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Modern Regression Techniques Using R: A Practical Guide
by Daniel B. Wright Kamala LondonStatistics is the language of modern empirical social and behavioural science and the varieties of regression form the basis of this language. Statistical and computing advances have led to new and exciting regressions that have become the necessary tools for any researcher in these fields. In a way that is refreshingly engaging and readable, Wright and London describe the most useful of these techniques and provide step-by-step instructions, using the freeware R, to analyze datasets that can be located on the books' webpage: www.sagepub.co.uk/wrightandlondon. Techniques covered in this book include multilevel modeling, ANOVA and ANCOVA, path analysis, mediation and moderation, logistic regression (generalized linear models), generalized additive models, and robust methods. These are all tested out using a range of real research examples conducted by the authors in every chapter. Given the wide coverage of techniques, this book will be essential reading for any advanced undergraduate and graduate student (particularly in psychology) and for more experienced researchers wanting to learn how to apply some of the more recent statistical techniques to their datasets. The Authors are donating all royalties from the book to the American Partnership for Eosinophilic Disorders.
Modern Series Methods in Econometrics and Statistics (Advanced Studies in Theoretical and Applied Econometrics #45)
by Jiti Gao Chaohua DongThis book introduces modern series methods with a focus on applications in econometrics and statistics. It explores how new orthogonal series techniques can address challenges in model building and estimation, particularly for variables with unbounded support, nonparametric nonstationary data, and high-dimensional models. By extending traditional series methods, which are typically limited to variables with bounded supports, this book provides tools to tackle emerging problems in econometrics and statistics effectively. The book is organized into the following key parts. Part one provides the mathematical foundation for modern series methods, offering the theoretical background needed for their application. Part two introduces fundamental econometric concepts, including conditional expectations and regression models, within the context of modern series techniques. The last part, part four examines advanced topics, such as the connections between series methods and generalized functions, and compares series methods with kernel methods, highlighting their respective strengths and use cases. With a balanced mix of theory and practical insights, this book is ideal for researchers, practitioners, and students looking to deepen their understanding of series methods and their applications in econometrics, statistics, and related fields.
Modern Solvers for Helmholtz Problems
by Domenico Lahaye Jok Tang Kees VuikThis edited volume offers a state of the art overview of fast and robust solvers for the Helmholtz equation. The book consists of three parts: new developments and analysis in Helmholtz solvers, practical methods and implementations of Helmholtz solvers, and industrial applications. The Helmholtz equation appears in a wide range of science and engineering disciplines in which wave propagation is modeled. Examples are: seismic inversion, ultrasone medical imaging, sonar detection of submarines, waves in harbours and many more. The partial differential equation looks simple but is hard to solve. In order to approximate the solution of the problem numerical methods are needed. First a discretization is done. Various methods can be used: (high order) Finite Difference Method, Finite Element Method, Discontinuous Galerkin Method and Boundary Element Method. The resulting linear system is large, where the size of the problem increases with increasing frequency. Due to higher frequencies the seismic images need to be more detailed and, therefore, lead to numerical problems of a larger scale. To solve these three dimensional problems fast and robust, iterative solvers are required. However for standard iterative methods the number of iterations to solve the system becomes too large. For these reason a number of new methods are developed to overcome this hurdle. The book is meant for researchers both from academia and industry and graduate students. A prerequisite is knowledge on partial differential equations and numerical linear algebra.
Modern Special Relativity: A Student's Guide with Discussions and Examples
by Johann RafelskiThis book presents Special Relativity in a language accessible to students while avoiding the burdens of geometry, tensor calculus, space-time symmetries, and the introduction of four vectors. The search for clarity in the fundamental questions about Relativity, the discussion of historical developments before and after 1905, the strong connection to current research topics, many solved examples and problems, and illustrations of the material in colloquial discussions are the most significant and original assets of this book. Importantly for first-time students, Special Relativity is presented such that nothing needs to be called paradoxical or apparent; everything is explained. The content of this volume develops and builds on the book Relativity Matters (Springer, 2017). However, this presentation of Special Relativity does not require 4-vector tools. The relevant material has been extended and reformulated, with additional examples and clarifications. This introduction of Special Relativity offers conceptual insights reaching well beyond the usual method of teaching relativity. It considers relevant developments after the discovery of General Relativity (which itself is not presented), and advances the reader into contemporary research fields. This presentation of Special Relativity is connected to present day research topics in particle, nuclear, and high intensity pulsed laser physics and is complemented by the current cosmological perspective. The conceptual reach of Special Relativity today extends significantly further compared even to a few decades ago. As the book progresses, the qualitative and historical introduction turns into a textbook-style presentation with many detailed results derived in an explicit manner. The reader reaching the end of this text needs knowledge of classical mechanics, a good command of elementary algebra, basic knowledge of calculus, and introductory know-how of electromagnetism.
