Selection of predictors and covariates
Missing ordinal covariates with informative selection
... Column 4 of Table 8 reports results from the missing covariate model and the extra controls specification. This specification exploits linkage of three datasets, NPD, LSYPE, and 2001 UK Census. As we discussed earlier, ...
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Selection of environmental covariates using stepwise regression
... used to find the lowest number of predictors that can correctly predict the dependent variable. The statistical criterion of optimizing the R2 of the included variables is used to apply variables to the regression ...
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Longitudinal variable selection by cross-validation in the case of many covariates
... Available covariates are age in years (AGE), a 3 level factor for birth cohort (BIRTH equals 1 if born in 1963-1967, 2 if born in 1955-1962 and 3 if born in 1955-1958), a 3 level factor for attained level of ...
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Logistic Regression with Missing Covariates -- Parameter Estimation, Model Selection and Prediction
... We first applied SAEM for logistic regression with all 14 predictors and for the whole dataset. The estimation obtained by SAEM was broadly similar to that obtained by multiple imputation. Next, we used the model ...
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Model Selection for Longitudinal Data With Time-Dependent Covariates Using Generalized Method of Moments
... all predictors, while model M 2 did not include BMI as a predictor of ...all predictors were ...all predictors lost the least amount of information available in the full ...
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Covariates in Pharmacometric Repeated Time-to-Event Models: Old and New (Pre)Selection Tools
... The Schoenfeld-like residual that was used here differs from the originally defined Schoenfeld residual, which is used to test the proportional hazard assumption of Cox regression models. As shown in Eq. 5, the weighted ...
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Selection and fusion of categorical predictors with L₀-type penalties
... Abstract: In regression modelling, categorical covariates have to be coded. Depending on the number of categorical covariates and on the number of levels they have, the number of coefficients can become ...
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Simultaneous regression shrinkage, variable selection and clustering of predictors with OSCAR
... cation covariates: the sum of cations and ...cation covariates together, giving a model with six distinct non-zero ...cation covariates are highly correlated and are all associated with the same ...
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Bayesian variable selection for high dimensional predictors and self-reported outcomes
... also covariates [ 7 ...of covariates with the time- to-event outcome of interest [ 12 ...variable selection in high-dimensional data, in which the number of features (p) far exceeds the number of ...
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Regularization and Model Selection with Categorial Predictors and Effect Modifiers in Generalized Linear Models
... all covariates which were available and meaningful for all women. Covariates which are postnatal ...employed covariates are found in Table ...
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A robust imputation method for missing responses and covariates in sample selection models
... Sample selection arises when the outcome of interest is partially observed in a ...the selection-t sample selection ...ent selection-t model- and performs better than models based on the ...
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Sleep quality and covariates as predictors of pain intensity among the general population in rural China
... regression model was used to predict the impact of sleep quality on pain intensity. Linear regression was derived from ordinary least squares estimation and requires that variables not have multicollinearity, which is a ...
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BTLLasso - A Common Framework and Software Package for the Inclusion and Selection of Covariates in Bradley-Terry Models
... on covariates. The covariates can be subject-specific or ...subject-object-specific covariates, that is, characteristics that vary both over subjects and over objects, are ...few covariates, ...
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Valid confidence intervals for post model selection predictors
... The rest of the paper is organized as follows. In Section 2, we introduce the models, the model-selection procedures, the design-dependent target, and the PoSI con…dence intervals for both the case where all ...
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Modeling Sample Selection for Durations with Time-Varying Covariates, With an Application to the Duration of Exchange Rate Regimes
... time-invariant covariates and those for TVCs, developing an estimator that accounts for sample selection in durations with TVCs requires a different approach than that used for continuous-time durations ...
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High-Dimensional Covariates
... high-dimensional covariates With high-dimensional covariates, it is often reasonable to assume that many of these covariates are not relevant for modeling the marginal mean of the response variable, ...
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Variable selection in linear regression models with large number of predictors
... variable selection and some popular regression meth- ods for data sets with large number of ...variable selection approach to produce automatic estimates for the regression ...variable selection ...
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Consistent group identification and variable selection in regression with correlated predictors
... variable selection have become integral elements in any ...correlated predictors are particularly challenging to ...variable selection while clustering groups of predictors ...existing ...
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Application of variable selection and dimension reduction on predictors of MSE’s development
... As conclusion Comparison of the results for reduced linear regressions model of vari- ables selected by association and correlation method Table 4 with variables selected by regression method Table 5 revealed that, the ...
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Predictors and Consequences of Faking in Personnel Selection : A Dual-Process Perspective
... directed IM tactic, and showed that interviewee self-promotion was positively linked to observer ratings of applicants’ competence. Similarly, results from mock and field interview settings demonstrate that applicants’ ...
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