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  • An essential resource for constructing and analyzing advanced actuarial models Loss Models: Further Topics presents extended coverage of modeling through the use of tools related to risk theory, loss distributions, and survival models. The book uses these methods to construct and evaluate actuarial models in the fields of insurance and business. Providing an advanced study of actuarial methods, the book features extended discussions of risk modeling and risk measures, including Tail-Value-at-Risk. Loss Models: Further Topics contains additional material to accompany the Fourth Edition of Loss Models: From Data to Decisions, such as: Extreme value distributions Coxian and related distributions Mixed Erlang distributions Computational and analytical methods for aggregate claim models Counting processes Compound distributions with time-dependent claim amounts Copula models Continuous time ruin models Interpolation and smoothing The book is an essential reference for practicing actuaries and actuarial researchers who want to go beyond the material required for actuarial qualification. Loss Models: Further Topics is also an excellent resource for graduate students in the actuarial field.
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  • Business intelligence is a broad category of applications and technologies for gathering, providing access to, and analyzing data for the purpose of helping enterprise users make better business decisions. The term implies having a comprehensive knowledge of all factors that affect a business, such as customers, competitors, business partners, economic environment, and internal operations, therefore enabling optimal decisions to be made. Business Intelligence provides readers with an introduction and practical guide to the mathematical models and analysis methodologies vital to business intelligence. This book: Combines detailed coverage with a practical guide to the mathematical models and analysis methodologies of business intelligence. Covers all the hot topics such as data warehousing, data mining and its applications, machine learning, classification, supply optimization models, decision support systems, and analytical methods for performance evaluation. Is made accessible to readers through the careful definition and introduction of each concept, followed by the extensive use of examples and numerous real-life case studies. Explains how to utilise mathematical models and analysis models to make effective and good quality business decisions. This book is aimed at postgraduate students following data analysis and data mining courses. Researchers looking for a systematic and broad coverage of topics in operations research and mathematical models for decision-making will find this an invaluable guide.
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  • Technical Report from the year 2012 in the subject Computer Science - Applied, grade: none, Royal Holloway, University of London, course: M.Sc. Information Security, language: English, abstract: As technology evolves, the threat landscape evolves. The threat landscape has changed frommere script kiddies for fun to organised cyber crime to steal data for identity theft andmonetary gains.It is very important to protect the business data from all kind of data breach attacks in orderto maintain the brand reputation and customer faith. New laws and governance policiesmake it mandatory for organisations to protect customer data. This project talks about thedifferent effect of data breaches and preventive measures. This project targets mostly on the'confidentiality' trait of the information security's CIA triad.
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  • Convert the promise of big data into real world results There is so much buzz around big data. We all need to know what it is and how it works – that much is obvious. But is a basic understanding of the theory enough to hold your own in strategy meetings? Probably. But what will set you apart from the rest is actually knowing how to USE big data to get solid, real-world business results – and putting that in place to improve performance. Big Data will give you a clear understanding, blueprint, and step-by-step approach to building your own big data strategy. This is a well-needed practical introduction to actually putting the topic into practice. Illustrated with numerous real-world examples from a cross section of companies and organisations, Big Data will take you through the five steps of the SMART model: Start with Strategy, Measure Metrics and Data, Apply Analytics, Report Results, Transform. Discusses how companies need to clearly define what it is they need to know Outlines how companies can collect relevant data and measure the metrics that will help them answer their most important business questions Addresses how the results of big data analytics can be visualised and communicated to ensure key decisions-makers understand them Includes many high-profile case studies from the authors work with some of the worlds best known brands
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  • Master's Thesis from the year 2001 in the subject Medicine - Neoplasms, Oncology, , language: English, abstract: Weight loss is a common feature in patients with chronic obstructive pulmonary disease (COPD). The clinical importance of weight loss; particularely loss of fat-free mass (FFM) has been demonstrated in its adverse effects on physical performance and quality of life. Moreover; weight loss and a low body weight are unfavorable prognostic factors in survival, independent of lung function.Mechanisms of malnutrition in those patients are not fully understood. Several factors have been implicated. Increased resting energy expenditure (REE) contributes the main hypothesis for weight loss in COPD patients. However, not all patients with COPD who lose weight are hypermetabolic.Recent data have shown that a systemic inflammatory response is present in patients with COPD. A clear evidence for a relationship between weight loss and plasma tumour necrosis factor-alpha (TNF-*) has been shown in COPD patients. TNF-* produces a cachexia-like syndrome in animal models and has been implicated as a mediator of cachexia in several clinical conditions including cancer, chronic heart failure, cystic fibrosis and anorexia nervosa.Nutritional assessment for COPD patients is essential; to identify those individuals who will benifit from nutritional support therapies and to determine baseline values to measure the effectiveness of nutritional intervention. It includes several methods, n...