Modern Statistical Methods for Health Research (Emerging Topics in Statistics and Biostatistics)
by Yichuan Zhao Din Ding-Geng ChenThis book brings together the voices of leading experts in the frontiers of biostatistics, biomedicine, and the health sciences to discuss the statistical procedures, useful methods, and novel applications in biostatistics research. It also includes discussions of potential future directions of biomedicine and new statistical developments for health research, with the intent of stimulating research and fostering the interactions of scholars across health research related disciplines. Topics covered include: Health data analysis and applications to EHR data Clinical trials, FDR, and applications in health science Big network analytics and its applications in GWAS Survival analysis and functional data analysis Graphical modelling in genomic studies The book will be valuable to data scientists and statisticians who are working in biomedicine and health, other practitioners in the health sciences, and graduate students and researchers in biostatistics and health.
Modern Statistical Methods for Spatial and Multivariate Data (STEAM-H: Science, Technology, Engineering, Agriculture, Mathematics & Health)
by Norou DiawaraThis contributed volume features invited papers on current models and statistical methods for spatial and multivariate data. With a focus on recent advances in statistics, topics include spatio-temporal aspects, classification techniques, the multivariate outcomes with zero and doubly-inflated data, discrete choice modelling, copula distributions, and feasible algorithmic solutions. Special emphasis is placed on applications such as the use of spatial and spatio-temporal models for rainfall in South Carolina and the multivariate sparse areal mixed model for the Census dataset for the state of Iowa. Articles use simulated and aggregated data examples to show the flexibility and wide applications of proposed techniques.Carefully peer-reviewed and pedagogically presented for a broad readership, this volume is suitable for graduate and postdoctoral students interested in interdisciplinary research. Researchers in applied statistics and sciences will find this book an important resource on the latest developments in the field. In keeping with the STEAM-H series, the editors hope to inspire interdisciplinary understanding and collaboration.
Modern Statistical, Systems, and GPSS Simulation, Second Edition
by Zaven A. Karian Edward J. DudewiczModern Statistical, Systems, and GPSS Simulation, Second Edition introduces the theory and implementation of discrete-event simulation. This text:establishes a theoretical basis for simulation methodologyprovides details of an important simulation language (GPSS - General Purpose Simulation System)integrates these two elements in a systems simulation case studyValuable additions to the second edition include coverage of random number generators with astronomic period, new entropy-based tests of uniformity, gamma variate generation, results on the GLD, and variance reduction techniques.GPSS/PC is an interactive implementation of GPSS for the IBM-PC compatible family of microcomputers. The disk accompanying Modern Statistical, Systems, and GPSS Simulation contains the limited educational version of GPSS/PC with many illustrative examples discussed in the text.
