Sentence examples for missing measurements where from inspiring English sources
A set of random variables satisfying certain probabilistic distributions is introduced to characterize the phenomenon of the missing measurements, where each sensor can have individual missing probability.
In the context of IoT/WSNs, one source of missing measurements is attributed to intentional subsampling, a scenario where the designer/operator reduces the sampling rate of the sensing infrastructure in order to increase the lifetime of the network.
In our example, we are justified in assuming DLs are fixed for type I missing measurements, but not for type II and III missing data where DLs depend on the amount of interfering compounds and are random variables.
There will be no imputation of missing data values; however, a mechanism relevant to the missing data will be taken into account by using Linear Mixed Models (LMM) with SAS PROC MIXED, where it is assumed, initially, that the missing measurements of HbA1c are missing at random.
Last-observation-carried-forward (LOCF) originated as a way of replacing missing measurements for use in classical repeated measurements analysis of variance (ANOVA) where complete data are required.
By using the state augmentation approach, the original system is converted into a new discrete system where the random one-step sensor delay and missing measurements exist in the sensor output.
Both the random perturbations and the missing measurements are addressed in the system model, where the random perturbations are characterized by the multiplicative noises and the addressed phenomena of the missing measurements are modeled by a series of mutually independent Bernoulli random variables with individual occurrence probability.
Only 25 days have missing measurements.
The missing measurements are considered the finite-time framework.
The missing measurements appear in a random way which is governed by missing rates obeying a certain probability distribution.
The missing measurements have been attracting a great number of attention due to the fact that the measurements are missing when sensors temporally fail [30 34].
