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Title:

Unobserved heterogeneity in event history models

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article

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Unobserved heterogeneity in hazard rate models: a test and an illustration from a study of career mobility

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article

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Estimation of multivariate probit models: A mixed generalized estimating/pseudo-score equations approach and some finite sample results

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In the present paper a mixed approach is proposed for the simultaneously estimation of regression and correlation structure parameters in multivariate probit models using generalized estimating equations for the former and pseudo-score equations for the latter. The finite sample properties of the corresponding estimators are compared to estimato...

In the present paper a mixed approach is proposed for the simultaneously estimation of regression and correlation structure parameters in multivariate probit models using generalized estimating equations for the former and pseudo-score equations for the latter. The finite sample properties of the corresponding estimators are compared to estimators proposed by Qu, Williams, Beck and Medendorp (1992) and Qu, Piedmonte and Williams (1994) using generalized estimating equations for both sets of parameters via a Monte Carlo experiment. As a `reference' estimator for an equicorrelation model, the maximum likelihood (ML) estimator of the random effects probit model is calculated. The results show the mixed approach to be the most robust approach in the sense that the number of datasets for which the corresponding estimates converged was largest relative to the other two approaches. Furthermore, the mixed approach led to the most efficient non-ML estimators and to very efficient estimators for. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2012-10-02

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper46.ps.Z

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper46.ps.Z Minimize

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text

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en

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Multivariate binary data ; Panel data ; Simulation study

Multivariate binary data ; Panel data ; Simulation study Minimize

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310 Collections of general statistics *(computed)*

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

A combined GEE/Buckley-James method for estimating an Accelerated Failure Time Model of multivariate failure times

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The present paper deals with the estimation of a frailty model of multivariate failure times. The failure times are modeled by an Accelerated Failure Time Model including observed covariates and an unobservable frailty component. The frailty is assumed random and differs across elementary units, but is constant across the spells of a unit or a g...

The present paper deals with the estimation of a frailty model of multivariate failure times. The failure times are modeled by an Accelerated Failure Time Model including observed covariates and an unobservable frailty component. The frailty is assumed random and differs across elementary units, but is constant across the spells of a unit or a group. We develop an estimator (of the regression parameters) that combines the GEE approach (Liang and Zeger, 1986) with the Buckley-James estimator for censored data. This estimator is robust against violations of the correlation structure and the distributional assumptions. Some simulation studies are conducted in order to study the empirical performance of the estimator. Finally, the methods are applied to data of repeated appearances of malign ventricular arrhythmias at patients with implanted defibrillator. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-03-26

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper47.ps.Z

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper47.ps.Z Minimize

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text

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en

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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On the properties of GEE estimators in the presence of invariant covariates

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In this paper it is shown that the use of non-singular block invariant matrices of covariates leads to `generalized estimating equations' estimators (GEE estimators; Liang, K.-Y. & Zeger, S. (1986). Biometrika, 73(1), 13--22) which are identical regardless of the `working' correlation matrix used. Moreover, they are efficient (McCullagh, P. (198...

In this paper it is shown that the use of non-singular block invariant matrices of covariates leads to `generalized estimating equations' estimators (GEE estimators; Liang, K.-Y. & Zeger, S. (1986). Biometrika, 73(1), 13--22) which are identical regardless of the `working' correlation matrix used. Moreover, they are efficient (McCullagh, P. (1983). The Annals of Statistics, 11(1), 59--67). If on the other hand only time invariant covariates are used the efficiency gain in choosing the `correct' vs. an `incorrect' correlation structure is shown to be negligible. The results of a simple simulation study suggest that although different GEE estimators are no more identical and are no more as efficient as an ML estimator, the differences are still negligible if both time and block invariant covariates are present. Key words: Generalized estimating equations; Invariant covariates; Asymptotic properties. 1 Introduction The `generalized estimating equations' approach (GEE approach) proposed. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-13

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper13.ps.Z

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper13.ps.Z Minimize

Document Type:

text

Language:

en

Subjects:

Key words ; Generalized estimating equations ; Invariant covariates ; Asymptotic properties

Key words ; Generalized estimating equations ; Invariant covariates ; Asymptotic properties Minimize

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310 Collections of general statistics *(computed)*

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Metadata may be used without restrictions as long as the oai identifier remains attached to it.

Metadata may be used without restrictions as long as the oai identifier remains attached to it. Minimize

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Title:

Semiparametric EM-estimation of censored linear regressionmodels for durations

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This paper investigates the sensitivity of maximum quasi likelihood estimators of the covariate effects in duration models in the presence of misspecification due to neglected heterogeneity or misspecification of the hazard function. We consider linear models for r (T ) where T is duration and r is a known, strictly increasing function. This cla...

