Sentence examples for models with explanatory from inspiring English sources

In many practical applications, parametric models with explanatory variables are used to estimate univariate survival functions for censored data.

For their analysis of TA and FA, the authors ran linear regression models with explanatory variables from TA, e.g., trend and momentum indicators based on past prices.

Heterogeneity in models with explanatory variables can be modelled, by assuming that Y has a parameter θ varying from individual to individual according to some regression model θ = η(x; β) + ε, where x is a vector of explanatory variables, β is a vector of regression coefficients, η is a function of a known form and ε has some known distribution.

*p < 0.05; **p < 0.01 (two-tailed) Models with explanatory variables are shown in table 4. In the model for consultation length, the main effect of list size is negative and statistically significant.

In order to determine trends in the number of cases attributed to injecting drugs, we performed multiple imputations of transmission category and region with iterative chained equations, using multinomial logistic regression models (with explanatory variables as year of diagnosis, age, sex, urban or rural residence, region and transmission category) for both imputed variables [ 30, 31].

Transferred models with explanatory variables from the original neighborhoods and recalibrated coefficients estimated the measured ln PNC) nearly as well as the original models and much better than transferred models without calibration (Table 5 and SI Tables S6 S9; Figures S5 S8).

Second, based on the CAPM model, we obtain a new model with explanatory ability for medium-term momentum and long-term reversal.

RR and their 95% credible intervals (95% CIs) were estimated on the basis of a Poisson conditional autoregressive Bayesian model with explanatory variables.

Possible explanations may include the following: first, in a model with explanatory variables and interaction terms, limited sample size can lead to an increase in the standard error of estimated coefficients; second, the effects of sibling sex composition are the result of rational choice in the face of budget constraints.

School-level risk factors were initially analyzed individually within a multilevel model with students at level 1 and schools at level 2. All risk factors with p<0.1 were then included in a multilevel logistic regression model for a multivariable analysis; again, individual students were at level 1, and schools were at level 2 of the model with explanatory variables at both levels.

The extent and configuration of native vegetation is typically mapped using remote sensing, and plant species and communities are typically mapped using statistical models built with explanatory variables derived from GIS layers.