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A compound-symmetry covariance matrix has a common diagonal entry and a common off-diagonal entry: $\Sigma =aI+cJ$. For a group of size $k$, its within-group eigenvalue is $a$ and its constant-direction eigenvalue is $a+kc$. Thus $a\ge 0$ and $a+kc\ge 0$ characterize positive semidefiniteness. When $c\ge 0$, an independent shared random intercept plus individual noise realizes this covariance. A group mean has variance $c+a/k$; a difference of two disjoint subgroup means within the same group cancels $c$.

At unit jump rate, this is $X_t={\sum }_{k=1}^{N_t}{\xi }_k$, where $N$ is a rate-one Poisson process and the independent marks are uniform signs. Equivalently it is the difference of two independent rate-$1/2$ Poisson processes. It is a Lévy process with characteristic function $\exp \{t(\cos \theta -1)\}$, mean zero, variance $t$, and Lévy characteristic exponent $1-\cos \theta$ in the negative-exponent convention. A general total jump rate rescales time.

A subject-specific random intercept and random slope may have a joint bivariate normal distribution with arbitrary positive-definite covariance matrix $D$. With independent normal measurement errors of variance ${\sigma }^2$, the marginal covariance between a person's readings at $s,t$ is $D_{00}+(s+t)D_{01}+st{D}_{11}$, plus ${\sigma }^2$ on the diagonal. Different subjects remain independent. Allowing $D_{01}$ distinguishes this model from an independent random-intercept and random-slope model.

For a fixed centered design matrix $X$, independent errors of variance ${\sigma }^2$, and objective $\Vert Y-X\beta {\Vert }^2+a\Vert \beta {\Vert }^2$, put $G=X^{T}X$ and $M=G+aI$. The ridge regression estimator hasThus its standard error alone does not justify centering a confidence interval for $\beta$ at the biased estimator. A penalty chosen from the responses also changes its sampling distribution.

Let $A,B$ be indicator random variables with conditional independence given a random variable $T$, and suppose $\mathbb{E}[A\mid T]=\mathbb{E}[B\mid T]=g(T)$. Then the law of total expectation gives $\mathbb{E}[AB]=\mathbb{E}[g(T{)}^2]$ and $\mathbb{E}[A]=\mathbb{E}[B]=\mathbb{E}[g(T)]$. Thus their covariance is the variance of $g(T)$ and is nonnegative. The result explains correlated component evolutionary states in a coeval binary population without correlation between initial masses.

The $n$th cumulant is the coefficient of $t^n/n!$ in the cumulant-generating function. The first two cumulants are the expected value and variance; the third is the third central moment.

In cycles per observation, frequency lies in $[-1/2,1/2]$ and $\gamma (h)=\int e^{2\pi ih\omega }{f}_c(\omega )d\omega$. Compared with angular-frequency density $f_a$, one has $f_c(\omega )=2\pi f_a(2\pi \omega )$. In particular, white noise of variance ${\sigma }^2$ has $f_c={\sigma }^2$.

For $m$ studies let $w_i=v_i^{-1}$, $W={\sum }_i{w}_i$, $C=W-{\sum }_i{w}_i^2/W$, and let $Q$ be Cochran's Q statistic. The moment estimate is ${\hat{\tau }}^2=\max \{0,[Q-(m-1)]/C\}$ when $C>0$. It estimates the variance of underlying study effects, not their sampling variance.

The design effect is a sampling variance under a complex design divided by the corresponding simple-random-sampling variance. For independent equal-size clusters of size $m$ with exchangeable intraclass correlation $\rho$, a common mean-estimation approximation is $1+(m-1)\rho$. It expresses the loss of effective sample size from positive correlation; finite-population and unequal-cluster-size designs require their own calculation.

A photodetector bias frame is an exposure of effectively zero integration time used to estimate the electronic offset and read noise. For independent identical bias reads, the variance of their difference is twice the single-read variance; subtracting an estimated mean bias removes the offset but adds the uncertainty of that estimate.

Detector conversion gain is the number of collected Electrons per analogue-to-digital unit. With independent Poisson distribution photoelectron counts, the shot-noise variance in count units is the mean count divided by the gain. Detected-photon and Electron conventions agree only when each detected photon contributes one Electron.

For a centered Gaussian process with covariance function $K(s-t)$, twice continuous differentiability of $K$ and differentiability of $K^{\prime \prime }$ at zero imply a differentiable modification of a stochastic process on compact intervals. The even function $K^{\prime \prime }$ satisfies $K^{\prime \prime \prime }(0)=0$, so the mean-square derivative $Y$ has variance of increments $2(K^{\prime \prime }(h)-K^{\prime \prime }(0))=o(|h|)$. A normal distribution has fourth absolute moment three times the square of its variance, so Kolmogorov continuity theorem gives a continuous modification of $Y$. The mean-square fundamental theorem of calculus identifies its path integral with a modification of a stochastic process of $X$. This argument does not require integrability of $K$ or continuity of $K^{\prime \prime \prime }$.

For a martingale with increments in $\{-1,0,1\}$, European call option prices satisfy $C(T+1,K)-C(T,K)=\frac{1}{2}{\sigma }^2(T,K)(C(T,K+1)-2C(T,K)+C(T,K-1))$. At positive-probability states, the variance and the upward/downward conditional transitions are determined by this identity; transitions at zero-probability states cannot be recovered.

On $[0,1]$, the sum $Q_n$ of squared adjacent dyadic increments of standard Brownian motion has $\mathbb{E}{Q}_n=1$ and $\mathrm{var}(Q_n)=2^{1-n}$. Therefore $Q_n\to 1$ in $L^2$. On an interval of length $t$, scaling gives mean $t$ and variance $2t^2{2}^{-n}$. Independence is used within each grid, not between different grids.

An Edgeworth expansion approximates a probability density function by a normal distribution multiplied by Hermite polynomials encoding higher cumulants. For a centered variable of variance ${\sigma }^2$ and third cumulant ${\kappa }_3$,The correction follows from inverse-transforming the cubic term of the cumulant-generating function. It is an asymptotic approximation near the bulk of the distribution; truncation can give a negative result in far tails.

Statistical efficiency compares the precision of procedures estimating the same estimand. For unbiased estimators, relative efficiency is commonly a ratio of their variances; lower variance means more information from the same sample size. An efficient estimator attains a relevant information lower bound. A baseline-adjusted linear regression can improve precision in a randomized controlled trial by removing prognostic variation.

An estimator is a function of observed data used to estimate a parameter or a specified function of parameters. Its definition cannot involve the unknown parameter. Its bias, variance, and expected loss describe its performance under repeated sampling.