This paper investigates the sensitivity of maximum quasi likelihood estimators of the covariate effects in duration models in the presence of misspecification due to neglected heterogeneity or misspecification of the hazard function. We consider linear models for r (T ) where T is duration and r is a known, strictly increasing function. This class of models is also referred to as location-scale models. In the absence of censoring, Gould and Lawless (1988) have shown that maximum likelihood estimators of the regression parameters are consistent and asymptotically normally distributed under the assumption that the location-scale structure of the model is of the correct form. In the presence of censoring, however, model misspecification leads to inconsistent estimates of the regression coefficients for most of the censoring mechanisms that are widely used in practice. We propose a semiparametric EMestimator, following ideas of Ritov (1990), and Buckley and James (1979). This estimator is robust against misspecification and is highly recommended if there is heavy censoring and if there may be specification errors. We present the results of simulation experiments illustrating the performance of the proposed estimator. KEY WORDS: Censored linear regression models; accelerated failure time models; misspecified models semiparametric EM-estimation; simulation study. 1 Introduction Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-13

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper15.ps.Z

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text

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en

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310 Collections of general statistics *(computed)*

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Regression models with correlated binary response variables: A comparison of different methods in finite samples

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The present paper deals with the comparison of the performance of different estimation methods for regression models with correlated binary responses. Throughout, we consider probit models where an underlying latent continous random variable crosses a threshold. The error variables in the unobservable latent model are assumed to be normally dist...

The present paper deals with the comparison of the performance of different estimation methods for regression models with correlated binary responses. Throughout, we consider probit models where an underlying latent continous random variable crosses a threshold. The error variables in the unobservable latent model are assumed to be normally distributed. The estimation procedures considered are (1) marginal maximum likelihood estimation using Gauss-Hermite quadrature, (2) generalized estimation equations (GEE) techniques with an extension to estimate tetrachoric correlations in a second step, and, (3) the MECOSA approach proposed by Schepers, Arminger and Küsters (1991) using hierarchical mean and covariance structure models. We present the results of a simulation study designed to evaluate the small sample properties of the different estimators and to make some comparisons with respect to technical aspects of the estimation procedures and to bias and mean squared error of the estimators. The results show that the calculation of the ML estimator requires the most computing time, followed by the MECOSA estimator. For small and moderate sample sizes the calculation of the MECOSA estimator is problematic because of problems of convergence as well as a tendency of underestimating the variances. In large samples with moderate or high correlations of the errors in the latent model, the MECOSA estimators are not as efficient as ML or GEE estimators. The higher the `true ' value of an equicorrelation structure in the latent model and the larger the sample sizes are, the more is the efficiency gain of the ML estimator compared to the GEE and MECOSA estimators. Using the GEE approach, the ML estimates of tetrachoric correlations calculated in a second step are biased to a smaller extent than using the MECOSA approach. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-03-26

Source:

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper10.ps.Z

ftp://ftp.stat.uni-muenchen.de/pub/sfb386/paper10.ps.Z Minimize

Document Type:

text

Language:

en

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310 Collections of general statistics *(computed)*

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Title:

Credit risk factor . . .

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2013-12-13

Source:

http://www.bundesbank.de/download/bankenaufsicht/dkp/200302dkp_b.pdf

http://www.bundesbank.de/download/bankenaufsicht/dkp/200302dkp_b.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Credit Risk ; Credit Ratings ; Probability of Default ; Bank Regulation

Credit Risk ; Credit Ratings ; Probability of Default ; Bank Regulation Minimize

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Title:

Misspecified Copulas in Credit Risk Models:

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We would like to thank an anonymous referee for providing many helpful suggestions.

We would like to thank an anonymous referee for providing many helpful suggestions. Minimize

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2009-04-07

Source:

http://www.wiwi.uni-regensburg.de/hamerle/mitarbeiter/assistenten/roesch/HR_JoR2005.pdf

http://www.wiwi.uni-regensburg.de/hamerle/mitarbeiter/assistenten/roesch/HR_JoR2005.pdf Minimize

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text

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en

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Title:

Probit models: Regression parameter estimation using the ML principle despite misspecification of the correlation structure

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The Pennsylvania State University CiteSeerX Archives

Year of Publication:

2012-04-02

Source:

http://epub.ub.uni-muenchen.de/1461/1/paper_67.pdf

http://epub.ub.uni-muenchen.de/1461/1/paper_67.pdf Minimize

Document Type:

text

Language:

en

Subjects:

Probit models ; Regression parameter estimation

Probit models ; Regression parameter estimation Minimize

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