Modern Statistics for the Social and Behavioral Sciences: A Practical Introduction (Second Edition)
by Rand Wilcox<p>Requiring no prior training, Modern Statistics for the Social and Behavioral Sciences provides a two-semester, graduate-level introduction to basic statistical techniques that takes into account recent advances and insights that are typically ignored in an introductory course. <p>Hundreds of journal articles make it clear that basic techniques, routinely taught and used, can perform poorly when dealing with skewed distributions, outliers, heteroscedasticity (unequal variances) and curvature. Methods for dealing with these concerns have been derived and can provide a deeper, more accurate and more nuanced understanding of data. A conceptual basis is provided for understanding when and why standard methods can have poor power and yield misleading measures of effect size. Modern techniques for dealing with known concerns are described and illustrated. <p>Features: <p> <li>Presents an in-depth description of both classic and modern methods <li>Explains and illustrates why recent advances can provide more power and a deeper understanding of data <li>Provides numerous illustrations using the software R <li>Includes an R package with over 1300 functions <li>Includes a solution manual giving detailed answers to all of the exercises</li> <p> <p>This second edition describes many recent advances relevant to basic techniques. For example, a vast array of new and improved methods is now available for dealing with regression, including substantially improved ANCOVA techniques. The coverage of multiple comparison procedures has been expanded and new ANOVA techniques are described.</p>
Modern Statistics with R: From Wrangling and Exploring Data to Inference and Predictive Modelling
by Måns ThulinThe past decades have transformed the world of statistical data analysis, with new methods, new types of data, and new computational tools. Modern Statistics with R introduces you to key parts of this modern statistical toolkit. It teaches you: Data wrangling – importing, formatting, reshaping, merging, and filtering data in R. Exploratory data analysis – using visualisations and multivariate techniques to explore datasets. Statistical inference – modern methods for testing hypotheses and computing confidence intervals. Predictive modelling – regression models and machine learning methods for prediction, classification, and forecasting. Simulation – using simulation techniques for sample size computations and evaluations of statistical methods. Ethics in statistics – ethical issues and good statistical practice. R programming – writing code that is fast, readable, and (hopefully!) free from bugs. No prior programming experience is necessary. Clear explanations and examples are provided to accommodate readers at all levels of familiarity with statistical principles and coding practices. A basic understanding of probability theory can enhance comprehension of certain concepts discussed within this book.In addition to plenty of examples, the book includes more than 200 exercises, with fully worked solutions available at: www.modernstatisticswithr.com.
Modern Statistics: A Computer-Based Approach with Python (Statistics for Industry, Technology, and Engineering)
by Shelemyahu Zacks Ron S. Kenett Peter GedeckThis innovative textbook presents material for a course on modern statistics that incorporates Python as a pedagogical and practical resource. Drawing on many years of teaching and conducting research in various applied and industrial settings, the authors have carefully tailored the text to provide an ideal balance of theory and practical applications. Numerous examples and case studies are incorporated throughout, and comprehensive Python applications are illustrated in detail. A custom Python package is available for download, allowing students to reproduce these examples and explore others.The first chapters of the text focus on analyzing variability, probability models, and distribution functions. Next, the authors introduce statistical inference and bootstrapping, and variability in several dimensions and regression models. The text then goes on to cover sampling for estimation of finite population quantities and time series analysis and prediction, concluding with two chapters on modern data analytic methods. Each chapter includes exercises, data sets, and applications to supplement learning.Modern Statistics: A Computer-Based Approach with Python is intended for a one- or two-semester advanced undergraduate or graduate course. Because of the foundational nature of the text, it can be combined with any program requiring data analysis in its curriculum, such as courses on data science, industrial statistics, physical and social sciences, and engineering. Researchers, practitioners, and data scientists will also find it to be a useful resource with the numerous applications and case studies that are included. A second, closely related textbook is titled Industrial Statistics: A Computer-Based Approach with Python. It covers topics such as statistical process control, including multivariate methods, the design of experiments, including computer experiments and reliability methods, including Bayesian reliability. These texts can be used independently or for consecutive courses.The mistat Python package can be accessed at https://gedeck.github.io/mistat-code-solutions/ModernStatistics/"In this book on Modern Statistics, the last two chapters on modern analytic methods contain what is very popular at the moment, especially in Machine Learning, such as classifiers, clustering methods and text analytics. But I also appreciate the previous chapters since I believe that people using machine learning methods should be aware that they rely heavily on statistical ones. I very much appreciate the many worked out cases, based on the longstanding experience of the authors. They are very useful to better understand, and then apply, the methods presented in the book. The use of Python corresponds to the best programming experience nowadays. For all these reasons, I think the book has also a brilliant and impactful future and I commend the authors for that."Professor Fabrizio RuggeriResearch Director at the National Research Council, ItalyPresident of the International Society for Business and Industrial Statistics (ISBIS)Editor-in-Chief of Applied Stochastic Models in Business and Industry (ASMBI)
Modern Stochastics and Applications
by Yuliya Mishura Nikolaos Limnios Volodymyr Korolyuk Lyudmyla Sakhno Georgiy ShevchenkoThis volume presents an extensive overview of all major modern trends in applications of probability and stochastic analysis. It will be a great source of inspiration for designing new algorithms, modeling procedures and experiments. Accessible to researchers, practitioners, as well as graduate and postgraduate students, this volume presents a variety of new tools, ideas and methodologies in the fields of optimization, physics, finance, probability, hydrodynamics, reliability, decision making, mathematical finance, mathematical physics and economics. Contributions to this Work include those of selected speakers from the international conference entitled "Modern Stochastics: Theory and Applications III," held on September 10 -14, 2012 at Taras Shevchenko National University of Kyiv, Ukraine. The conference covered the following areas of research in probability theory and its applications: stochastic analysis, stochastic processes and fields, random matrices, optimization methods in probability, stochastic models of evolution systems, financial mathematics, risk processes and actuarial mathematics and information security.
