115,78 € *

ggf. zzgl. Versand

ggf. zzgl. Versand

Recurrent event data arise in diverse fields such as medicine, public health, insurance, social science, economics, manufacturing and reliability. The purpose of this book is to present models and statistical methods for the analysis of recurrent event data. No single comprehensive treatment of these areas currently exists. The authors provide broad but detailed coverage of the major approaches to analysis, while also emphasizing the modeling assumptions that they are based on. Thus, they consider important models such as Poisson and renewal processes, with extensions to incorporate covariates or random effects.More general intensity-based models are also considered, as well as simpler models that focus on rate or mean functions. Parametric, nonparametric and semiparametric methodologies areall covered, with clear descriptions of procedures for estimation, testing and model checking. Important practical topics such as variations in observation schemes or selection of individuals for study, the planning of randomized experiments, events of several types, and the prediction of future events are considered.Methods of modeling and analysis are illustrated through many examples taken from health research and industry. The objectives and interpretations of different analyses are discussed in detail, and issues of robustness are addressed. Statistical analysis of the examples is carried out with S-PLUS software and code is given for some examples.This book is directed at graduate students, researchers, and applied statisticians working in industry, government or academia. Some familiarity with survival analysis is beneficial since survival software is used to carry out many of the analyses considered. This book can be used as a textbook for a graduate course on the analysis of recurrent events or as a reference for a more general course on event history analysis. Problems are given at the end of chapters to reinforce the material presented and to provide additional background or extensions to certain topics.

Anbieter: buecher

Stand: 08.08.2020 Zum Angebot

Stand: 08.08.2020 Zum Angebot

48,77 € *

ggf. zzgl. Versand

ggf. zzgl. Versand

Both in insurance and in finance applications, questions involving extremal events (such as large insurance claims, large fluctuations, in financial data, stock-market shocks, risk management, ...) play an increasingly important role. This much awaited book presents a comprehensive development of extreme value methodology for random walk models, time series, certain types of continuous-time stochastic processes and compound Poisson processes, all models which standardly occur in applications in insurance mathematics and mathematical finance. Both probabilistic and statistical methods are discussed in detail, with such topics as ruin theory for large claim models, fluctuation theory of sums and extremes of iid sequences, extremes in time series models, point process methods, statistical estimation of tail probabilities. Besides summarising and bringing together known results, the book also features topics that appear for the first time in textbook form, including the theory of subexponential distributions and the spectral theory of heavy-tailed time series. A typical chapter will introduce the new methodology in a rather intuitive (tough always mathematically correct) way, stressing the understanding of new techniques rather than following the usual "theorem-proof" format. Many examples, mainly from applications in insurance and finance, help to convey the usefulness of the new material. A final chapter on more extensive applications and/or related fields broadens the scope further. The book can serve either as a text for a graduate course on stochastics, insurance or mathematical finance, or as a basic reference source. Its reference quality is enhanced by a very extensive bibliography, annotated by various comments sections making the book broadly and easily accessible. "A reader's first impression on leafing through this book is of the large number of graphs and diagrams, used to illustrate shapes of distributions...and to show real data examples in various ways. A closer reading reveals a nice mix of theory and applications, with the copious graphical illustrations alluded to. Such a mixture is of course dear to the heart of the applied probabilist/statistician, and should impress even the most ardent theorists." --MATHEMATICAL REVIEWS

