Small-sample inference about variance and its transformations

We discuss minimum mean squared error and Bayesian estimation of the variance and its common transformations in the setting of normality and homoscedasticity with small samples, for which asymptotics do not apply. We show that permitting some bias can be rewarded by greatly reduced mean squared error. We apply borderline and equilibrium priors. The purpose of these priors is to reduce the onus on the expert or client to specify a single prior distribution that would capture the information available prior to data inspection. Instead, the (parametric) class of all priors considered is partitioned to subsets that result in the preference for different actions. With the family of conjugate inverse gamma priors, this Bayesian approach can be formulated in the frequentist paradigm, describing the prior as being equivalent to additional observations.Peer Reviewe

Small-sample inference about variance and its transformations

We discuss minimum mean squared error and Bayesian estimation of the variance and its common transformations in the setting of normality and homoscedasticity with small samples, for which asymptotics do not apply. We show that permitting some bias can be rewarded by greatly reduced mean squared error. We apply borderline and equilibrium priors. The purpose of these priors is to reduce the onus on the expert or client to specify a single prior distribution that would capture the information available prior to data inspection. Instead, the (parametric) class of all priors considered is partitioned to subsets that result in the preference for different actions. With the family of conjugate inverse gamma priors, this Bayesian approach can be formulated in the frequentist paradigm, describing the prior as being equivalent to additional observations.Peer Reviewe

Determining the Meaning of Parameters in Multilevel Models for Longitudinal Data

This paper is concerned with parameter interpretation in longitudinal, multilevel models. Models are described that consider repeated observations nested within individuals. These models typically first estimate subject-specific parameters for growth curves that describe the development of some observed variable overtime. Examples of such descriptors include polynomials. It is shown that interpretation of polynomial parameters can be facilitated by linear transformations. Examples of such transformations include centring (i.e. subtracting the mean from raw data). When parameters are specified such that they have no straightforward meaning at the first level of analysis, interpretation problems carry over to the second and higher levels. Therefore, proper specification of models at the first level is of utmost importance. Methods of transformation are introduced. Examples illustrate the method using data that describe children’s vocabulary development in the second year of life.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/66841/2/10.1080_016502598384234.pd

XRCC1–DNA polymerase ß interaction is required for efficient base excision repair.

X-ray repair cross-complementing protein-1 (XRCC1)-deficient cells are sensitive to DNA damaging agents and have delayed processing of DNA base lesions. In support of its role in base excision repair, it was found that XRCC1 forms a tight complex with DNA ligase III and also interacts with DNA polymerase ß (Pol ß) and other base excision repair (BER) proteins. We have isolated wild-type XRCC1–DNA ligase III heterodimer and mutated XRCC1–DNA ligase III complex that does not interact with Pol ß and tested their activities in BER reconstituted with human purified proteins. We find that a point mutation in the XRCC1 protein which disrupts functional interaction with Pol ß, affected the ligation efficiency of the mutant XRCC1–DNA ligase III heterodimer in reconstituted BER reactions. We also compared sensitivity to hydrogen peroxide between wild-type CHO-9 cells, XRCC1-deficient EM-C11 cells and EM-C11 cells transfected with empty plasmid vector or with plasmid vector carrying wild-type or mutant XRCC1 gene and find that the plasmid encoding XRCC1 protein, that does not interact with Pol ß has reduced ability to rescue the hydrogen peroxide sensitivity of XRCC1- deficient cells. These data suggest an important role for the XRCC1–Pol ß interaction for coordinating the efficiency of the BER process

On normal theory based inference for multilevel models with distributional violations

nonnormal data, normal estimating equation, rescaled statistic, sandwich-type covariance, random effect ANOVA, hierarchical linear models, structural equation models,