Mathematik, Informatik und Statistik - Open Access LMU - Teil 01/03

A nonlinear structural errors-in-variables model is investigated, where the response variable has a density belonging to an exponential family and the error-prone covariate follows a Gaussian distribu...

We investigate a stationary random cofficient autoregressive process. Using renewal type arguments tailor-made for such processes we show that the stationary distribution has a power-law tail. When th...

With the introduction of compulsory long term care (LTC) insurance in Germany in 1995, a large claims portfolio with a significant proportion of censored observations became available. In first part o...

In this paper we analyse data originating from the German Deep Drill Program. We model the amount of 'cataclastic rocks' in a series of measurements taken from deep drill samples ranging from 1000 up ...

In this paper we review certain aspects around the Value-at-Risk, which is nowadays the industry benchmark risk measure. As a small quantile (usually 1%) Value-at-Risk is closely related to extreme va...

Based on a reversible jump Markov Chain Monte Carlo (RJMCMC) algorithm which was developed by Fronk and Giudici (2000) to deal with model selection for Gaussian dags, we propose a new approach for the...

After a short introduction of the model, the missing mechanism and the method of inference some imputation procedures are introduced with special focus on the simulation experiment. Within this experi...

Do reported reservation wages correspond to the concept of reservation wages that economists have? Using panel data on British unemployed I calculate reservation wages from a search model and compare ...

The incidence of underweight amongst children under five in Western Africa has been increasing over the last decade (UNICEF, 2002). In Asia, where about two thirds of the world's underweight children ...

Dempster-Shafer theory allows to construct belief functions from (precise) basic probability assignments. The present paper extends this idea substantially. By considering SETS of basic probability as...

This paper studies Cox`s proportional hazards model under covariate measurement error. Nakamura`s (1990) methodology of corrected log-likelihood will be applied to the so called Breslow likelihood, wh...

Model selection in graphical models is still not fully investigated. The main difficulty lies in the search space of all possible models which grows more than exponentially with the number of variable...

The structural variant of a regression model with measurement error is characterized by the assumption of an underlying known distribution of the latent covariate. Several estimation methods, like reg...

Proportional hazard models for survival data, even though popular and numerically handy, suffer from the restrictive assumption that covariate effects are constant over survival time. A number of test...

We consider a Poisson model, where the mean depends on certain covariates in a log-linear way with unknown regression parameters. Some or all of the covariates are measured with errors. The covariates...

This paper considers an additive model y = f(x) + e when some observations on x are missing at random but corresponding observations on y are available. Especially for this model missing at random is ...

Nonignorable nonresponse is a common problem in bivariate or multivariate data. Here a selection model for bivariate normal distributed data (Y1 ; Y2) is proposed. The missingness of Y2 is supposed to...

This paper gives a detailed overview of the problem of missing data in parametric and nonparametric regression. Theoretical basics, properties as well as simulation results may help the reader to get ...

We investigate the geographical and socioeconomic determinants of childhood undernutrition in Malawi, Tanzania and Zambia, three neighboring countries in Southern Africa using the 1992 Demographic and...

A crucial task in modern genetic medicine is the understanding of complex genetic diseases. The main complicating features are that a combination of genetic and environmental risk factors is involved,...

The Poisson regression model is often used as a first model for count data with covariates. Since this model is a GLM with canonical link, regression parameters can be easily fitted using standard sof...

Generalized additive mixed models extend the common parametric predictor of generalized linear models by adding unknown smooth functions of different types of covariates as well as random effects. Fro...

We propose a new class of state space models for longitudinal discrete response data where the observation equation is specified in an additive form involving both deterministic and dynamic components...

In a polynomial regression with measurement errors in the covariate, which is supposed to be normally distributed, one has (at least) three ways to estimate the unknown regression parameters: one can ...

No abstract available!...

In this paper binary state space mixed models of Czado and Song (2001) are applied to construct individual risk profiles based on a daily dairy of a migraine headache sufferer. These models allow for ...

P-splines are an attractive approach for modelling nonlinear smooth effects of covariates within the generalized additive and varying coefficient models framework. In this paper we propose a Bayesian ...

Generalized linear models (GLMs) and semiparametric extensions provide a flexible framework for analyzing the claims process in non-life insurance. Currently, most applications are still based on trad...

When prior estimates of regression coefficients along with their stan¡ dard errors or their variance covariance matrix are available, they can be incorporated into the estimation procedure through min...

Assuming the nonavailability of some observations and the availability of some stochastic linear constraints connecting the coefficients in a linear regression, the technique of mixed regression estim...

We have considered the estimation of coefficients in a linear regression model when some responses on the study variable are missing and some prior information in the form of lower and upper bounds fo...

This paper deals with the application of the weighted mixed regression estimation of the coefficients in a linear model when some values of some of the regressors are missing. Taking the weight factor...

The success of a newly founded company or small business depends on various initial risk factors or staring conditions, respectively, like e.g. the market the business aims for, the experience and the...

We consider two consistent estimators for the parameters of the linear predictor in the Poisson regression model, where the covariate is measured with errors. The measurement errors are assumed to be ...

We estimate semiparametric regression models of chronic undernutrition (stunting) using the 1992 Demographic and Health Surveys (DHS) from Tanzania and Zambia. We focus particularly on the influence ...

This article presents a modified Newton method for minimizing the Generalized Cross-Validation criterion, a commonly used smoothing parameter selection method in nonparametric regression. The method ...

We consider online monitoring of sequentially arising data as e.g. met in clinical information systems. The general focus thereby is to detect breakpoints, i.e. timepoints where the measurement series...

We propose two approaches for the spatial analysis of cancer incidence data with additional information on the stage of the disease at time of diagnosis. The two formulations are extensions of commonl...

Semiparametrically structured models are defined as a class of models for which the predictors may contain parametric parts, additive parts of covariates with an unspecified functional form and intera...

Generalized linear mixed models are a common tool in statistics which extends generalized linear models to situations where data are hierarchically clustered or correlated. In this article the simple ...

This paper considers the problem of linear calibration and presents two estimators arising from a synthesis of classical and inverse calibration approaches. Their performance properties are analyzed e...

This paper analyses the impact of the Hungarian unemployment insurance (UI) benefit system on the speed of exit from unemployment to regular employment. The duration analysis relies on unemployment sp...

The analysis of the geographical distribution of disease on the scale of geographic areas such as administrative boundaries plays an important role in veterinary epidemiology. Prevalence estimates of ...

Smooth models became more and more popular over the last couple of years. Standard smoothing methods however can not cope with discontinuities in a function or its first derivative. In particular, thi...

Local polynomial modelling can be seen as a local fit of the data against the basis functions 1, x, ... , x^p. In this paper we extend this method to a wide range of other basis functions. We will foc...

This paper presents the unique position of A(n)-based nonparametric predictive inference within the theory of interval probability. It provides a completely new understanding, leading to powerful new ...

Building reserves for outstanding liabilities is an important issue in the financial statement of any insurance company. In this paper we present a new model for delay in claim settlement and to predi...

This paper discusses point estimation of the coefficients of polynomial measurement error (errors-in-variables) models. This includes functional and structural models. The connection between these mod...

This contribution studies the Cox model under covariate measurement error. Methods proposed in the literature to adjust for measurement error are reviewed. The basic structural and functional approach...

Usual sequential testing procedures often are very sensitive against even small deviations from the `ideal model' underlying the hypotheses. This makes robust procedures highly desirable. To rely on a...