Anbieter: buecher

Stand: 08.08.2020 Zum Angebot

Stand: 08.08.2020 Zum Angebot

48,77 € *

ggf. zzgl. Versand

ggf. zzgl. Versand

Anbieter: buecher

Stand: 08.08.2020 Zum Angebot

Stand: 08.08.2020 Zum Angebot

46,96 € *

ggf. zzgl. Versand

ggf. zzgl. Versand

Were you looking for the book with access to MyStatLab? This product is the book alone, and does NOT come with access to MyStatLab. Buy the book and access card package to save money on this resource. In Statistics for Business: Decision Making and Analysis, authors Robert Stine and Dean Foster of the University of Pennsylvania’s Wharton School, take a sophisticated approach to teaching statistics in the context of making good business decisions. The authors show students how to recognize and understand each business question, use statistical tools to do the analysis, and how to communicate their results clearly and concisely. In addition to providing cases and real data to demonstrate real business situations, this text provides resources to support understanding and engagement. A successful problem-solving framework in the 4-M Examples (Motivation, Method, Mechanics, Message) model a clear outline for solving problems, new What Do You Think questions give students an opportunity to stop and check their understanding as they read, and new learning objectives guide students through each chapter and help them to review major goals. Software Hints provide instructions for using the most up-to-date technology packages. The Second Edition also includes expanded coverage and instruction of Excel® 2010 and the XLSTAT ™ add-in. The MyStatLab™ course management system includes increased exercise coverage with the Second Edition, along with 100% of the You Do It exercises and a library of 1,000 Conceptual Questions that require students to apply their statistical understanding to conceptual business scenarios. Business Insight Videos show students how statistical methods are used by real businesses, and new StatTalk Videos present statistical concepts through a series of fun, brief, real-world examples. Technology tutorial videos at the exercise level support software use. Features + Benefits Statistics in Practice: Preparing Students for Real Business 4-M Examples (Motivation, Method, Mechanics, Message) provide a consistent methodology used for worked-out examples. This approach gives students a consistent structure for solving problems and presenting their findings in the appropriate context. Running Business Examples start each chapter by framing a business question to motivate the contents of the chapter. The example is referenced throughout the chapter when new statistical methods are presented. Statistics in Action case studies follow each of the four parts of the book. These longer applications expand on the statistical methods presented within the preceding part and use them to delve into substantive aspects of real-world business cases. Video resources available in MyStatLab offer students insight into how statistical concepts are applied in the business world and the world around us. Business Insight Videos show how statistical methods are used by real businesses. NEW! StatTalk Videos present statistical concepts through a series of fun, brief, real-world vignettes. Practice & Support: Challenging Students to Assess, Analyze and Report NEW! More than 150 exercises are new or have been updated to provide readers with the most up-to-date and relevant data available. Exercises are divided into five types. Each type focuses on a particular skill to build a deeper understanding of business statistics. Mix and Match and True/False problems test whether students recognize symbols and important steps of calculations. Think About It questions encourage students to pull together concepts and ideas from the chapter; no technology is required. You Do It problems provide practice working through the mechanics of solving a problem (statistical software usage is recommended). These exercises apply the statistical concepts students have learned in the chapter to data related to a business application. Data are available on the included CD-ROM. 4-M Questions are richer, more substantive problems that mimic real applications of statistics in business. Data are available on the included CD-ROM. Support NEW! 30 new What Do You Think? questions check students’ comprehension of the important ideas in the preceding section, ensuring that they understand the concepts before moving on in the chapter. Caution icons indicate a concept that can be troublesome and helps students avoid making common mistakes. Tip icons highlight important ideas or hints within the exposition so that readers don’t overlook them. Best Practices and Pitfalls listed at the end of every chapter offer reminders to help students avoid mistakes such as using the wrong method for a situation, or misinterpreting results. Technology Integration: Giving Students More Tools for Their Future Careers Software Hints at the end of each chapter provide relevant commands for popular statistics packages: Excel®, Minitab®, and JMP®. Extensive graphics, including Excel screenshots throughout the chapters and exercise sets, give students the opportunity to get familiar with seeing and interpreting statistical software output. Technology Tutorial Videos and Study Cards within MyStatLab provide targeted guidance to using statistical software. (Study Cards are available for bundling.) Preface Index of Application PART ONE: VARIATION 1. Introduction 1.1 What Is Statistics? 