Sentence examples for estimates as continuous from inspiring English sources

For use in further calculations, such as variance-stabilized normalization [ 21, 22], it is desirable to represent the various precision estimates as continuous functions, = f(x), where is the estimated precision value, x is the signal level, and f is some function parameterized with a relatively small set of coefficients.

In all models, the current age of the woman and her total number of living children were estimated as continuous variables, while region, education, religion, wealth index, exposure to media (radio and television), and residence (urban versus rural) were estimated as categorical independent variables.

We show that special Poisson regression models allow hazard functions to be estimated as continuous functions, which makes it possible to derive and calculate probabilities, expected length of life and expected gain in QALYs.

The estimated serum PFOA level at the time of pregnancy was skewed, so we considered serum PFOA estimates as a continuous log-transformed measure, a continuous untransformed measure, and in quintiles of exposure.

Applying the inversion technique developed by Yabuki and Matsuura (1992) to the excess pore-fluid pressures of the dataset, a static image of the 3-D excess pore-fluid pressure fields can be estimated as a continuous function defined in the mode region with estimation errors (e.g., Terakawa et al. 2012).

BMI-1 mRNA expression is estimated as a continuous variable, and this may increase the ability to detect differences.

Daily alcohol consumption was estimated as a continuous variable (g/day) based on the assumption that the concentrations of alcohol were 5% for beer, 12% for wine, 40% for liquor, and 25% for shochu (distilled spirit).

The CREIM was re-specified and re-estimated as a continuous-time model (CT-CREIM).

'Symptom presentation' was defined as actual symptom presentation (retrospectively recalled) or anticipated symptom presentation (hypothetically estimated) measured as continuous (time to presentation) or binary (did/did not present) variables.

The log linear regression model was estimated as OOPE was continuous variable and skewed in nature.

Risk estimates were calculated by considering the exposure metrics as continuous as well as categorical variables based on observed cut points in the data.