1,721,168 research outputs found
Smoothing techniques and bootstrap methods for multiparameter likelihood models
No full textnot availabl
Smoothing techniques and bootstrap methods for multiparameter likelihood models
No full textnot availabl
Goodness-of-fit tests for the frailty distribution in proportional hazards models with shared frailty
No full textFrailty models account for the clustering present in event time data. A proportional hazards model with shared frailties expresses the hazard for each subject. Often a one-parameter gamma distribution is assumed for the frailties. In this paper, we construct formal goodness-of-fit tests to test for gamma frailties. We construct a new class of frailty models that extend the gamma frailty model by using certain polynomial expansions that are orthogonal with respect to the gamma density. For this extended family, we obtain an explicit expression for the marginal likelihood of the data. The order selection test is based on finding the best fitting model in such a series of expanded models. A bootstrap is used to obtain p-values for the tests. Simulations and data examples illustrate the test’s performance.sponsorship: This work was supported by the IAP Research Network P6/03 of the Belgian State (Belgian Research Policy). For the simulations we used the infrastructure of the VSC-Flemish Supercomputer Center, funded by the Hercules Foundation and the Flemish Government department EWI. The time to first insemination data are retrieved from: http://www.vetstat.ugent.be/education/survivalpartim2/0008insem.dat. (IAP Research Network of the Belgian State (Belgian Research Policy)|P6/03, Hercules Foundation, Flemish Government department EWI)status: Publishe
Time to default in credit scoring using survival analysis: a benchmark study
No full textWe investigate the performance of various survival analysis techniques applied to ten actual credit data sets from Belgian and UK financial institutions. In the comparison we consider classical survival analysis techniques, namely the accelerated failure time models and Cox proportional hazards regression models, as well as Cox proportional hazards regression models with splines in the hazard function. Mixture cure models for single and multiple events were more recently introduced in the credit risk context. The performance of these models is evaluated using both a statistical evaluation and an economic approach through the use of annuity theory. It is found that spline-based methods and the single event mixture cure model perform well in the credit risk context
Bootstrapping multiparameter models, with applications to clustered binary data
No full textIt is shown how a one-step semiparametric bootstrap procedure can be applied to multiparameter models in different situations: for testing hypotheses, for the construction of simultaneous confidence intervals based on local polynomial smoothers and for improved estimation and bias correction. The method is illustrated on models for clustering binary dataThis project was partly supported by the NATO Collaborative Research Grant 950648. The research of Gerda Claeskens was supported by the Fund for Scientific Research - Flanders (Belgium) (FWO). We thank Professor R. Klein of the University of Wisconsin Madison, for kindly providing this data set (NIH grant EY 03083, Wisconsin Diabetic Retinopathy Study)
Bootstrapping multiparameter models, with applications to clustered binary data
No full textIt is shown how a one-step semiparametric bootstrap procedure can be applied to multiparameter models in different situations: for testing hypotheses, for the construction of simultaneous confidence intervals based on local polynomial smoothers and for improved estimation and bias correction. The method is illustrated on models for clustering binary dataThis project was partly supported by the NATO Collaborative Research Grant 950648. The research of Gerda Claeskens was supported by the Fund for Scientific Research - Flanders (Belgium) (FWO). We thank Professor R. Klein of the University of Wisconsin Madison, for kindly providing this data set (NIH grant EY 03083, Wisconsin Diabetic Retinopathy Study)
Predicting time-to-churn of prepaid mobile telephone customers using social network analysis
No full textMobile phone carriers in a saturated market must focus on customer retention to maintain profitability. This study investigates the incorporation of social network information into churn prediction models to improve accuracy, timeliness, and profitability. Traditional models are built using customer attributes, however these data are often incomplete for prepaid customers. Alternatively, call record graphs that are current and complete for all customers can be analysed. A procedure was developed to build the call graph and extract relevant features from it to be used in classification models. The scalability and applicability of this technique are demonstrated on a telecommunications data set containing 1.4 million customers and over 30 million calls each month. The models are evaluated based on ROC plots, lift curves, and expected profitability. The results show how using network features can improve performance over local features while retaining high interpretability and usability
Asymptotic properties of penalized spline estimators
No full textWe study the class of penalized spline estimators, which enjoy similarities to both regression splines, without penalty and with fewer knots than data points, and smoothing splines, with knots equal to the data points and a penalty controlling the roughness of the fit. Depending on the number of knots, sample size and penalty, we show that the theoretical properties of penalized regression spline estimators are either similar to those of regression splines or to those of smoothing splines, with a clear breakpoint distinguishing the cases. We prove that using fewer knots results in better asymptotic rates than when using a large number of knots. We obtain expressions for bias and variance and asymptotic rates for the number of knots and penalty parameter.status: Publishe
Macro-economic factors in credit risk calculations: including time-varying covariates in mixture cure models
No full textThe prediction of the time of default in a credit risk setting via survival analysis needs to take a high censoring rate into account. This rate is because default does not occur for the majority of debtors. Mixture cure models allow the part of the loan population that is unsusceptible to default to be modeled, distinct from time of default for the susceptible population. In this article, we extend the mixture cure model to include time-varying covariates. We illustrate the method via simulations and by incorporating macro-economic factors as predictors for an actual bank dataset
Simultaneous Confidence Bands for Penalized Spline Estimators
Full text linkIn this paper we construct simultaneous confidence bands for a smooth curve using penalized spline estimators. We consider three types of estimation methods: (i) as a standard (fixed effect) nonparametric model, (ii) using the mixed model framework with the spline coefficients as random effects and (iii) a Bayesian approach. The volume-of-tube formula is applied for the first two methods and compared from a frequentist perspective to Bayesian simultaneous confidence bands. It is shown that the mixed model formulation of penalized splines can help to obtain, at least approximately, confidence bands with either Bayesian or frequentist properties. Simulations and data analysis support the methods proposed. The R package ConfBands accompanies the paper
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