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.

Statistical analysis of datasets comprising several levels of hierarchy (e.g., students nested in classes nested in schools or hierarchical forecasting). For questions about mixed models use [mixed-mo…
A machine-learning library for Python. Use this tag for any on-topic question that (a) involves scikit-learn either as a critical part of the question or expected answer, & (b) is not just about how t…
Signals situations where one is concerned about achieving intended power and size when more than one hypothesis test is performed.
Descriptive statistics summarize features of a sample, such as mean and standard deviations, median and quartiles, the maximum and minimum. With multiple variables, may include correlations and crosst…
Prediction of unknown random quantities, using a statistical model.
1646 questions
Covariance is a quantity used to measure the strength and direction of the linear relationship between two variables. The covariance is unscaled, & thus often difficult to interpret; when scaled by th…
Autocorrelation (serial correlation) is the correlation of a series of data with itself at some lag. This is an important topic in time series analysis.
The residuals of a model are the actual values minus the predicted values. Many statistical models make assumptions about the error, which is estimated by the residuals.
Probability density function (PDF) of a continuous random variable gives the relative probability for each of its possible values. Use this tag for discrete probability mass functions (PMFs) too.
Usage and meaning of specific technical words/concepts in statistics.
Markov Chain Monte Carlo (MCMC) refers to a class of simulation methods for generating samples from a complex target distribution by generating random numbers from a Markov Chain whose stationary dist…
When the data present lack of information (gaps), i.e., are not complete. Hence, it is important to consider this feature when performing an analysis or test.
for any on-topic question that (a) involves Stata either as a critical part of the question or expected answer, & (b) is not just about how to use Stata.
Parameters associated with the particular levels of a covariate are sometimes called the “effects” of the levels. If the levels that are observed represent a random sample from the set of all possible…
Refers to the standard deviation of the sampling distribution of a statistic calculated from a sample. Standard errors are often required when forming confidence intervals or testing hypotheses about …
Convolutional Neural Networks are a type of neural network in which only subsets of possible connections between layers exist to create overlapping regions. They are commonly used for visual tasks.
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…
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 $θ$: $\operatorname{L}(θ | x)=\operat…
Cox proportional hazards regression is a semi-parametric method for survival analysis. No distributional form needs to be assumed, only that the effect of one-unit increase in a covariate is a constan…
The relationship between cause and effect.
A regularization method for regression models that shrinks coefficients towards zero, making some of them equal to zero. Thus lasso performs feature selection.
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…
Inclusion of additional constraints (typically a penalty for complexity) in the model fitting process. Used to prevent overfitting / enhance predictive accuracy.
A formalization of relationships between stochastically (randomly) related variables in the form of mathematical equations. DO NOT USE THIS TAG BY ITSELF: always include a more specific one.
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.
A stochastic process describes the evolution of random variables/systems over time and/or space and/or any other index set. It has applications in areas such as econometrics, weather, signal processin…
A binary variable takes one of two values, typically coded as "0" and "1".
Events (or random variables) are independent when information on some of them tells you nothing about the probability of occurrence (/ distribution) of the others. Please DO NOT use this tag for indep…
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…
for any on-topic question that (a) involves MATLAB either as a critical part of the question or expected answer, & (b) is not just about how to use MAT…
1258 questions
Methods focused on contrasting and combining results from different studies, in the hope of increasing precision and external validity.
An outlier is an observation that appears to be unusual or not well described relative to a simple characterization of a dataset. A discomfiting possibility is that these data come from a different po…
'Classification And Regression Trees'. CART is a popular machine learning technique, and it forms the basis for techniques like random forests and common implementations of gradient boosting machines.…
Using (pseudo-)random numbers and the Law of Large Numbers to simulate the random behavior of a real system.
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…
1174 questions
Usually "normalization" means re-expressing univariate data to make values lie within a specified range.
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