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etimología

Qué (quién) es variance component - definición

TYPE OF STATISTICAL MODEL

Random effects estimation; Random effects estimator; Random-effects models; Random-effect model; Random effect model; Variance components model; Variance components; Random effects; Random effect; Variance component; Random-effects meta-analysis

Bias–variance tradeoff

PROPERTY OF A SET OF PREDICTIVE MODELS WHEREBY MODELS WITH A LOWER BIAS IN PARAMETER ESTIMATION HAVE A HIGHER VARIANCE OF THE PARAMETER ESTIMATES ACROSS SAMPLES, AND VICE VERSA

Bias variance; Bias-variance tradeoff; Bias-variance dilemma; Bias–variance dilemma; Bias-variance decomposition; Bias–variance decomposition; Bias and variance tradeoff; Bias--variance tradeoff

In statistics and machine learning, the bias–variance tradeoff is the property of a model that the variance of the parameter estimated across samples can be reduced by increasing the bias in the estimated parameters.

https://en.wikipedia.org/wiki/Bias–variance_tradeoff

Allan variance

MEASURE OF FREQUENCY STABILITY IN CLOCKS AND OSCILLATORS

Allen variance; Allan deviation; Allan Variance; ADEV

The Allan variance (AVAR), also known as two-sample variance, is a measure of frequency stability in clocks, oscillators and amplifiers. It is named after David W.

https://en.wikipedia.org/wiki/Allan_variance

Random effects model

In statistics, a random effects model, also called a variance components model, is a statistical model where the model parameters are random variables. It is a kind of hierarchical linear model, which assumes that the data being analysed are drawn from a hierarchy of different populations whose differences relate to that hierarchy.

https://en.wikipedia.org/wiki/Random_effects_model

Wikipedia

Random effects model

In statistics, a random effects model, also called a variance components model, is a statistical model where the model parameters are random variables. It is a kind of hierarchical linear model, which assumes that the data being analysed are drawn from a hierarchy of different populations whose differences relate to that hierarchy. A random effects model is a special case of a mixed model.

Contrast this to the biostatistics definitions, as biostatisticians use "fixed" and "random" effects to respectively refer to the population-average and subject-specific effects (and where the latter are generally assumed to be unknown, latent variables).

https://en.wikipedia.org/wiki/Random effects model