Results 11 to 20 of about 829,002 (272)
Beyond first-order asymptotics for Cox regression [PDF]
, 2015 To go beyond standard first-order asymptotics for Cox regression, we develop parametric bootstrap and second-order methods. In general, computation of $P$-values beyond first order requires more model specification than is required for the likelihood ...
Bellio, Ruggero, Pierce, Donald A.
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Implementation of complex interactions in a Cox regression framework [PDF]
, 2003 The standard Cox proportional hazards model has been extended by functionally describable interaction terms. The first of which are related to neural networks by adopting the idea of transforming sums of weighted covariables by means of a logistic ...
Müller, M., Ulm, Kurt
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Generating Survival Times to Simulate Cox Proportional Hazards Models [PDF]
, 2003 This paper discusses techniques to generate survival times for simulation studies regarding Cox proportional hazards models. In linear regression models, the response variable is directly connected with the considered covariates, the regression ...
Augustin, Thomas +2 more
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Model Uncertainty Quantification in Cox Regression
Biometrics, 2023 Abstract We consider covariate selection and the ensuing model uncertainty aspects in the context of Cox regression. The perspective we take is probabilistic, and we handle it within a Bayesian framework. One of the critical elements in variable/model selection is choosing a suitable prior for model parameters.
Gonzalo García-Donato +2 more
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Probabilistic methods for seasonal forecasting in a changing climate: Cox-type regression models [PDF]
, 2010 For climate risk management, cumulative distribution functions (CDFs) are an important source of information. They are ideally suited to compare probabilistic forecasts of primary (e.g. rainfall) or secondary data (e.g. crop yields).
Maia, A.H.N., Meinke, H.B.
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Cox's Periodic Regression Model
The Annals of Statistics, 1988 Cox's regression model has been successfully used for censored survival data. It can be adapted to model a counting process having a periodic underlying intensity. In survival analysis, the asymptotic properties, as studied by \textit{P. K. Andersen} and \textit{R. D. Gill} [ibid.
Pons, O., de Turckheim, E.
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Hypoalbuminaemia predicts outcome in adult patients with congenital heart disease [PDF]
, 2015 Background In patients with acquired heart failure, hypoalbuminaemia is associated with increased risk of death. The prevalence of hypoproteinaemia and hypoalbuminaemia and their relation to outcome in adult patients with congenital heart disease (ACHD ...
Alonso-Gonzalez, R +11 more
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FLCRM: Functional Linear Cox Regression Model [PDF]
Biometrics, 2017 SummaryWe consider a functional linear Cox regression model for characterizing the association between time-to-event data and a set of functional and scalar predictors. The functional linear Cox regression model incorporates a functional principal component analysis for modeling the functional predictors and a high-dimensional Cox regression model to ...
Dehan Kong +3 more
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Regression calibration for Cox regression under heteroscedastic measurement error - Determining risk factors of cardiovascular diseases from error-prone nutritional replication data [PDF]
, 2003 For instance nutritional data are often subject to severe measurement error, and an adequate adjustment of the estimators is indispensable to avoid deceptive conclusions.
Augustin, Thomas +2 more
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Mimicking Cox-Regression [PDF]
Communications in Statistics - Theory and Methods, 2003 Abstract A class of objective functions, related to the Cox partial likelihood, that generates unbiased estimating equations is proposed. These equations allow for estimation of interest parameters when nuisance parameters are proportional to expectations.
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