Modern Survey Analysis: Using Python for Deeper Insights
by Walter R. PaczkowskiThis book develops survey data analysis tools in Python, to create and analyze cross-tab tables and data visuals, weight data, perform hypothesis tests, and handle special survey questions such as Check-all-that-Apply. In addition, the basics of Bayesian data analysis and its Python implementation are presented. Since surveys are widely used as the primary method to collect data, and ultimately information, on attitudes, interests, and opinions of customers and constituents, these tools are vital for private or public sector policy decisions.As a compact volume, this book uses case studies to illustrate methods of analysis essential for those who work with survey data in either sector. It focuses on two overarching objectives:Demonstrate how to extract actionable, insightful, and useful information from survey data; andIntroduce Python and Pandas for analyzing survey data.
Modern Survey Sampling
by Arijit ChaudhuriStarting from the preliminaries and ending with live examples, Modern Survey Sampling details what a sample can communicate about an unknowable aggregate in a real situation. The author lucidly develops and presents numerous approaches. He details recent developments and explores fresh and unseen problems, hitting upon possible solutions. The text covers current research output in a student-friendly manner with attractive illustrations. It introduces sampling and discusses how to select a sample for which a selection-probability is specified to prescribe its performance characteristics. The author then explains how to examine samples with varying probabilities to derive profits. He then examines how to use partial segments to make reasonable guesses about a sample's behavior and assess the elements of discrepancies. Including case studies, exercises, and solutions, the book highlights special survey techniques needed to capture trustworthy data and put it to intelligent use. It then discusses the model-assisted approach and network sampling, before moving on to speculating about random processes. The author draws on his extensive teaching experience to create a textbook that gives your students a thorough grounding in the technologies of survey sampling and modeling and also provides you with the tools to teach them.
Modern Survival Analysis in Clinical Research: Cox Regressions Versus Accelerated Failure Time Models
by Aeilko H. Zwinderman Ton J. CleophasAn important novel menu for Survival Analysis entitled Accelerated Failure Time (AFT) models has been published by IBM (international Businesss Machines) in its SPSS statistical software update of 2023. Unlike the traditional Cox regressions that work with hazards, which are the ratio of deaths and non-deaths in a sample, it works with risk of death, which is the proportion of deaths in the same sample. The latter approach may provide better sensitivity of testing, but has been seldom applied, because with computers risks are tricky and hazards because they are odds are fine. This was underscored in 1997 by Keiding and colleague statisticians from Copenhagen University who showed better-sensitive goodness of fit and null-hypothesis tests with AFT than with Cox survival tests.So far, a controlled study of a representative sample of clinical Kaplan Meier assessments, where the sensitivity of Cox regression is systematically tested against that of AFT modeling, has not been accomplished. This edition is the first textbook and tutorial of AFT modeling both for medical and healthcare students and for professionals. Each chapter can be studied as a standalone, and, using, real as well as hypothesized data, it tests the performance of the novel methodology against traditional Cox regressions. Step by step analyses of over 20 data files stored at Supplementary Files at Springer Interlink are included for self-assessment. We should add that the authors are well qualified in their field. Professor Zwinderman is past-president of the International Society of Biostatistics (2012-2015) and Professor Cleophas is past-president of the American College of Angiology (2000-2002). From their expertise they should be able to make adequate selections of modern data analysis methods for the benefit of physicians, students, and investigators. The authors have been working and publishing together for 25 years and their research can be characterized as a continued effort to demonstrate that clinical data analysis is not mathematics but rather a discipline at the interface of biology and mathematics.
