Locally robust correlation coefficients

Robustness of correlation coefficients is studied in the framework of testing for independence, robustness being measured by the minimum asymptotic power over a suitable class of local alternatives. A minimax result is derived; the robust correlation coefficient is computed from suitably transformed and truncated observations. Moreover, the related approach of Huber (1977) is analyzed, and inadmissibility of his minimax solution is shown.     

A general robustness property of rank correlations

Rank correlations are shown to be generally robust in the sense that the tests for independence, which they naturally define, have weakly equicontinuous error probabilities. The ordinary sample correlation coefficient is not robust in this respect.     

Family Location and Caregiving Patterns from an International Perspective

We conduct a cross‐national econometric analysis of intra‐family location and caregiving patterns. Using European data, we first assess, from an international perspective, the relationship between family structure and the geographic proximity between adult children and their parents. We then examine whether differences in family structure are related to the amount of informal care adult children provide to their elderly parents. Lastly, we look for cross‐country differences in family location and caregiving patterns, and interpret observed differences in terms of heterogeneous institutional solutions to elderly care. Our results not only provide a new empirical perspective on the geography of the family, but also provide insights into how family‐related and institutional factors shape patterns of time transfers from adult children to elderly parents.     

Estimation of structural impulse responses: short-run versus long-run identifying restrictions

There is evidence that estimates of long-run impulse responses of structural vector autoregressive (VAR) models based on long-run identifying restrictions may not be very accurate. This finding suggests that using short-run identifying restrictions may be preferable. We compare structural VAR impulse response estimates based on long-run and short-run identifying restrictions and find that long-run identifying restrictions can result in much more precise estimates for the structural impulse responses than restrictions on the impact effects of the shocks.     

Optimal robust influence functions in semiparametric regression

Robust statistics allows the distribution of the observations to be any member of a suitable neighborhood about an ideal model distribution. In this paper, the ideal models are semiparametric with finite-dimensional parameter of interest and a possibly infinite-dimensional nuisance parameter.In the asymptotic setup of shrinking neighborhoods, we derive and study the Hampel-type problem and the minmax MSE-problem. We show that, for all common types of neighborhood systems, the optimal influence function can be approximated by the optimal influence functions for certain parametric models.For general semiparametric regression models, we determine in case of error-in-variables and in case of error-free-variables.Finally, the results are applied to Cox regression where we compare our approach to that of Bednarski [1993. Robust estimation in Cox's regression model. Scand. J. Statist. 20, 213–225] in a small simulation study and on a real data set.     

PREDICTING STOCK MARKET BEHAVIOR THROUGH RULE INDUCTION: AN APPLICATION OF THE LEARNING-FROM-EXAMPLE APPROACH*

An artificial intelligence-based rule-induction approach to the analysis of stock market prediction is presented. A single investment analyst was used as the expert for this study. Predicting intermediate fluctuations in the movement of the market for nonconservative investors was selected as the decision to analyze. Commercially available rule-induction software was used to generate rules that predicted the market calls of the market analyst and the actual movements of the market. Rules predicting actual market movement performed better than rules predicting the analyst's calls and better than the analyst himself. Such an approach may prove useful in designing a decision support system for market analysts or in improving the decision-making processes of such analysts. The dynamic nature of the stock market represents a substantially different decision environment than those previously analyzed by learning-from-example (LFE) techniques. This study provides insights into the limits and applications of LFE approaches.     

Assisted reproductive technologies (ART) in a model of fertility choice

We examine the relationship between assisted reproductive technologies (ART) and the microeconomics of fertility choice. Along the way, we develop a model consistent with between-country differences in overall fertility and fertility timing. Our analysis of ART centers around the distinction between biomedical and behavioral effects. While improvements in ART have the biomedical effect of raising fertility, they may cause some women who would otherwise have tried to have children earlier on in life to postpone childbirth to later in life when the conception success probability is lower. This behavioral effect of postponement may reduce the fertility rate.     

Testing for the Cointegrating Rank of a VAR Process with Level Shift at Unknown Time

A systems cointegration rank test is proposed that is applicable for vector autoregressive (VAR) processes with a structural shift at unknown time. The structural shift is modeled as a simple shift in the level of the process. It is proposed to estimate the break date first on the basis of a full unrestricted VAR model. Two alternative estimators are considered and their asymptotic properties are derived. In the next step the deterministic part of the process including the shift size is estimated and the series are adjusted by subtracting the estimated deterministic part. A Johansen type test for the cointegrating rank is applied to the adjusted series. The test statistic is shown to have a well‐known asymptotic null distribution that does not depend on the break date. The performance of the procedure in small samples is investigated by simulations.     

Performance of phase-I dose finding designs with and without a run-in intra-patient dose escalation stage

Dose-finding designs for phase-I trials aim to determine the recommended phase-II dose (RP2D) for further phase-II drug development. If the trial includes patients for whom several lines of standard therapy failed or if the toxicity of the investigated agent does not necessarily increase with dose, optimal dose-finding designs should limit the frequency of treatment with suboptimal doses. We propose a two-stage design strategy with a run-in intra-patient dose escalation part followed by a more traditional dose-finding design. We conduct simulation studies to compare the 3 + 3 design, the Bayesian Optimal Interval Design (BOIN) and the Continual Reassessment Method (CRM) with and without intra-patient dose escalation. The endpoints are accuracy, sample size, safety, and therapeutic efficiency. For scenarios where the correct RP2D is the highest dose, inclusion of an intra-patient dose escalation stage generally increases accuracy and therapeutic efficiency. However, for scenarios where the correct RP2D is below the highest dose, intra-patient dose escalation designs lead to increased risk of overdosing and an overestimation of RP2D. The magnitude of the change in operating characteristics after including an intra-patient stage is largest for the 3 + 3 design, decreases for the BOIN and is smallest for the CRM.     

Editorial: Ten Years After: The Third Sector and Civil Society in Central and Eastern Europe

VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations -