270
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-…
× 268
The quantiles of a distribution refer to points on its cumulative distribution function. Some common quantiles are quartiles and percentiles.
× 268
A distribution describing the time between events in a Poisson process; a continuous analogue of the geometric distribution.
× 267
'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 …
× 261
Refers to data generated from a distribution that has a countable sample space. Discrete data may be nominal (e.g. the distribution of race in a sample of individuals) or ordinal (e.g. the number of e…
× 261
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…
× 260
a test for goodness of fit of data to a distribution. It is often used to test whether a variable is normally distributed.
× 260
A measure of association between two binary variables equal to the odds of a 'positive' outcome in 1 variable divided by the odds in the other. The OR ranges (0, infinity). It has a strong connection …
× 260
Robustness in general refers to a statistic's insensitivity to deviations from its underlying assumptions (Huber and Ronchetti, 2009).
× 259
Seasonality refers to the recurring fluctuation around the mean of a time-series for a given period of time, usually a calendar year.
× 259
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…