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begin{aligned} ASR=frac{hbox {Mean}left( {hbox {R}_{t+h}^{Portf} -Rfree} right) }{hbox {VaR}} end{aligned}where VaR is equal to N times (sigma _{Portf} ) and (hbox {N}=2.33) is the number of standard deviations associated with a 1 % level of probability, assuming that returns are normally distributed.
Our approach is mainly motivated by the following: a. the gradient of the objective function of CM is hard to compute, b. it is shown in [33, 34] that the subgradient of each U c * p ¯ var is equal to the optimal Lagrange multiplier corresponding to the constraint (14) in (ICP c ).
Finally, the global subgradient is ∇ p ¯ var U p ¯ var c * = ∑ c ∈ C λ c * p ¯ var. . the gradient of the objective function of CM is hard to compute, it is shown in [33, 34] that the subgradient of each U c * p ¯ var is equal to the optimal Lagrange multiplier corresponding to the constraint (14) in (ICP c ).
Then β ^ is equal to the first entry of (Q TV−1 Q)−1 Q TV−1 y and var is equal to the first entry of (Q TV−1 Q)−1.
Similar(50)
Where β is the regression coefficients of area-level poverty rate, Z0.90 and Z0.10 are the values of standard normal distribution at 90% and 10% percentiles, respectively (equals ± 1.2816) and Var is the variance at the corresponding ZIP-level poverty rate.
VaR is essentially a probability measurement based on historical prices.
The VaR is considerably higher.
% var is variance as % of mean.
The problem with VAR is that it uses historical.
The default is public, if only var is used; var is a synonym for public.
Where Var is the variance at ZIP5- or ZIP3-level, and Z0.75 is the value of standard normal distribution at 75% percentile (equals 0.6745).
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com