Some remarks on a ridge-type-estimator and good prior means

Pliskin (1987) compared modified ridge regression estimators based on prior information with respect to their mean square error matrices. A further characterization of good prior mean is given here, and the case of different ridge parameters is also considered.     

On the euclidean distance between biased estimators

The purpose of this paper is to extend the results achieved by Hsuan (1981) to a wide class of biased estimators. It is shown that in the case of multicollinearity ridge and Iteration estimator can be made very close to the principal component estimator whereas the shrunken estimator does not have this property.     

Rank covariance matrix estimation of a partially known covariance matrix

Classical multivariate methods are often based on the sample covariance matrix, which is very sensitive to outlying observations. One alternative to the covariance matrix is the affine equivariant rank covariance matrix (RCM) that has been studied in Visuri et al. [2003. Affine equivariant multivariate rank methods. J. Statist. Plann. Inference 114, 161–185]. In this article we assume that the covariance matrix is partially known and study how to estimate the corresponding RCM. We use the properties that the RCM is affine equivariant and that the RCM is proportional to the inverse of the regular covariance matrix, and hence reduce the problem of estimating the original RCM to estimating marginal rank covariance matrices. This is a great computational advantage when the dimension of the original data vector is large.     

The Effects of Ignoring Level Shifts on Systems Cointegration Tests

Summary: In this paper I analyse the effects of ignoring level shifts in the data generating process on systems cointegration tests that do not accommodate level shifts. I consider two groups of Likelihood Ratio tests based on procedures suggested by Johansen (1988) and Saikkonen and Lütkepohl (2000b). The Monte Carlo analysis reveals that ignoring level shifts reduces the tests’ sizes to zero and causes an important drop in the small sample power for increasing shift magnitudes. This suggests that one should apply test procedures, which take account of level shifts. * This paper is a revised and summarized version of Chapter 3 of my PhD thesis (Trenkler, 2002). I would like to thank two anonymous referees for helpful comments on the submitted paper. Furthermore, I am grateful to Christian Müller, Ralf Brüggemann, and Helmut Lütkepohl for many useful suggestions and comments on an earlier version of the paper and the corresponding chapter of my thesis. The research was supported by the Deutsche Forschungsgemeinschaft (DFG) through the SFB 373 “Quantification and Simulation of Economic Processes” and the SFB 649 “Economic Risk”.     

Quantal resp0nse assays by inverse regression

A general inverse regression procedure for estimating dose-response curves in quanta1 response assays is presented. Asymptotic distributional properties are developed. The special case is given, where after suitable transformation, the -dose response curve is linear. The inverse method has a decided advantage over the more classical methods in this case, both inflexibility and in ease of application. The procedure will be shown to be fully efficient, in the asymptotic context*     

Small area estimation under a multivariate linear model for repeated measures data

In this article, small area estimation under a multivariate linear model for repeated measures data is considered. The proposed model aims to get a model which borrows strength both across small areas and over time. The model accounts for repeated surveys, grouped response units, and random effects variations. Estimation of model parameters is discussed within a likelihood based approach. Prediction of random effects, small area means across time points, and per group units are derived. A parametric bootstrap method is proposed for estimating the mean squared error of the predicted small area means. Results are supported by a simulation study.     

The effects of sampling strategies on the small sample properties of the logit estimator

Empirical researchers face a trade-off between the lower resource costs associated with smaller samples and the increased confidence in the results gained from larger samples. Choice of sampling strategy is one tool researchers can use to reduce costs yet still attain desired confidence levels. This study uses Monte Carlo simulation to examine the impact of nine sampling strategies on the finite sample performance of the maximum likelihood logit estimator. The results show stratified random sampling with balanced strata sizes and a bias correction for choice-based sampling outperforms all other sampling strategies with respect to four small-sample performance measures.     

Local Influence Analysis in AB–BA Crossover Designs

The aim of this article is to develop methodology for detecting influential observations in crossover models with random individual effects. Various case‐weighted perturbations are performed. We obtain the influence of the perturbations on each parameter estimator and on their dispersion matrices. The obtained results exhibit the possibility to obtain closed‐form expressions of the influence using the residuals in mixed linear models. Some graphical tools are also presented.