Analysis of Type I Error Rates of Univariate and Multivariate Procedures in Repeated Measures Designs

We compared the robustness of univariate and multivariate statistical procedures to control Type I error rates when the normality and homocedasticity assumptions were not fulfilled. The procedures we evaluated are the mixed model adjusted by means of the SAS Proc Mixed module, and Bootstrap-F approach, Brown–Forsythe multivariate approach, Welch–James multivariate approach, and Welch–James multivariate approach with robust estimators. The results suggest that the Kenward Roger, Brown–Forsythe, Welch–James, and Improved Generalized Aprroximate procedures satisfactorily kept Type I error rates within the nominal levels for both the main and interaction effects under most of the conditions assessed.     

How are Time-Dependent Childbearing Intentions Realized? Realization,Postponement, Abandonment,Bringing Forward

Our study aims to identify factors that facilitate or inhibit the realization of fertility intentions. The analysis uses data collected in the first two waves of a Hungarian longitudinal survey. Fertility intentions recorded at the first wave pertain to the subsequent 3-year period, just similar to the behavior variable measuring the realization of intentions, i.e., a birth within the 3-year period in question. For this analysis, we used the respondents’ demographic, socio-structural, and orientational traits recorded at the first interview. Our findings show that age, parity, and partnership play a determining role in the realization of fertility intentions, but employment status, religious affiliation, and overall life satisfaction all exhibit significant effects. A marked gender difference was detected not only with regard to employment status but in the area of values and orientations as well.     

Readouts for echo-state networks built using locally regularized orthogonal forward regression

Echo state network (ESN) is viewed as a temporal expansion which naturally give rise to regressors of various relevance to a teacher output. We illustrate that often only a certain amount of the generated echo-regressors effectively explain the teacher output and we propose to determine the importance of the echo-regressors by a joint calculation of the individual variance contributions and Bayesian relevance using the locally regularized orthogonal forward regression (LROFR). This information can be advantageously used in a variety of ways for an analysis of an ESN structure. We present a locally regularized linear readout built using LROFR. The readout may have a smaller dimensionality than the ESN model itself, and improves robustness and accuracy of an ESN. Its main advantage is ability to determine what type of an additional readout is suitable for a task at hand. Comparison with PCA is provided too. We also propose a radial basis function (RBF) readout built using LROFR, since flexibility of the linear readout has limitations and might be insufficient for complex tasks. Its excellent generalization abilities make it a viable alternative to feed-forward neural networks or relevance-vector-machines. For cases where more temporal capacity is required we propose well studied delay&sum readout.     

Bayesian Additive Regression Trees using Bayesian model averaging

Bayesian Additive Regression Trees (BART) is a statistical sum of trees model. It can be considered a Bayesian version of machine learning tree ensemble methods where the individual trees are the base learners. However, for datasets where the number of variables p is large the algorithm can become inefficient and computationally expensive. Another method which is popular for high-dimensional data is random forests, a machine learning algorithm which grows trees using a greedy search for the best split points. However, its default implementation does not produce probabilistic estimates or predictions. We propose an alternative fitting algorithm for BART called BART-BMA, which uses Bayesian model averaging and a greedy search algorithm to obtain a posterior distribution more efficiently than BART for datasets with large p. BART-BMA incorporates elements of both BART and random forests to offer a model-based algorithm which can deal with high-dimensional data. We have found that BART-BMA can be run in a reasonable time on a standard laptop for the “small n large p” scenario which is common in many areas of bioinformatics. We showcase this method using simulated data and data from two real proteomic experiments, one to distinguish between patients with cardiovascular disease and controls and another to classify aggressive from non-aggressive prostate cancer. We compare our results to their main competitors. Open source code written in R and Rcpp to run BART-BMA can be found at: https://github.com/BelindaHernandez/BART-BMA.git.     

A practical comparison of blinded methods for sample size reviews in survival data clinical trials

This paper presents practical approaches to the problem of sample size re-estimation in the case of clinical trials with survival data when proportional hazards can be assumed. When data are readily available at the time of the review, on a full range of survival experiences across the recruited patients, it is shown that, as expected, performing a blinded re-estimation procedure is straightforward and can help to maintain the trial's pre-specified error rates. Two alternative methods for dealing with the situation where limited survival experiences are available at the time of the sample size review are then presented and compared. In this instance, extrapolation is required in order to undertake the sample size re-estimation. Worked examples, together with results from a simulation study are described. It is concluded that, as in the standard case, use of either extrapolation approach successfully protects the trial error rates.     

A new mathematical approach for the estimation of the AUC and its variability under different experimental designs in preclinical studies

The aim of the present work was to develop a new mathematical method for estimating the area under the curve (AUC) and its variability that could be applied in different preclinical experimental designs and amenable to be implemented in standard calculation worksheets. In order to assess the usefulness of the new approach, different experimental scenarios were studied and the results were compared with those obtained with commonly used software: WinNonlin® and Phoenix WinNonlin®. The results do not show statistical differences among the AUC values obtained by both procedures, but the new method appears to be a better estimator of the AUC standard error, measured as the coverage of 95% confidence interval. In this way, the new proposed method demonstrates to be as useful as WinNonlin® software when it was applicable. Copyright © 2011 John Wiley & Sons, Ltd.     

The mandatory private pension pillar in Hungary: An obituary

In 1998, the left‐of‐centre government of Hungary carved out a second‐pillar mandatory private pension scheme from the original mono‐pillar public system. Participation in the two‐pillar system was optional for those who were already working, but mandatory for new entrants to the workforce. About 50 per cent of the workforce joined the second pillar voluntarily and another 25 per cent were mandated to do so by law between 1999 and 2010. The second pillar has not improved the financial stability of the social security system. Moreover, the international financial and economic crisis has highlighted the transition costs that are associated with moving, even if only partially, to a system of pre‐funding. In 2010, the conservative government de facto “nationalized” the second pillar, and it is to use part of the accumulated pension capital to reduce Hungary's excessive public debt and annual budget deficit and to compensate for income tax reductions.     

Cooperative Institutions and Inequality in the OECD: Bringing the Firm Back In*

Objective. To examine the relationship between firm‐level cooperation, inequality, and redistribution in 18 advanced industrialized democracies. Methods. The relationships are investigated using multiple regression analyses of institutional, political, and economic variables. Results. Multilevel models reveal that contrary to neocorporatism, firm‐level cooperative ties have significant inegalitarian effects, particularly in the distribution of pretax, pretransfer market income. The effects, however, are also felt in the distribution of posttax, posttransfer income. Conclusion. By paying attention to the effect of firm‐level cooperation, the study sheds new light on inequality in the OECD as a result of both market‐based and nonmarket coordination.