Modern Thermodynamics and Statistical Mechanics: A Comprehensive Foundation (Undergraduate Lecture Notes in Physics)
by Ravinder R. PuriThis undergraduate-level textbook offers a unique and in-depth approach to the study of thermodynamics and statistical mechanics. It covers the fundamentals of thermodynamics using both traditional and postulatory approaches, including origin of the concept of thermodynamic entropy, Euler’s equation, Gibbs-Duhem relations, stability of equilibrium, and the concept of thermodynamic potentials, and that of independent thermodynamic observables. The book then delves into the microscopic foundation of thermodynamics, starting with the kinetic theory and highlighting its historical development. Boltzmann's concept of entropy is explored, along with its applications in deriving Planck’s, Bose’s, Bose-Einstein, and Fermi-Dirac distribution functions. The formal structure of classical and quantum statistical mechanics is built based on the concept of statistical entropy and the maximum entropy principle and used to investigate in detail the thermodynamic properties of ideal classical and quantum systems. The book also covers phase transitions, simple theory of critical phenomena, and the theory of interacting van der Waals gases. Throughout the text, the book provides historical context, enriching the reader's understanding. This textbook is a valuable resource for undergraduate physics students, offering comprehensive coverage, including overlooked topics, and a historical perspective on thermodynamics and statistical mechanics.
Modern Time Series Forecasting with Python: Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas
by Manu Joseph Jeffrey TackesLearn traditional and cutting-edge machine learning (ML) and deep learning techniques and best practices for time series forecasting, including global forecasting models, conformal prediction, and transformer architecturesKey FeaturesApply ML and global models to improve forecasting accuracy through practical examplesEnhance your time series toolkit by using deep learning models, including RNNs, transformers, and N-BEATSLearn probabilistic forecasting with conformal prediction, Monte Carlo dropout, and quantile regressionsPurchase of the print or Kindle book includes a free eBook in PDF formatBook DescriptionPredicting the future, whether it's market trends, energy demand, or website traffic, has never been more crucial. This practical, hands-on guide empowers you to build and deploy powerful time series forecasting models. Whether you’re working with traditional statistical methods or cutting-edge deep learning architectures, this book provides structured learning and best practices for both. Starting with the basics, this data science book introduces fundamental time series concepts, such as ARIMA and exponential smoothing, before gradually progressing to advanced topics, such as machine learning for time series, deep neural networks, and transformers. As part of your fundamentals training, you’ll learn preprocessing, feature engineering, and model evaluation. As you progress, you’ll also explore global forecasting models, ensemble methods, and probabilistic forecasting techniques. This new edition goes deeper into transformer architectures and probabilistic forecasting, including new content on the latest time series models, conformal prediction, and hierarchical forecasting. Whether you seek advanced deep learning insights or specialized architecture implementations, this edition provides practical strategies and new content to elevate your forecasting skills.What you will learnBuild machine learning models for regression-based time series forecastingApply powerful feature engineering techniques to enhance prediction accuracyTackle common challenges like non-stationarity and seasonalityCombine multiple forecasts using ensembling and stacking for superior resultsExplore cutting-edge advancements in probabilistic forecasting and handle intermittent or sparse time seriesEvaluate and validate your forecasts using best practices and statistical metricsWho this book is forThis book is ideal for data scientists, financial analysts, quantitative analysts, machine learning engineers, and researchers who need to model time-dependent data across industries, such as finance, energy, meteorology, risk analysis, and retail. Whether you are a professional looking to apply cutting-edge models to real-world problems or a student aiming to build a strong foundation in time series analysis and forecasting, this book will provide the tools and techniques you need. Familiarity with Python and basic machine learning concepts is recommended.