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  • Leverage big data to add value to your business Social media analytics, web-tracking, and other technologies help companies acquire and handle massive amounts of data to better understand their customers, products, competition, and markets. Armed with the insights from big data, companies can improve customer experience and products, add value, and increase return on investment. The tricky part for busy IT professionals and executives is how to get this done, and thats where this practical book comes in. Big Data: Understanding How Data Powers Big Business is a complete how-to guide to leveraging big data to drive business value. Full of practical techniques, real-world examples, and hands-on exercises, this book explores the technologies involved, as well as how to find areas of the organization that can take full advantage of big data. Shows how to decompose current business strategies in order to link big data initiatives to the organization’s value creation processes Explores different value creation processes and models Explains issues surrounding operationalizing big data, including organizational structures, education challenges, and new big data-related roles Provides methodology worksheets and exercises so readers can apply techniques Includes real-world examples from a variety of organizations leveraging big data Big Data: Understanding How Data Powers Big Business is written by one of Big Datas preeminent experts, William Schmarzo. Dont miss his invaluable insights and advice.
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  • Provides an introduction of the data industry to the field of economics This book bridges the gap between economics and data science to help data scientists understand the economics of big data, and enable economists to analyze the data industry. It begins by explaining data resources and introduces the data asset. This book defines a data industry chain, enumerates data enterprises’ business models versus operating models, and proposes a mode of industrial development for the data industry. The author describes five types of enterprise agglomerations, and multiple industrial cluster effects. A discussion on the establishment and development of data industry related laws and regulations is provided. In addition, this book discusses several scenarios on how to convert data driving forces into productivity that can then serve society. This book is designed to serve as a reference and training guide for ata scientists, data-oriented managers and executives, entrepreneurs, scholars, and government employees. Defines and develops the concept of a “Data Industry,” and explains the economics of data to data scientists and statisticians Includes numerous case studies and examples from a variety of industries and disciplines Serves as a useful guide for practitioners and entrepreneurs in the business of data technology The Data Industry: The Business and Economics of Information and Big Data is a resource for practitioners in the data science industry, government, and students in economics, business, and statistics. CHUNLEI TANG, Ph.D., is a research fellow at Harvard University. She is the co-founder of Fudan’s Institute for Data Industry and proposed the concept of the “data industry”. She received a Ph.D. in Computer and Software Theory in 2012 and a Master of Software Engineering in 2006 from Fudan University, Shanghai, China.
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  • The decision to implement environmental protection options is a political one. These, and other political and social decisions affect the balance of the ecosystem and how the point of equilibrium desired is to be reached. This book develops a stochastic, temporal model of how political processes influence and are influenced by ecosystem processes and looks at how to find the most politically feasible plan for managing an at-risk ecosystem. Finding such a plan is accomplished by first fitting a mechanistic political and ecological model to a data set composed of observations on both political actions that impact an ecosystem and variables that describe the ecosystem. The parameters of this fitted model are perturbed just enough to cause human behaviour to change so that desired ecosystem states occur. This perturbed model gives the ecosystem management plan needed to reach desired ecosystem states. To construct such a set of interacting models, topics from political science, ecology, probability, and statistics are developed and explored. Key features: Explores politically feasible ways to manage at-risk ecosystems. Gives agent-based models of how social groups affect ecosystems through time. Demonstrates how to fit models of population dynamics to mixtures of wildlife data. Presents statistical methods for fitting models of group behaviour to political action data. Supported by an accompanying website featuring datasets and JAVA code. This book will be useful to managers and analysts working in organizations charged with finding practical ways to sustain biodiversity or the physical environment. Furthermore this book also provides a political roadmap to help lawmakers and administrators improve institutional environmental management decision making.
