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The weighted mean in this case is: ¯ = ¯ (=), (where the order of the matrix–vector product is not commutative), in terms of the covariance of the weighted mean: ¯ = (=), For example, consider the weighted mean of the point [1 0] with high variance in the second component and [0 1] with high variance in the first component.
The expected value of a random variable is the weighted average of the possible values it might take on, with the weights being the respective probabilities. More generally, the expected value of a function of a random variable is the probability-weighted average of the values the function takes on for each possible value of the random variable.
For normally distributed random variables inverse-variance weighted averages can also be derived as the maximum likelihood estimate for the true value. Furthermore, from a Bayesian perspective the posterior distribution for the true value given normally distributed observations and a flat prior is a normal distribution with the inverse-variance weighted average as a mean and variance ().
Kernel average smoother example. The idea of the kernel average smoother is the following. For each data point X 0, choose a constant distance size λ (kernel radius, or window width for p = 1 dimension), and compute a weighted average for all data points that are closer than to X 0 (the closer to X 0 points get higher weights).
The weighted average return on assets, or WARA, is the collective rates of return on the various types of tangible and intangible assets of a company.. The presumption of a WARA is that each class of a company's asset base (such as manufacturing equipment, contracts, software, brand names, etc.) carries its own rate of return, each unique to the asset's underlying operational risk as well as ...
Despite the newly abstract situation, this definition is extremely similar in nature to the very simplest definition of expected values, given above, as certain weighted averages. This is because, in measure theory, the value of the Lebesgue integral of X is defined via weighted averages of approximations of X which take on finitely many values ...
In statistics, there are many applications of "weighting": Weighted mean; Weighted harmonic mean; Weighted geometric mean; Weighted least squares
The basic definition is also known as WAULT to expiry to make the distinction clear. Depending on the market conditions, one might desire a high or low WAULT. For instance, if the rental market is strong and rents are rising, a low WAULT is desirable as that indicates that the current leases are going to expire or renegotiate in the short term ...