Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics (Statistics for Biology and Health)

This book, suitable for numerate biologists and for applied statisticians, provides the foundations of likelihood, Bayesian and MCMC methods in the context of genetic analysis of quantitative traits. Although a number of excellent texts in these areas have become available in recent years, the basic ideas and tools are typically described in a technically demanding style and contain much more detail than necessary. Here, an effort has been made to relate biological to statistical parameters throughout, and the book includes extensive examples that illustrate the developing argument.

Science & Math

Sörensen, Daniel

Gianola, Daniel

Biological Sciences

Springer

This book provides an introduction to computer-based methods for the analysis of genomic data. Breakthroughs in molecular and computational biology have contributed to the emergence of vast data sets, where millions of genetic markers for each individual are coupled with medical records, generating an unparalleled resource for linking human genetic variation to human biology and disease. Similar developments have taken place in animal and plant breeding, where genetic marker information is combined with production traits. An important task for the statistical geneticist is to adapt, construct and implement models that can extract information from these large-scale data. An initial step is to understand the methodology that underlies the probability models and to learn the modern computer-intensive methods required for fitting these models. The objective of this book, suitable for readers who wish to develop analytic skills to perform genomic research, is to provide guidance to take this first step. 
This book is addressed to numerate biologists who typically lack the formal mathematical background of the professional statistician. For this reason, considerably more detail in explanations and derivations is offered. It is written in a concise style and examples are used profusely. A large proportion of the examples involve programming with the open-source package R. The R code needed to solve the exercises is provided. The MarkDown interface allows the students to implement the code on their own computer, contributing to a better understanding of the underlying theory. 
Part I presents methods of inference based on likelihood and Bayesian methods, including computational techniques for fitting likelihood and Bayesian models. Part II discusses prediction for continuous and binary data using both frequentist and Bayesian approaches. Some of the models used for prediction are also used for gene discovery. The challenge is to find promising genes without incurring a large proportion of false positive results. Therefore, Part II includes a detour on False Discovery Rate assuming frequentist and Bayesian perspectives. The last chapter of Part II provides an overview of a selected number of non-parametric methods. Part III consists of exercises and their solutions. 
Daniel Sorensen holds PhD and DSc degrees from the University of Edinburgh and is an elected Fellow of the American Statistical Association. He was professor of Statistical Genetics at Aarhus University where, at present, he is professor emeritus.

Statistical Learning in Genetics: An Introduction Using R (Statistics for Biology and Health)

Dieses Buch zeigt einzelne Strategien, Bewertungen, das Risikocontrolling und den Financial-Engineering-Prozess auf und geht dabei explizit auf die verwendeten Derivate sowie die eingesetzten Kombinationsstrategien ein.

Financial Engineering: Certified Financial Engineer (German Edition)

Business & Money

Ernst, Dietmar

Häcker, Joachim

Bloss, Michael

Accounting

De Gruyter Oldenbourg

Management & Leadership

It is the aim of this book to train and educate financial experts, investment bankers, traders, financial advisors and natural scientists who are active in financial engineering. Financial engineering is a necessary skill in many sectors of financial industry. Knowledge of financial engineering improves career opportunities for financial experts and opens doors to new and highly interesting employment opportunities. The book comes with numerous Excel and VBA models and can be used as the basis for a training course.

Financial Engineering

Oldenbourg Wissenschaftsverlag

Motivated by Toyota's product development capabilities, Daniel Sörensen examines the question of how much to invest in pursuing parallel design alternatives. A real option to switch is modeled accounting for interproject correlations. Based upon economic theory, five principles for value-maximizing the product development process are presented.

The Automotive Development Process: A Real Options Analysis

Economics

Deutscher Universitätsverlag

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Books by Sörensen, Daniel