Tags

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.

× 624
"a measure of the strength of a phenomenon or a sample-based estimate of that quantity" [Wikipedia].
× 623
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…
× 617
a statistical software package. Use this tag for any on-topic question that (a) involves SAS either as a critical part of the question or expected answer, & (b) is not just about how to use SA…
× 617
A discrete, univariate distribution modelling the number of ${\rm Bernoulli}(p)$ trial successes until a specified number of failures occur.
× 610
A model for time series in which the conditional variance is time-varying and autocorrelated.
× 604
The act of generating a sequence of numbers or symbols randomly, or (almost always) pseudo-randomly; i.e., with lack of any predictability or pattern.
× 600
a stochastic process modelling time series, which specifies the value of the series linearly in terms of the previous values.
× 593
The process of fiting some statistical model to a particular set of data. Mostly done on a computer, and using varied numerical methods such as optimization or numerical integration, or simulation. …
× 592
An inquiry into the quality of a statistical test by calculating the power - the probability of rejecting the null hypothesis given that it is false - under certain circumstances. Power analysis is of…
× 588
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 instrument (s…
× 587
The science of statistics applied to the analysis of biological or medical data.
× 581
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…
× 579
A non-negative continuous probability distribution indexed by two strictly positive parameters.
× 576
A name given to the log-odds function, which maps probabilities to the real line.
× 576
A family of algorithms combining weakly predictive models into a strongly predictive model. The most common approach is called gradient boosting, and the most commonly used weak models are classificat…
× 572
The Wilcoxon rank sum test, also known as Mann-Whitney U test, is a non-parametric rank test to assess whether one of two samples has larger values than the other.
× 561
one of a number of regression models for dependent variables that are counts (non-negative integers). A more general model is negative binomial regression. Both have numerous var…
× 555
Vector Auto-Regression, a multiple time-series model / method. VAR is common in econometrics, & allows each time-series to be modeled based on its own previous values, & also the previous values of ea…
× 555
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 …
× 553
Usually refers to "z-standardization" which is shifting and rescaling data to assure they have zero mean and unit variance. Other "standardizations" are possible, too.
× 538
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…
× 537
Seasonality refers to the recurring fluctuation around the mean of a time-series for a given period of time, usually a calendar year.
× 535
the degree of closeness of the estimates to the true value. For a classifier, accuracy is the proportion of correct classifications. (This second usage is not good practice…
× 532
Refers to the variables used in a model to predict a response. This tag can also be used for $X$ variables in explanatory & descriptive modeling, not just predictive modeling. This same construct goes…
× 530
The quantiles of a distribution refer to points on its cumulative distribution function. Some common quantiles are quartiles and percentiles.
× 529
Measure of distance between distributions or variables, such as Euclidean distance between points in n-space.
× 516
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…
× 512
Joint probability distribution of several random variables gives the probability that all of them simultaneously lie in a particular region.
× 511
Skewness measures (or refers to) a degree of asymmetry in the distribution of a variable.
× 509
used for modelling systems that are assumed to be Markov processes with hidden (i.e. unobserved) states.
× 508
The uniform distribution describes a random variable that is equally likely to take any value in its sample space.
× 503
A regularization method for regression models that shrinks coefficients towards zero.
× 501
Classically, a Likert scale was composed of the sum of many Likert items (ordinal ratings of the amount of agreement with a statement), where all the items were equally valid. Today the term sometimes…
× 500
a neural network architecture that contains recurrent NN blocks that can remember a value for an arbitrary length of time.
× 498
Refers to the interface of statistics and computing; the use of algorithms and software for statistical purposes.
× 496
a simple probabilistic classifier based on applying Bayes' theorem with strong independence assumptions. A more descriptive term for the underlying probability model would …