Parametric and Semi-parametric Regression Models
Urbanization and Environmental Quality in Africa
... polynomial regression, and more generally parametric regression models, have been shown to have undesirable “nonlocal effects” (Magee, ...above parametric framework, a ...
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A semi-parametric regression model for analysis of middle censored lifetime data
... covariates. Regression models are commonly employed to study such ...used semi-parametric regression model is the well known proportional haz- ards model by David ...the ...
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A quasi-Monte Carlo comparison of developments in parametric and semi-parametric regression methods for heavy-tailed and non-normal data : with an application to healthcare costs
... flexible parametric models—distributions such as the three-parameter generalized gamma and the four-parameter generalized beta of the second kind ...these models encompass, whereas models with ...
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The semi-parametric midas models and some of their applications : the impact of news on the stock volatility
... introduce semi-parametric MIDAS ...the semi-parametric infinite ARCH estimation in Linton and Mammen (2005) I show that the asymptotic distribution of semi-parametric MIDAS ...
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... using parametric and semi-parametric models (Cox model), mentioned factors affecting the interval between marriage and the first birth that were marriage age and women’s education ...Cox ...
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Semi parametric Bayesian Partially Identified Models based on Support Function
... identified models currently developed in the ...a parametric likelihood function, which is assumed to be known by econometricians up to a finite dimensional ...econometric models usually only ...
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Semi-parametric risk prediction models for recurrent cardiovascular events in the LIPID study
... the semi-parametric models selected the same explanatory variables compared with the para- metric conditional frailty ...the regression coefficients and 95% confidence intervals were also ...
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umi-unc-1027.pdf
... hierarchical regression methods are increasingly being used. Hierarchical regression places a prior distribution on the exposure-specific regression coefficients in order to stabilize estimates and ...
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Instrumental variable estimation in semi parametric additive hazards models
... hazard models for censored sur- vival data. The semi-parametric additive hazard model which can include time-independent and time-dependent covariate effects is particularly suited for the two-stage ...
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Semi and non semi parametric models for reliability analysis
... time models, refer to Bradburn, ...used regression model in survival analysis Bender ...Cox regression model has been used to identify the relationship between the selected variables and the survival ...
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Modelling fertility in rural South Africa with combined nonlinear parametric and semi parametric methods
... of parametric and generalised linear regression, despite more recent innovation in statistical analysis in recent ...logistic regression techniques are commonly used, but are very constraining in ...
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Iterative Least Squares Estimator of Binary Choice Models: a Semi Parametric Approach
... The simulation results indicate that the estimator is, 1 easy-tocompute and fast, 2 insensitive to initial estimates, 3 appears to be \/-consistent and asymptotically normal, and, 4 bett[r] ...
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Regression Modeling of Longitudinal Outcomes With Outcome-Dependent Observation Times
... Longitudinal studies commonly assume that the data-collection schedule is independent of a sub- ject’s outcomes and measured or unmeasured characteristics. However, this independence as- sumption may be violated if ...
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... of biomarkers are Mixture Of Gaussian (MG) [36]. They algebraically showed that AUC and TPF are calculated by weighting parameters of each component of AUC’s and TPF’s of mixture elements. For a real problem, the ...
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Who benefits from reducing the cost of formality? Quantile regression discontinuity analysis
... the regression discontinu- ity approach and the quantile regression ...the regression discontinu- ity design and they propose an uniformly consistent estimator for the potential outcome distributions ...
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Financial Fraud Detection Model Based on Random Forest
... including parametric and non-parametric models to construct detection models and concluded that Random Forest has the highest accuracy and the non-parametric models have higher ...
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Survival Analysis By Using Cox Regression Model With Application
... which deals with analysis of time to events, such as death in biological organisms and failure in mechanical systems. This topic is or reliability analysis in engineering, and duration analysis or duration modeling in ...
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The Berry Esseen bounds of wavelet estimator for regression model whose errors form a linear process with a ρ mixing
... a semi-parametric regression model under some mild conditions, Liang and Li [] studied the asymptotic normality and the Berry-Esseen type bound of the estimator with linear process error, Li et ...
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A comparative study of mixture cure model
... model, parametric and nonparametric. For parametric models the survival data is described by a known non negative ...Survival models that take into account the existing of a cure fraction are ...
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Software reliability prediction : a multi-modelling approach
... That is, a number of parametric and nondata failure be the to the technique parametric models and recalibration should applied from the system of interest and by using the various analys[r] ...
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