Автор: Straub Название: Non-Life Insurance Mathematics ISBN: 3540187871 ISBN-13(EAN): 9783540187875 Издательство: Springer Рейтинг: Цена: 9357.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: The book gives a comprehensive overview of modern non-life actuarial science. It starts with a verbal description (i.e. without using mathematical formulae) of
the main actuarial problems to be solved in non-life practice.
Then in an extensive second chapter all the mathematical tools needed to solve these problems are dealt with -
now in mathematical notation. The rest of the book is devoted to the exact formulation of various problems and their possible solutions. Being a good mixture of practical problems and
their actuarial solutions, the book addresses above all two types of readers: firstly students (of mathematics, probability and statistics, informatics, economics) having some mathematical
knowledge, and secondly insurance practitioners who remember mathematics only from some distance.
Prerequisites are basic calculus and probability theory.
Описание: Presents the basics of functional analysis, as well as elements of variational equations (on the basis of bi-linear forms), including the Vishik-Lax-Milgram theorem and of generalized solutions of eliptic problems. Sobolev spaces and embedding theorems are introduced.
Автор: Anthony, M, , Biggs N. Название: Mathematics for economics and finance: methods and modelling ISBN: 0521559138 ISBN-13(EAN): 9780521559133 Издательство: Cambridge Academ Рейтинг: Цена: 7126.00 р. Наличие на складе: Поставка под заказ.
Описание: An introduction to mathematical modelling in economics and finance for students of both economics and mathematics. Throughout, the stress is firmly on how the mathematics relates to economics, illustrated with copious examples and exercises that will foster depth of understanding.
Автор: Gerber Hans U Название: Life Insurance Mathematics ISBN: 3642082858 ISBN-13(EAN): 9783642082856 Издательство: Springer Рейтинг: Цена: 6981.00 р. Наличие на складе: Есть у поставщика Поставка под заказ.
Описание: From the reviews: "The highly esteemed 1990 first edition of this book now appears in a much expanded second edition. ....As already hinted at above, this book provides the ideal bridge between the classical (deterministic) life insurance theory and the emerging dynamic models based on stochastic processes and the modern theory of finance.
Finance and insurance companies are facing a wide range of parametric statistical problems. Statistical experiments generated by a sample of independent and identically distributed random variables are frequent and well understood, especially those consisting of probability measures of an exponential type. However, the aforementioned applications also offer non-classical experiments implying observation samples of independent but not identically distributed random variables or even dependent random variables.
Three examples of such experiments are treated in this book. First, the Generalized Linear Models are studied. They extend the standard regression model to non-Gaussian distributions. Statistical experiments with Markov chains are considered next. Finally, various statistical experiments generated by fractional Gaussian noise are also described.
In this book, asymptotic properties of several sequences of estimators are detailed. The notion of asymptotical efficiency is discussed for the different statistical experiments considered in order to give the proper sense of estimation risk. Eighty examples and computations with R software are given throughout the text.
Examines a range of statistical inference methods in the context of finance and insurance applications
Presents the LAN (local asymptotic normality) property of likelihoods
Combines the proofs of LAN property for different statistical experiments that appears in financial and insurance mathematics
Provides the proper description of such statistical experiments and invites readers to seek optimal estimators (performed in R) for such statistical experiments
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