1,720,993 research outputs found
Bestimmung c-optimaler Versuchspläne in Modellen mit zufälligen Effekten, mit Anwendungen in der Pharmakokinetik
Full text linkMedizinische Studien im Bereich der Pharmakokinetik basieren in vielen Fällen auf speziellen
Modellen mit zufälligen Effekten, den sogenannten Populationsmodellen. In solchen Studien werden
jeweils mehrere Messungen an einer Anzahl verschiedener Patienten durchgeführt. Aus dem
Blickwinkel der optimalen Versuchsplanung führt dies zu methodischen Schwierigkeiten, da der
zufällige Effekt sowohl eine parameterabhängige Varianz der Beobachtungen als auch teilweise
korrelierte Daten zur Folge hat. Auf diese Weise sind zwei der Schlüsselannahmen klassischer
Versuchsplanungsliteratur verletzt. Es ist das Ziel dieser Arbeit, die bestehende Methodik
so anzupassen und zu ergänzen, dass auch diese Situationen betrachtet werden können. Da
die wichtigsten zu schätzenden Kenngrößen in der Pharmakokinetik bestimmte Summengrößen
der Parameter sind (z.B. die Fläche unter der Konzentrationskurve eines Präparates) wird der
Schwerpunkt dieser Arbeit auf c-optimalen Designs liegen, die die optimale Schätzung solcher
Größen erlauben.
Im einzelnen wird zunächst die geometrische Repräsentation optimaler Designs nach Elfving
(1952) so verallgemeinert, dass die beschriebene Situation abgedeckt ist. Im zweiten Schritt
wird die Äquivalenztheorie nach Kiefer (1974) und Pukelsheim (1993) auf das spezifische Modell
angewendet. Dritter Schritt ist die Anpassung multplikativer Algorithmen zur numerischen
Bestimmung optimaler Designs in dieser Situation, und als letztes Ergebnis wird das Konzept
asymptotisch optimaler Designs auf einen speziellen Fall korrelierter Beobachtungen angewendet
A geometric characterization of c-optimal designs for heteroscedastic regression
Full text linkWe consider the common nonlinear regression model where the variance as well as the mean is a parametric function of the explanatory variables. The c-optimal design problem is investigated in the case when the parameters of both the mean and the variance function are of interest. A geometric characterization of c-optimal designs in this context is presented, which generalizes the classical result of Elfving (1952) for c-optimal designs. As in Elfving's famous characterization c-optimal designs can be described as representations of boundary points of a convex set. However, in the case where there appear parameters of interest in the variance, the structure of the Elfving set is different. Roughly speaking the Elfving set corresponding to a heteroscedastic regression model is the convex hull of a set of ellipsoids induced by the underlying model and indexed by the design space. The c-optimal designs are characterized as representations of the points where the line in direction of the vector c intersects the boundary of the new Elfving set. The theory is illustrated in several examples including pharmacokinetic models with random effects. --c-optimal design,heteroscedastic regression,Elfving's theorem,pharmacokinetic models,random effects,locally optimal design,geometric characterization
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
A geometric characterization of c-optimal designs for regression models with correlated observations
Full text linkWe consider the problem of optimal design of experiments for random effects models, especially population models, where a small number of correlated observations can be taken on each individual, while the observations corresponding to different individuals can be assumed to be uncorrelated. We focus on c-optimal design problems and show that the classical equivalence theorem and the famous geometric characterization of Elfving (1952) from the case of uncorrelated data can be adapted to the problem of selecting optimal sets of observations for the n individual patients. The theory is demonstrated in a linear model with correlated observations and a nonlinear random effects population model, which is commonly used in pharmacokinetics
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Efficient algorithms for calculating optimal designs in pharmacokinetics and dose finding studies
Full text linkRandom effects models are widely used in population pharmacokinetics and dose finding
studies. In such models the presence of correlated observations (due to shared random
effects and possibly residual serial correlation) usually makes the explicit determination of optimal designs diffcult. In this paper we develop a class of multiplicative algorithms for
the numerical calculation of optimal experimental designs in such situations. In particular we demonstrate its application in a concrete example of a cross-over dose finding trial. Additionally, we show that the methodology can be modified to determine optimal designs where there exist some requirements regarding the minimal number of treatments for several (in some cases all) experimental conditions. AMS Subject Classi cation: 62K0
- …
