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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.

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Generalized Linear Mixed (effects) Models are typically used for modeling non-independent non-normal data (eg, longitudinal binary data).
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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.
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A name given to the log-odds function, which maps probabilities to the real line.
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A model for time series in which the conditional variance is time-varying and autocorrelated.
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Refers to the probability distribution of parameters conditioned on data in Bayesian statistics.
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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.
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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. …
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The science of statistics applied to the analysis of biological or medical data.
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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…
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On evaluating models, either in-sample or out-of-sample.
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A non-negative continuous probability distribution indexed by two strictly positive parameters.
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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 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…
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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 instrument (s…
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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…
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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.
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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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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.
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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…
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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…
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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…
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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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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…
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Measure of distance between distributions or variables, such as Euclidean distance between points in n-space.
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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…
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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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Skewness measures (or refers to) a degree of asymmetry in the distribution of a variable.
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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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ordering data from highest to lowest or *vice versa.* For questions about *constructing* scores to use in ranking, please use the [rating] tag, too.
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Refers to the interface of statistics and computing; the use of algorithms and software for statistical purposes.
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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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Joint probability distribution of several random variables gives the probability that all of them simultaneously lie in a particular region.
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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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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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The uniform distribution describes a random variable that is equally likely to take any value in its sample space.
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a neural network architecture that contains recurrent NN blocks that can remember a value for an arbitrary length of time.