1.2 Previews 2. Data 2.1 Data Tables 2.2 Categorical and Numerical Data 2.3 Recoding and Aggregation 2.4 Time Series 2.5 Further Attributes of Data Chapter Summary 3. Describing Categorical Data 3.1 Looking at Data 3.2 Charts of Categorical Data 3.3 The Area Principle 3.4 Mode and Median Chapter Summary 4. Describing Numerical Data 4.1 Summaries of Numerical Variables 4.2 Histograms 4.3 Boxplot 4.4 Shape of a Distribution 4.5 Epilog Chapter Summary 5. Association between Categorical Variables 5.1 Contingency Tables 5.2 Lurking Variables and Simpson's Paradox 5.3 Strength of Association Chapter Summary 6. Association between Quantitative Variables 6.1 Scatterplots 6.2 Association in Scatterplots 6.3 Measuring Association 6.4 Summarizing Association with a Line 6.5 Spurious Correlation Chapter Summary Statistics in Action: Financial Time Series Statistics in Action: Executive Compensation PART TWO: PROBABILITY 7. Probability 7.1 From Data to Probability 7.2 Rules for Probability 7.3 Independent Events Chapter Summary 8. Conditional Probability 8.1 From Tables to Probabilities 8.2 Dependent Events 8.3 O rganizing Probabilities 8.4 O rder in Conditional Probabilities Chapter Summary 9. Random Variables 9.1 Random Variables 9.2 Properties of Random Variables 9.3 Properties of Expected Values 9.4 Comparing Random Variables Chapter Summary 10. Association between Random Variables 10.1 Portfolios and Random Variables 10.2 Joint Probability Distribution 10.3 Sums of Random Variables 10.4 Dependence between Random Variables 10.5 IID Random Variables 10.6 Weighted Sums Chapter Summary 11. Probability Models for Counts 11.1 Random Variables for Counts 11.2 Binomial Model 11.3 Properties of Binomial Random Variables 11.4 Poisson Model Chapter Summary 12. The Normal Probability Model 12.1 Normal Random Variable 12.2 The Normal Model 12.3 Percentiles 12.4 Departures from Normality Chapter Summary Statistics in Action: Managing Financial Risk Statistics in Action: Modeling Sampling Variation PART THREE: INFERENCE 13. Samples and Surveys 13.1 Two Surprising Properties of Samples 13.2 Variation 13.3 Alternative Sampling Methods 13.4 Questions to Ask Chapter Summary 14. Sampling Variation and Quality 14.1 Sampling Distribution of the Mean 14.2 Control Limits 14.3 Using a Control Chart 14.4 Control Charts for Variation Chapter Summary 15. Confidence Intervals 15.1 Ranges for Parameters 15.2 Confidence Interval for the Mean 15.3 Interpreting Confidence Intervals 15.4 Manipulating Confidence Intervals 15.5 Margin of Error Chapter Summary 16. Statistical Tests 16.1 Concepts of Statistical Tests 16.2 Testing the Proportion 16.3 Testing the Mean 16.4 Significance versus Importance 16.5 Confidence Interval or Test? Chapter Summary 17. Comparison 17.1 Data for Comparisons 17.2 Two-Sample z-test for Proportions 17.3 Two-Sample Confidence Interval for Proportions 17.4 Two-Sample T-test 17.5 Confidence Interval for the Difference between Means 17.6 Paired Comparisons Chapter Summary 18. Inference for Counts 18.1 Chi-Squared Tests 18.2 Test of Independence 18.3 General versus Specific Hypotheses 18.4 Tests of Goodness of Fit Chapter Summary Statistics in Action: Rare Events Statistics in Action: Data Mining Using Chi-Squared PART FOUR: REGRESSION MODELS 19. Linear Patterns 19.1 Fitting a Line to Data 19.2 Interpreting the Fitted Line 19.3 Properties of Residuals 19.4 Explaining Variation 19.5 Conditions for Simple Regression Chapter Summary 20. Curved Patterns 20.1 Detecting Nonlinear Patterns 20.2 Transformations 20.3 Reciprocal Transformation 20.4 Logarithm Transformation Chapter Summary 21. The Simple Regression Model 21.1 The Simple Regression Model 21.2 Conditions for the SRM 21.3 Inference in Regression 21.4 Prediction Intervals Chapter Summary 22. Regression Diagnostics 22.1 Changing Variation 22.2 Outliers 22.3 Dependent Errors and Time Series Chapter Summary 23. Multiple Regression 23.1 The Multiple Regression Model 23.2 Interpreting Multiple Regression 23.3 Checking Conditions 23.4 Inference in Multiple Regression 23.5 Steps in Fitting a Multiple Regression Chapter Summary 24. Building Regression Models 24.1 Identifying Explanatory Variables 24.2 Collinearity 24.3 Removing Explanatory Variables Chapter Summary 25. Categorical Explanatory Variables 25.1 Two-Sample Comparisons 25.2 Analysis of Covariance 25.3 Checking Conditions 25.4 Interactions and Inference 25.5 Regression with Several Groups Chapter Summary 26. Analysis of Variance 26.1 Comparing Several Groups 26.2 Inference in ANOVA Regression Models 26.3 Multiple Comparisons 26.4 Groups of Different Size Chapter Summary 27. Time Series 27.1 Decomposing a Time Series 27.2 Regression Models 27.3 Checking the Model Chapter Summary Statistics in Action: Analyzing Experiments Statistics in Action: Automated Modeling Appendix: Tables Answers Photo Acknowledgments Index Supplementary Material (online-only) Alternative Approaches to Inference More Regression 2-Way ANOVAWere you looking for the book with access to MyStatLab? This product is the book alone, and does NOT come with access to MyStatLab. Buy the book and access card package to save money on this resource. In Statistics for Business: Decision Making and Analysis, authors Robert Stine and Dean Foster of the University of Pennsylvania's Wharton School, take a sophisticated approach to teaching statistics in the context of making good business decisions. The authors show students how to recognize and understand each business question, use statistical tools to do the analysis, and how to communicate their results clearly and concisely. In addition to providing cases and real data to demonstrate real business situations, this text provides resources to support understanding and engagement. A successful problem-solving framework in the 4-M Examples (Motivation, Method, Mechanics, Message) model a clear outline for solving problems, new What Do You Think questions give students an opportunity to stop and check their understanding as they read, and new learning objectives guide students through each chapter and help them to review major goals. Software Hints provide instructions for using the most up-to-date technology packages. The Second Edition also includes expanded coverage and instruction of Excel® 2010 and the XLSTAT (TM) add-in. The MyStatLab(TM) course management system includes increased exercise coverage with the Second Edition, along with 100% of the You Do It exercises and a library of 1,000 Conceptual Questions that require students to apply their statistical understanding to conceptual business scenarios. Business Insight Videos show students how statistical methods are used by real businesses, and new StatTalk Videos present statistical concepts through a series of fun, brief, real-world examples. Technology tutorial videos at the exercise level support software use.