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  • Data Science gets thrown around in the press like its magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. Its a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions. But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the «data scientist,» to extract this gold from your data? Nope. Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how thats done within the familiar environment of a spreadsheet. Why a spreadsheet? Its comfortable! You get to look at the data every step of the way, building confidence as you learn the tricks of the trade. Plus, spreadsheets are a vendor-neutral place to learn data science without the hype. But dont let the Excel sheets fool you. This is a book for those serious about learning the analytic techniques, the math and the magic, behind big data. Each chapter will cover a different technique in a spreadsheet so you can follow along: Mathematical optimization, including non-linear programming and genetic algorithms Clustering via k-means, spherical k-means, and graph modularity Data mining in graphs, such as outlier detection Supervised AI through logistic regression, ensemble models, and bag-of-words models Forecasting, seasonal adjustments, and prediction intervals through monte carlo simulation Moving from spreadsheets into the R programming language You get your hands dirty as you work alongside John through each technique. But never fear, the topics are readily applicable and the author laces humor throughout. Youll even learn what a dead squirrel has to do with optimization modeling, which you no doubt are dying to know.
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  • A comprehensive compilation of new developments in data linkage methodology The increasing availability of large administrative databases has led to a dramatic rise in the use of data linkage, yet the standard texts on linkage are still those which describe the seminal work from the 1950-60s, with some updates. Linkage and analysis of data across sources remains problematic due to lack of discriminatory and accurate identifiers, missing data and regulatory issues. Recent developments in data linkage methodology have concentrated on bias and analysis of linked data, novel approaches to organising relationships between databases and privacy-preserving linkage. Methodological Developments in Data Linkage brings together a collection of contributions from members of the international data linkage community, covering cutting edge methodology in this field. It presents opportunities and challenges provided by linkage of large and often complex datasets, including analysis problems, legal and security aspects, models for data access and the development of novel research areas. New methods for handling uncertainty in analysis of linked data, solutions for anonymised linkage and alternative models for data collection are also discussed. Key Features: Presents cutting edge methods for a topic of increasing importance to a wide range of research areas, with applications to data linkage systems internationally Covers the essential issues associated with data linkage today Includes examples based on real data linkage systems, highlighting the opportunities, successes and challenges that the increasing availability of linkage data provides Novel approach incorporates technical aspects of both linkage, management and analysis of linked data This book will be of core interest to academics, government employees, data holders, data managers, analysts and statisticians who use administrative data. It will also appeal to researchers in a variety of areas, including epidemiology, biostatistics, social statistics, informatics, policy and public health.
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  • Master's Thesis from the year 2014 in the subject Environmental Sciences, grade: Excellent, , course: Urban Environment and Climate Change Management, language: English, abstract: Urban transport is one of the necessities that cities require to perform their day-to-day activities. At the same time, it is also the major treat to global climate change. Transport sector has emitted more than 7200 billion tons of greenhouse gases to the atmosphere. This study therefore intends to assess the greenhouse gas emission reduction measures in urban transport of Mekelle city. It employs a descriptive research with qualitative and quantitative research approaches and survey research strategy. Data for the study were collected from primary and secondary data sources. Questionnaire, interview, and observation were used to gather primary data from owners of private automobiles and transport office, whereas, archives were used to gather secondary data. These data were analyzed through qualitative (narrative analysis) and quantitative (descriptive and statistical analysis) methods. Tables, charts, graphs and plates are used to present the data. This study finds that Atoz and Yaris car models; and Hyundai and Daihatsu motors emit lesser amount of carbon dioxide, while Land cruiser and WllB car models; and Mercedes Benz and Suzuki motors emit more. The average emission level of private automobiles in Mekelle city is found to be 209.93grams of CO2/km with an average fuel efficiency of 12.115k...
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  • Features a practical approach to the analysis of biomedical data via mathematical methods and provides a MATLAB® toolbox for the collection, visualization, and evaluation of experimental and real-life data Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB® presents a practical approach to the task that biological scientists face when analyzing data. The primary focus is on the application of mathematical models and scientific computing methods to provide insight into the behavior of biological systems. The author draws upon his experience in academia, industry, and government–sponsored research as well as his expertise in MATLAB to produce a suite of computer programs with applications in epidemiology, machine learning, and biostatistics. These models are derived from real–world data and concerns. Among the topics included are the spread of infectious disease (HIV/AIDS) through a population, statistical pattern recognition methods to determine the presence of disease in a diagnostic sample, and the fundamentals of hypothesis testing. In addition, the author uses his professional experiences to present unique case studies whose analyses provide detailed insights into biological systems and the problems inherent in their examination. The book contains a well-developed and tested set of MATLAB functions that act as a general toolbox for practitioners of quantitative biology and biostatistics. This combination of MATLAB functions and practical tips amplifies the book’s technical merit and value to industry professionals. Through numerous examples and sample code blocks, the book provides readers with illustrations of MATLAB programming. Moreover, the associated toolbox permits readers to engage in the process of data analysis without needing to delve deeply into the mathematical theory. This gives an accessible view of the material for readers with varied backgrounds. As a result, the book provides a streamlined framework for the development of mathematical models, algorithms, and the corresponding computer code. In addition, the book features: Real–world computational procedures that can be readily applied to similar problems without the need for keen mathematical acumen Clear delineation of topics to accelerate access to data analysis Access to a book companion website containing the MATLAB toolbox created for this book, as well as a Solutions Manual with solutions to selected exercises Applied Mathematics for the Analysis of Biomedical Data: Models, Methods, and MATLAB® is an excellent textbook for students in mathematics, biostatistics, the life and social sciences, and quantitative, computational, and mathematical biology. This book is also an ideal reference for industrial scientists, biostatisticians, product development scientists, and practitioners who use mathematical models of biological systems in biomedical research, medical device development, and pharmaceutical submissions.
