We are interested in estimating α by trying to minimize the conditional sum of squares Q = ∑ t = 1 T d H 2 ( X t, α X t − 1 ).
This paper presents the design philosophy of a fault tolerant conditional sum adder that uses hot-standby technique, which is an online swapping process of faulty components of a circuit by fault-free spares without interrupting the normal operation of the system.
We show that the proposed architectures performs faster by 30% or more than the circuit based on the conditional sum structure.
Then the model is 3 The conditional Poisson model is the same as model (3), except that instead of the parameters {αs} being estimated they are "conditioned out", by conditioning on the sum of events in each stratum.
Conditional likelihoods generalize the restricted maximum likelihood estimation for a discrete response by conditioning on the sum of the read counts per class and improve the statistical properties of dispersion estimates.
k = number of parameter estimates, RSS = residual sum of squares, AICc = Akaike information criterion adjusted for the sample size, w = conditional model probability (likelihood of model i divided by the sum of model likelihoods).
UV = UV index, Temp = average maximum temperature, RH = relative humidity, AtmP = atmospheric pressure, PC1 and PC2 = genetic principal components, k = number of parameter estimates, RSS = residual sum of squares, AICc = Akaike information criterion adjusted for the sample size, w = conditional model probability (likelihood of model i divided by the sum of model likelihoods).
The estimator is based on the analytical solutions of the first two conditional moments for the latent integrated volatility, the realization of which is effectively approximated by the sum of the squared high-frequency increments of the process.
This was confirmed by conditional backward elimination of predictive parameters.
