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Nowadays the term dose-response is used in many different contexts
and many different scientific disciplines including agriculture,
biochemistry, chemistry, environmental sciences, genetics,
pharmacology, plant sciences, toxicology, and zoology. In the 1940
and 1950s, dose-response analysis was intimately linked to
evaluation of toxicity in terms of binary responses, such as
immobility and mortality, with a limited number of doses of a toxic
compound being compared to a control group (dose 0). Later,
dose-response analysis has been extended to other types of data and
to more complex experimental designs. Moreover, estimation of model
parameters has undergone a dramatic change, from struggling with
cumbersome manual operations and transformations with pen and paper
to rapid calculations on any laptop. Advances in statistical
software have fueled this development. Key Features: Provides a
practical and comprehensive overview of dose-response analysis.
Includes numerous real data examples to illustrate the methodology.
R code is integrated into the text to give guidance on applying the
methods. Written with minimal mathematics to be suitable for
practitioners. Includes code and datasets on the book's GitHub:
https://github.com/DoseResponse. This book focuses on estimation
and interpretation of entirely parametric nonlinear dose-response
models using the powerful statistical environment R. Specifically,
this book introduces dose-response analysis of continuous,
binomial, count, multinomial, and event-time dose-response data.
The statistical models used are partly special cases, partly
extensions of nonlinear regression models, generalized linear and
nonlinear regression models, and nonlinear mixed-effects models
(for hierarchical dose-response data). Both simple and complex
dose-response experiments will be analyzed.
Nowadays the term dose-response is used in many different contexts
and many different scientific disciplines including agriculture,
biochemistry, chemistry, environmental sciences, genetics,
pharmacology, plant sciences, toxicology, and zoology. In the 1940
and 1950s, dose-response analysis was intimately linked to
evaluation of toxicity in terms of binary responses, such as
immobility and mortality, with a limited number of doses of a toxic
compound being compared to a control group (dose 0). Later,
dose-response analysis has been extended to other types of data and
to more complex experimental designs. Moreover, estimation of model
parameters has undergone a dramatic change, from struggling with
cumbersome manual operations and transformations with pen and paper
to rapid calculations on any laptop. Advances in statistical
software have fueled this development. Key Features: Provides a
practical and comprehensive overview of dose-response analysis.
Includes numerous real data examples to illustrate the methodology.
R code is integrated into the text to give guidance on applying the
methods. Written with minimal mathematics to be suitable for
practitioners. Includes code and datasets on the book's GitHub:
https://github.com/DoseResponse. This book focuses on estimation
and interpretation of entirely parametric nonlinear dose-response
models using the powerful statistical environment R. Specifically,
this book introduces dose-response analysis of continuous,
binomial, count, multinomial, and event-time dose-response data.
The statistical models used are partly special cases, partly
extensions of nonlinear regression models, generalized linear and
nonlinear regression models, and nonlinear mixed-effects models
(for hierarchical dose-response data). Both simple and complex
dose-response experiments will be analyzed.
Many real-world problems are inherently hierarchically structured.
The use of this structure in an agent's policy may well be the key
to improved scalability and higher performance on motor skill
tasks. However, such hierarchical structures cannot be exploited by
current policy search algorithms. We concentrate on a basic, but
highly relevant hierarchy - the mixed option' policy. Here, a
gating network first decides which of the options to execute and,
subsequently, the option-policy determines the action. Using a
hierarchical setup for our learning method allows us to learn not
only one solution to a problem but many. We base our algorithm on a
recently proposed information theoretic policy search method, which
addresses the exploitation-exploration trade-off by limiting the
loss of information between policy updates.
Wissenschaftlicher Aufsatz aus dem Jahr 2011 im Fachbereich BWL -
Unternehmensfuhrung, Management, Organisation,
Friedrich-Alexander-Universitat Erlangen-Nurnberg, Sprache:
Deutsch, Abstract: Diverse Studien zeigen, dass zwischen 40 und 90
Prozent aller neu eingefuhrten Produkte, je nach Branche, nach der
Markteinfuhrung scheitern (Gourville, 2006, S. 100). Ernst (2001,
S. 2f) beziffert die Misserfolgsquote bei der erfolgreichen
Umsetzung und Markteinfuhrung innovativer Produkte sogar auf 50 bis
80 Prozent. Vor diesem Hintergrund gewinnt das Marketing von
Innovationen - unternehmensintern wie auch unternehmensextern -
immer mehr an Bedeutung. Der Innovationskommunikation fallt hierbei
eine Schlusselrolle zu, da uber dieses Instrument die Innovation
nicht nur an den potentiellen Anwender, sondern auch an
Kooperationspartner etc. herangetragen wird. Im Rahmen dieses
Aufsatzes wird zunachst der theoretische Hintergrund zu
Innovationskommunikation aufgezeigt. Daruber hinaus wird
detailliert auf die externe Innovationskommunikation eingegangen,
welche die Verbreitung von Innovationen uber Kommunikationskanale
und die Erfolgsfaktoren fur die Kommunikation von Innovationen
umfasst. Darauf basierend wurde eine empirische Erhebung
durchgefuhrt, welche die Innovationskommunikation uber das Internet
und Web 2.0 zum Gegenstand hat. Als Untersuchungsobjekt wurden
insgesamt 21 deutsche und auslandische Unternehmen ausgewahlt, die
unterschiedlichen Branchen angehoren, ihren Schwerpunkt aber
jeweils in der industriellen Fertigung besitzen. Untersucht wurden
die Innovationskommunikation anhand der Kriterien
Vermittlungsbotschaft, Personifizierung, Gestaltung der Website
sowie Integration von sozialen Medien. Die Ergebnisse der
Auswertung werden in mehreren Teilergebnissen im Detail
dargestellt.
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