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  • Inhaltsangabe:Abstract: We discuss the main approaches to quantify the risk of losses arising from a defaulting counterparty to a financial transaction that have been developed over the last 25 years. Every existing method faces major problems in assessing the numerous and partly non-observable factors influencing credit risk. One shortcoming common to all methods is the classical normal assumption for interest rate changes and asset returns. Therefore we suggest the introduction of stable Paretian models to yield more realistic credit spreads. Inhaltsverzeichnis:Table of Contents: 1.Introduction 2.Basic Properties of Credit Risk Models 2.1Financial Position 2.2Default Probability 2.3The Price Of Credit Risk 3.Structural Models 3.1Structural Models With Constant Interest Rates 3.2Structural Models With Stochastic Interest Rates 4.Reduced Form Models 4.1Terminology of Reduced Form Models 4.1.1Credit Risk and Credit Events 4.1.2Rating Categories and Transition Matrices 4.2Reduced Form Modesl With Default Rates 4.3Reduced Form Models With Rating Transitions 4.3.1Modelling Rating Histories With Markov Chains 4.3.2The Introduction of Pseudo-Probabilities 4.3.3Parameter Estimation 5.Models With Implied Credit Spread 6.Hybrid Models 6.1Rating Transitions 6.2Forward Prices 6.3The Distribution of Values 6.3.1Distributions in Credit Risk and Market Risk Measurement 6.4Expected Loss 6.5Unexpected Loss 6.6Example 7.Rating Categories 7.1Alternative Credit Analysis And Rating Methodology 7...
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  • In this book, integrated inventory models involving defective items under various settings has been discussed. The optimal strategy for supply chain models have been examined from two aspects, viz. individual management and joint management system. Some theoretical results are derived to identify optimal decisions. The models are illustrated through numerical examples and sensitivity analysis has been carried out to gain managerial insights.
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  • An accessible introduction to the essential quantitative methods for making valuable business decisions Quantitative methods-research techniques used to analyze quantitative data-enable professionals to organize and understand numbers and, in turn, to make good decisions. Quantitative Methods: An Introduction for Business Management presents the application of quantitative mathematical modeling to decision making in a business management context and emphasizes not only the role of data in drawing conclusions, but also the pitfalls of undiscerning reliance of software packages that implement standard statistical procedures. With hands-on applications and explanations that are accessible to readers at various levels, the book successfully outlines the necessary tools to make smart and successful business decisions. Progressing from beginner to more advanced material at an easy-to-follow pace, the author utilizes motivating examples throughout to aid readers interested in decision making and also provides critical remarks, intuitive traps, and counterexamples when appropriate. The book begins with a discussion of motivations and foundations related to the topic, with introductory presentations of concepts from calculus to linear algebra. Next, the core ideas of quantitative methods are presented in chapters that explore introductory topics in probability, descriptive and inferential statistics, linear regression, and a discussion of time series that includes both classical topics and more challenging models. The author also discusses linear programming models and decision making under risk as well as less standard topics in the field such as game theory and Bayesian statistics. Finally, the book concludes with a focus on selected tools from multivariate statistics, including advanced regression models and data reduction methods such as principal component analysis, factor analysis, and cluster analysis. The book promotes the importance of an analytical approach, particularly when dealing with a complex system where multiple individuals are involved and have conflicting incentives. A related website features Microsoft Excel® workbooks and MATLAB® scripts to illustrate concepts as well as additional exercises with solutions. Quantitative Methods is an excellent book for courses on the topic at the graduate level. The book also serves as an authoritative reference and self-study guide for financial and business professionals, as well as readers looking to reinforce their analytical skills.
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