In probability theory and statistics, a conditional variance is the variance of a random variable given the value(s) of one or more other variables. Particularly...

A conditional variance swap is a type of variance swap or swap derivative product that allows investors to take exposure to volatility in the price of...

total variance is a fundamental result in probability theory that expresses the variance of a random variable Y in terms of its conditional variances and...

the autoregressive conditional heteroskedasticity (ARCH) model is a statistical model for time series data that describes the variance of the current error...

all its random variables have the same finite variance; this is also known as homogeneity of variance. The complementary notion is called heteroscedasticity...

In probability theory and statistics, variance is the expected value of the squared deviation from the mean of a random variable. The standard deviation...

corresponding names such as the conditional mean and conditional variance. More generally, one can refer to the conditional distribution of a subset of a...

In probability theory, the conditional expectation, conditional expected value, or conditional mean of a random variable is its expected value evaluated...

matrix (also known as auto-covariance matrix, dispersion matrix, variance matrix, or variance–covariance matrix) is a square matrix giving the covariance between...

In statistics and machine learning, the bias–variance tradeoff describes the relationship between a model's complexity, the accuracy of its predictions...

inverting back to get the conditional covariance matrix. Note that knowing that x2 = a alters the variance, though the new variance does not depend on the...

swap, conditional variance swap, corridor variance swap, forward-start variance swap, option on realized variance and correlation trading. "Variance and...

the conditional variance of the outcome is not scalable to the identity matrix. When the conditional variance is known, then the inverse variance weighted...

specific conditional constructions, see the sections below on simple conditional, conditional progressive, conditional perfect, and conditional perfect...

statistics, the variance function is a smooth function that depicts the variance of a random quantity as a function of its mean. The variance function is...

Hierarchical linear modeling Fixed effects MINQUE Covariance estimation Conditional variance Panel analysis Baltagi, Badi H. (2008). Econometric Analysis of Panel...

a linear function of X {\displaystyle \mathbf {X} } and that the conditional variance of the error term given X {\displaystyle \mathbf {X} } is a known...

inverse gamma distribution over the variance, and a normal distribution over the mean, conditional on the variance) and with the same four parameters just...

by the zoning ordinance. Such a variance has much in common with a special-use permit (sometimes known as a conditional use permit). Some municipalities...

testing (FAST) is a variance-based global sensitivity analysis method. The sensitivity value is defined based on conditional variances which indicate the...

Jaehyuk; Wu, Lixin (July 2021). "The equivalent constant-elasticity-of-variance (CEV) volatility of the stochastic-alpha-beta-rho (SABR) model". Journal...

financial instruments. These are volatility swaps, variance swaps, conditional variance swaps, variance options, VIX futures for equities, and (with some...

or probability distribution is the square root of its variance. (For a finite population, variance is the average of the squared deviations from the mean...

the minimum-variance mean (for large normal samples), which is to say the variance of the median will be ~50% greater than the variance of the mean....

Analysis of variance (ANOVA) is a family of statistical methods used to compare the means of two or more groups by analyzing variance. Specifically, ANOVA...

minimum-variance unbiased estimator (MVUE) or uniformly minimum-variance unbiased estimator (UMVUE) is an unbiased estimator that has lower variance than...

expectation Conditional independence Conditional probability Conditional probability distribution Conditional random field Conditional variance Conditionality principle...

commonly, the conditional median or some other quantile is used. Like all forms of regression analysis, linear regression focuses on the conditional probability...

chance, but rather that the regression coefficient, conditional variance, and population variance, were interdependent quantities related by a simple...

Variance-based sensitivity analysis (often referred to as the Sobol’ method or Sobol’ indices, after Ilya M. Sobol’) is a form of global sensitivity analysis...