Modern Trends in Controlled Stochastic Processes: Theory and Applications, V.III (Emergence, Complexity and Computation #41)
by Yi Zhang Alexey PiunovskiyThis book presents state-of-the-art solution methods and applications of stochastic optimal control. It is a collection of extended papers discussed at the traditional Liverpool workshop on controlled stochastic processes with participants from both the east and the west. New problems are formulated, and progresses of ongoing research are reported. Topics covered in this book include theoretical results and numerical methods for Markov and semi-Markov decision processes, optimal stopping of Markov processes, stochastic games, problems with partial information, optimal filtering, robust control, Q-learning, and self-organizing algorithms. Real-life case studies and applications, e.g., queueing systems, forest management, control of water resources, marketing science, and healthcare, are presented. Scientific researchers and postgraduate students interested in stochastic optimal control,- as well as practitioners will find this book appealing and a valuable reference.
Modern Trends in Fuzzy Graph Theory
by Madhumangal Pal Sovan Samanta Ganesh GhoraiThis book provides an extensive set of tools for applying fuzzy mathematics and graph theory to real-life problems. Balancing the basics and latest developments in fuzzy graph theory, this book starts with existing fundamental theories such as connectivity, isomorphism, products of fuzzy graphs, and different types of paths and arcs in fuzzy graphs to focus on advanced concepts such as planarity in fuzzy graphs, fuzzy competition graphs, fuzzy threshold graphs, fuzzy tolerance graphs, fuzzy trees, coloring in fuzzy graphs, bipolar fuzzy graphs, intuitionistic fuzzy graphs, m-polar fuzzy graphs, applications of fuzzy graphs, and more. Each chapter includes a number of key representative applications of the discussed concept. An authoritative, self-contained, and inspiring read on the theory and modern applications of fuzzy graphs, this book is of value to advanced undergraduate and graduate students of mathematics, engineering, and computer science, as well as researchers interested in new developments in fuzzy logic and applied mathematics.
Modern and Interdisciplinary Problems in Network Science: A Translational Research Perspective
by Zengqiang Chen, Matthias Dehmer, Frank Emmert-Streib and Yongtang ShiModern and Interdisciplinary Problems in Network Science: A Translational Research Perspective covers a broad range of concepts and methods, with a strong emphasis on interdisciplinarity. The topics range from analyzing mathematical properties of network-based methods to applying them to application areas. By covering this broad range of topics, the book aims to fill a gap in the contemporary literature in disciplines such as physics, applied mathematics and information sciences.
Modern's ABC of Mathematics class 10 - Meghalaya Board
by J. P. Mohindru Bharat MohindruTextbook for Mathematics specially created for Meghalaya Board of School Education for the students of class 10.
Moderne Datenanalyse mit R: Daten einlesen, aufbereiten, visualisieren, modellieren und kommunizieren (FOM-Edition)
by Sebastian SauerDie Kaufempfehlung, die Ihnen ein Webstore ausspricht, die Einschätzung, welcher Kunde kreditwürdig ist, oder die Analyse der Werttreiber von Immobilien – alle diese Beispiele aus dem heutigen Leben sind Ergebnis moderner Verfahren der Datenanalyse. Dieses Buch führt in solche statistische Verfahren anhand der Programmiersprache R ein. Ziel ist es, Leser mit der Art und Weise vertraut zu machen, wie führende Organisationen und Praktiker angewandte Statistik heute einsetzen. Weil sich mit der Digitalisierung auch die statistischen Verfahren verändert haben, vermittelt der Autor neben klassischen Analysemethoden wie Regression auch moderne Methoden wie Textmining und Random-Forest-Modelle. Dabei sind die Inhalte des Buchs durchgehend so aufbereitet, dass sie auch für Leser ohne umfangreiche mathematische Vorkenntnisse verständlich sind. Anhand von Fallbeispielen und Übungen werden die Leser durch alle Phasen der Datenanalyse geführt: Sie lernen, wie Daten eingelesen, aufbereitet, visualisiert, modelliert und kommuniziert werden können. Dabei wird vor allem die Aufbereitung, Umformung und Prüfung der Daten ausführlicher als in anderen Publikationen behandelt, da dieser Teil in der Praxis oft einen wesentlichen Teil des Aufwands ausmacht. Aber auch die Visualisierung bekommt viel Raum, denn gute Diagramme ermöglichen Einblicke, die Zahlen und Worte verbergen.Mit seinem praxisorientierten Ansatz will das Buch dazu befähigen,alle grundlegenden Schritte eines Datenanalyseprojekts durchzuführen,Daten kompetent in R zu bearbeiten,simulationsbasierte Inferenzstatistik anzuwenden und kritisch zu hinterfragen,klassische und moderne Vorhersagemethoden anzuwenden undbetriebswirtschaftliche Fragestellungen mittels datengetriebener Vorhersagemodelle zu beantworten.Sowohl Anwender ohne statistisches Grundlagenwissen als auch Nutzer mit Vorerfahrung lesen dieses Buch mit Gewinn. In verständlicher Sprache und anhand von anschaulichen Beispielen zeigt der Autor, wie moderne Datenanalyse heute funktioniert.
