semi-parametric
The failure of the profile likelihood method for semi-parametric effective age models
... We consider a semi-parametric model for recurrent events. The model consists of an unknown hazard rate function, the infinite-dimensional parameter of the model, and a parametrically specified effective age ...
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A Semi-Parametric Factor Model of Interest Rates and Tests of the Affine Term Structure
... The most commonly used term structure models are factor mod- els with an ane structure where the drift and volatility functions are ane functions of the state variable process. This in turn imposes re- strictions on ...
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GDP nowcasting with ragged-edge data: a semi-parametric modeling
... This paper formalizes the process of forecasting unbalanced monthly data sets in order to ob- tain robust nowcasts and forecasts of quarterly GDP growth rate through a semi-parametric modelling. This ...
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Extracting a Common Signal in Tree Ring Widths with a Semi-parametric Bayesian Hierarchical Model
... called expected growth corresponds to exp(G t ). To eliminate the age-affect G t or equiva- lently the expected growth, an age-related trend is first estimated for each individual tree. This is classically done by ...
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Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Models for Time-to-Event Analysis Through the Concordance Index
... In this paper, we make an experimental compar- ison of semi-parametric (Cox proportional hazards model, Aalen additive model), parametric (Weibull AFT model), and machine learning models[r] ...
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Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Models for Time-to-Event Analysis Through the Concordance Index
... of semi-parametric (Cox proportional hazards model, Aalen additive model), parametric (Weibull AFT model), and machine learning methods (Random Survival Forest, Gradient Boosting Cox propor- tional ...
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Experimental Comparison of Semi-parametric, Parametric, and Machine Learning Methods for Time-to-Event Analysis Through the IPEC Score
... of semi-parametric (Cox proportional hazards model, Aalen additive model), parametric (Weibull AFT model), and machine learning methods (Random Survival Forest, Gradient Boosting Cox propor- tional ...
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Semi-parametric modeling of excesses above high multivariate thresholds with censored data
... non parametric angular measure, while marginal excesses above asymptotically large thresholds have a parametric ...flexible semi-parametric Dirichlet mix- ture model for angular measures is ...
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Choice between semi-parametric estimators of Markov and non-Markov multi-state models from coarsened observations: Choice between semi-parametric estimators of Markov and non-Markov multi-state models
... The semi-parametric approaches offer the greatest ...different semi-parametric models, such as stratified and non-stratified proportional hazard survival ...
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Semi-parametric estimation of the variogram scale parameter of a Gaussian process with stationary increments
... where C > 0, 0 < s < 2, and the remainder function r satisfies some hypothesis detailed further (see Sect. 2.1 ) and is a o(|h| s ) as h → 0. Note that, since s < 2, V is indeed not (2D + 2) differentiable. ...
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Data-driven semi-parametric detection of multiple changes in long-range dependent processes
... the parametric case (see Lavielle and Ludena, 2000). Monte-Carlo experiments illustrate the consistency of the estimators. When the number of changes is known, the theoretical results concerning the consistencies ...
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Semi-Parametric Weak Instrument Regressions with an Application to the Risk-Return Trade-off
... Keywords: Instrumental Variables, Weak Instruments, Local-to-Zero Analysis, LM Tests, Wald Tests, Risk Premium, Expectations, Semi-Parametric Models, Kernel. * Thanks go to two referees, the Editor, Peter ...
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Semi-parametric estimation of the variogram of a Gaussian process with stationary increments
... 6 Conclusion We have provided an in-depth analysis of the estimation of the scale parameter of a one-dimensional Gaussian process by quadratic variations. Indeed, the knowledge of this scale parameter is essential when ...
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Parental Income and School Attendance in a Low-Income Country: a semi-parametric analysis
... this parametric framework, our model has a Tobit model ...this parametric approach provides results consistent with those previ- ously obtained using semi-parametric ...
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GDP nowcasting with ragged-edge data : A semi-parametric modelling
... This paper formalizes the process of forecasting unbalanced monthly data sets in order to ob- tain robust nowcasts and forecasts of quarterly GDP growth rate through a semi-parametric modelling. This ...
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Simultaneous semi-parametric estimation of clustering and regression
... smoothed log-likelihood with respect to the number of classes leads us to consider K = 3 classes for both losses. We now compare the results obtained by the proposed method with K = 3 classes in a ...
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SMOOTH MIN-DIVERGENCE INFERENCE IN SEMI PARAMETRIC MODELS
... some semi parametric models through some speci…c class of statistical procedure, which have proved to be of valuable interest in parametric ...
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A semi-parametric approach to estimate risk functions associated with multidimensional exposure profiles: application to smoking and lung cancer.
... flexible semi-parametric approach, we were able to partition a European smoking population into 12 typ- ical groups corresponding to different combinations of smoking profiles associated with log odds ...
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Semi-parametric frailty model for clustered interval-censored data
... of parametric, semi-parametric and non-parametric approaches have already been proposed to analyze such data ( Peto , 1973 ; Turnbull , 1976 ; Komárek et ...
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A Semi-Parametric Factor Model for Interest Rates
... a semi-parametric procedure to model interest rates. In a semi-parametric approach one typically parameterizes the object of interest while leaving unspeci ed the rest of the ...a ...
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