New imperfect rankings models for ranked set sampling

Ranked set sampling is a sampling approach that leads to improved statistical inference in situations where the units to be sampled can be ranked relative to each other prior to formal measurement. This ranking may be done either by subjective judgment or according to an auxiliary variable, and it need not be completely accurate. In fact, results in the literature have shown that no matter how poor the quality of the ranking, procedures based on ranked set sampling tend to be at least as efficient as procedures based on simple random sampling. However, efforts to quantify the gains in efficiency for ranked set sampling procedures have been hampered by a shortage of available models for imperfect rankings. In this paper, we introduce a new class of models for imperfect rankings, and we provide a rigorous proof that essentially any reasonable model for imperfect rankings is a limit of models in this class. We then describe a specific, easily applied method for selecting an appropriate imperfect rankings model from the class.     

Reducing Refusal Rates for Telephone Interviews

In two experiments, refusal rates to telephone interviews werenot affected by substantial changes in the introductory remarksof the interviewer. A prior letter significantly lowered refusalrates in a third experiment. In all three, interviewer sex hadno effect.     

Predictive Inference for Big,Spatial, Non‐Gaussian Data: MODIS Cloud Data and its Change‐of‐Support

Remote sensing of the earth with satellites yields datasets that can be massive in size, nonstationary in space, and non‐Gaussian in distribution. To overcome computational challenges, we use the reduced‐rank spatial random effects (SRE) model in a statistical analysis of cloud‐mask data from NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on board NASA's Terra satellite. Parameterisations of cloud processes are the biggest source of uncertainty and sensitivity in different climate models’ future projections of Earth's climate. An accurate quantification of the spatial distribution of clouds, as well as a rigorously estimated pixel‐scale clear‐sky‐probability process, is needed to establish reliable estimates of cloud‐distributional changes and trends caused by climate change. Here we give a hierarchical spatial‐statistical modelling approach for a very large spatial dataset of 2.75 million pixels, corresponding to a granule of MODIS cloud‐mask data, and we use spatial change‐of‐Support relationships to estimate cloud fraction at coarser resolutions. Our model is non‐Gaussian; it postulates a hidden process for the clear‐sky probability that makes use of the SRE model, EM‐estimation, and optimal (empirical Bayes) spatial prediction of the clear‐sky‐probability process. Measures of prediction uncertainty are also given.     

Pearson-Tukey Three-Point Approximations Versus Monte Carlo Simulation

Accuracy of the Pearson-Tukey three-point approximation is measured in units of standard deviation and compared with that of Monte Carlo simulation. Using a variety of well-known distributions, comparisons are made for the mean of a random variable and for common functions of one and two random variables. Comparisons are also made for the mean of an assortment of risk-analysis (Monte Carlo) models drawn from the literature. The results suggest that the Pearson-Tukey approximation is a useful alternative to simulation in risk-analysis situations.     

An omnibus two-sample test for ranked-set sampling data

We develop an omnibus two-sample test for ranked-set sampling (RSS) data. The test statistic is the conditional probability of seeing the observed sequence of ranks in the combined sample, given the observed sequences within the separate samples. We compare the test to existing tests under perfect rankings, finding that it can outperform existing tests in terms of power, particularly when the set size is large. The test does not maintain its level under imperfect rankings. However, one can create a permutation version of the test that is comparable in power to the basic test under perfect rankings and also maintains its level under imperfect rankings. Both tests extend naturally to judgment post-stratification, unbalanced RSS, and even RSS with multiple set sizes. Interestingly, the tests have no simple random sampling analog.     

Suicide ideation,anxiety, and depression: Are children a protective factor for male Veterans?

ABSTRACT

Numerous mental health disorders plague our veterans when they return from deployment including anxiety and depressive disorders, which have been linked to elevated suicide risk when left untreated. Family factors, such as parenthood status, may serve as a protective factor against these mental health issues. This study examined the role of parenthood status of male veterans (N = 234) based on age of the child in order to determine whether a child’s age (minor children v. adult children) affects the likelihood of meeting diagnosis criteria for anxiety, depression, and suicide ideation after controlling for marital status. Three hierarchical binary logistic regression models were constructed to assess the predictive influence of children 18 years old and younger, children older than 18, and no children with the results indicating that parenthood status did meaningfully enhance the prediction of suicide ideation. Complete findings, clinical implications, and future considerations are discussed.     

Coaching für Habilitand/innen am Beispiel der LMU München

This practice report describes coachings for junior faculty members at LMU Munich. First, we describe the challenges junior faculty members are faced with at German universities and the HR development undertaken at LMU to help junior faculty cope with these challenges. In particular, we elaborate on coachings, i. e. we describe the process and topics most frequently mentioned therein. These include the sandwich position junior faculty members find themselves in, the oftentimes informal nature of their leadership position, and the requirement to react to conflicts. Finally, we present the various ways of responding to these challenges developed within the coaching process.     

Die Auswirkungen von Neid auf individuelle Leistungen: Ergebnisse einer Panelanalyse

Relative income differences are likely to lead to envy within a reference group. Envy in turn influences social behavior and individual performance. While positional concerns are apparent in daily life, empirical evidence is rare in the economic literature. This paper investigates the impact of the relative income position on individuals’ performance or productivity. As ?performance“ is difficult to measure we turn to soccer players whose performance has been well documented. The broad sample covers eight seasons of the German premier soccer league (Bundesliga) between 1995 and 2004, and includes 1040 players, a salary proxy and several performance variables. The results show that player performance is strongly affected by relative income position. A disadvantage in the relative income position reduces productivity. The larger the income differences within a team, the stronger are the effects of positional concern. Team composition also significantly affects behaviour.     

Tolerance‐based error reduction in multi‐agent bioassays

Some degree of error is inevitable in multi‐agent bioassays regardless of design or measurement technology. Estimation error can be reduced post facto by exploiting the matrix partial ordering of the bioassay survival estimates. The standard method for this is order‐restricted regression (ORR). If the joint action of the bioassay agents admits a tolerance‐based interpretation, additional structure beyond matrix partial ordering is available, leading to a new method of error reduction. This tolerance‐based error reduction (TBER) procedure almost always outperforms ORR. Like ORR, TBER applies to complete factorial bioassay designs and, using weighting, to incomplete designs.     

Optimal distribution-free confidence bands for a distribution function

Distribution-free confidence bands for a distribution function are typically obtained by inverting a distribution-free hypothesis test. We propose an alternate strategy in which the upper and lower bounds of the confidence band are chosen to minimize a narrowness criterion. We derive necessary and sufficient conditions for optimality with respect to such a criterion, and we use these conditions to construct an algorithm for finding optimal bands. We also derive uniqueness results, with the Brunn–Minkowski Inequality from the theory of convex bodies playing a key role in this work. We illustrate the optimal confidence bands using some galaxy velocity data, and we also show that the optimal bands compare favorably to other bands both in terms of power and in terms of area enclosed.