The potential of integrated modelling in conservation biology: A case study of the black‐footed albatross (Phoebastria nigripes)

Conservation biology aims at assessing the status of a population, based on information which is often incomplete. Integrated population modelling based on state‐space models appears to be a powerful and relevant way of combining into a single likelihood several types of information such as capture‐recapture data and population surveys. In this paper, the authors describe the principles of integrated population modelling and they evaluate its performance for conservation biology based on a case study, that of the black‐footed albatross, a northern Pacific albatross species suspected to be impacted by longline fishing     

A class of count models and a new consistent test for the Poisson distribution

We define a class of count distributions which includes the Poisson as well as many alternative count models. Then the empirical probability generating function is utilized to construct a test for the Poisson distribution, which is consistent against this class of alternatives. The limit distribution of the test statistic is derived in case of a general underlying distribution, and efficiency considerations are addressed. A simulation study indicates that the new test is comparable in performance to more complicated omnibus tests.     

Multivariate outlier detection in incomplete survey data: the epidemic algorithm and transformed rank correlations

Summary.  As a part of the EUREDIT project new methods to detect multivariate outliers in incomplete survey data have been developed. These methods are the first to work with sampling weights and to be able to cope with missing values. Two of these methods are presented here. The epidemic algorithm simulates the propagation of a disease through a population and uses extreme infection times to find outlying observations. Transformed rank correlations are robust estimates of the centre and the scatter of the data. They use a geometric transformation that is based on the rank correlation matrix. The estimates are used to define a Mahalanobis distance that reveals outliers. The two methods are applied to a small data set and to one of the evaluation data sets of the EUREDIT project.     

Overcoming the Effects of Variation in Infant Speech Segmentation: Influences of Word Familiarity

Previous studies have shown that 7.5‐month‐olds can track and encode words in fluent speech, but they fail to equate instances of a word that contrast in talker gender, vocal affect, and fundamental frequency. By 10.5 months, they succeed at generalizing across such variability, marking a clear transition period during which infants' word recognition skills become qualitatively more mature. Here we explore the role of word familiarity in this critical transition and, in particular, whether words that occur frequently in a child's listening environment (i.e., “Mommy” and “Daddy”) are more easily recognized when they differ in surface characteristics than those that infants have not previously encountered (termed nonwords). Results demonstrate that words are segmented from continuous speech in a more linguistically mature fashion than nonwords at 7.5 months, but at 10.5 months, both words and nonwords are segmented in a relatively mature fashion. These findings suggest that early word recognition is facilitated in cases where infants have had significant exposure to items, but at later stages, infants are able to segment items regardless of their presumed familiarity.     

Quadratic estimators of covariance components in a multivariate mixed linear model

It is known that the Henderson Method III (Biometrics 9:226–252, 1953) is of special interest for the mixed linear models because the estimators of the variance components are unaffected by the parameters of the fixed factor (or factors). This article deals with generalizations and minor extensions of the results obtained for the univariate linear models. A MANOVA mixed model is presented in a convenient form and the covariance components estimators are given on finite dimensional linear spaces. The results use both the usual parametric representations and the coordinate-free approach of Kruskal (Ann Math Statist 39:70–75, 1968) and Eaton (Ann Math Statist 41:528–538, 1970). The normal equations are generalized and it is given a necessary and sufficient condition for the existence of quadratic unbiased estimators for covariance components in the considered model.     

Wer das Stabilisieren vergisst: Warum eine schöne Abschlusspräsentation noch kein gutes Ende bedeutet

Many organizational change projects fail — despite a well thought out concept, professional project management and a binding implementation timetable. The reason why the expected success doesn’t materialize often lies with the missing maintenance. Once the project is officially completed and the client and project manager withdraw, the manner in which the organization will take up these changes is seldom systematically dealt with. Difficulties often arise when no discrete stabilizing measures are planned. We have derived four central action areas which will be clarified by way of practical examples. It will also be shown what is important to pay attention to in the stabilization phase so that a lasting success of the organizational change project can be ensured.