Moderne Finanzmathematik – Theorie und praktische Anwendung Band 2: Erweiterungen Des Black-scholes-modells, Zins, Kreditrisiko Und Statistik (Studienbücher Wirtschaftsmathematik Ser.)
by Ralf Korn Sascha DesmettreDas vorliegende Buch und der zugehörige erste Band über Optionsbewertung und Portfolio-Optimierung geben eine gründliche Einführung in die Methoden und Prinzipien der modernen Finanzmathematik. Dieser zweite Band behandelt insbesondere Zinsmodellierung, Verallgemeinerungen des Black-Scholes-Modells zur realistischeren Modellierung von Aktienpreisen sowie Parameterschätzung und -kalibrierung. Um das Lesen und Verstehen aller Kapitel zu vereinfachen, werden jeweils einführende Abschnitte mit Motivation und Überblick voran gestellt, in denen der im Kapitel folgende Stoff ökonomisch motiviert, seine Entstehungs- und Entwicklungsgeschichte beschrieben oder auch Aspekte der Praxis gegeben werden. Technisch anspruchsvolle theoretische Konzepte werden wieder in Exkursen dort präsentiert, wo sie zum ersten Mal benötigt werden. Das Werk richtet sich an Studierende der Mathematik und der Finanzwirtschaft sowie an Praktiker in Banken und Versicherungen.
Moderne Verfahren der Kryptographie: Von RSA zu Zero-Knowledge und darüber hinaus
by Jörg Schwenk Albrecht Beutelspacher Klaus-Dieter WolfenstetterDie Entwicklung und Analyse von Protokollen wird ein immer wichtigerer Zweig der modernen Kryptologie. Große Berühmtheit erlangt haben die so genannten "Zero-Knowledge-Protokolle", mit denen es gelingt, einen anderen von der Existenz eines Geheimnisses zu überzeugen, ohne ihm das Geringste zu verraten.
Moderne mathematische Methoden der Physik
by Karl-Heinz Goldhorn Hans-Peter Heinz Margarita KrausDer Vorzug des Buchs liegt in der strengen Konzentration auf das Wesentliche. Dabei deckt der Stoff ein breites Spektrum mathematischer Konzepte und Methoden ab und ist so angeordnet, dass er den Bedürfnissen der Studierenden folgt. Neben mathematischen Beweisen, die Studierende mit mathematischer Denkweise konfrontieren, bietet das Lehrbuch Aufgaben, von denen ein Großteil dem Einüben von Rechentechniken dient. Theoretische Aufgaben helfen, Begriffe zu klären und logisches Argumentieren zu üben. Das Glossar enthält alle Definitionen und Sätze.
Modernity and the Unmaking of Men (New Anthropologies of Europe: Perspectives and Provocations #1)
by Violeta SchubertResponding to the renewed emphasis on the significance of village studies, this book focuses on aging bachelorhood as a site of intolerable angst when faced with rural depopulation and social precarity. Based on ongoing ethnographic fieldwork in contemporary Macedonian society, the book explores the intersections between modernity, kinship and gender. It argues that as a critical consequence of demographic rupture, changing values and societal shifts, aging bachelorhood illuminates and challenges conceptualizations of performativity and social presence.