Anbieter: buecher

Stand: 08.08.2020 Zum Angebot

Stand: 08.08.2020 Zum Angebot

46,96 € *

ggf. zzgl. Versand

ggf. zzgl. Versand

Anbieter: buecher

Stand: 08.08.2020 Zum Angebot

Stand: 08.08.2020 Zum Angebot

68,00 € *

ggf. zzgl. Versand

ggf. zzgl. Versand

Present study deals with operational risks of banks (i.e. as Basel II defines the risk of loss resulting from inadequate or failed operation of people, systems, and processes or from external events ). The complexity of financial institutions and the regulatory efforts make the analysis of the operational risk necessary. The main message of this book is that institution size has an important effect on operational risk exposure and management. Firstly, a well-behaving stylised stochastic process based approach underpins the applicability of Poisson frequency and fat-tailed loss distributions, however a method built from historical data on a small sample may result in estimation bias. Secondly similarly to the results for other countries the total operational risk losses in a given period are significantly correlated with gross income-based size of banks in Hungary as well, mainly driven by frequency. Finally, it is found that larger institutions are more inclined to use advanced operational risk management methods. This might be a favourable tendency from systemic risk point of view, as institutions with potentially higher system risk tend to apply more conscious risk management.

Anbieter: Dodax

Stand: 08.08.2020 Zum Angebot

Stand: 08.08.2020 Zum Angebot

45,00 € *

ggf. zzgl. Versand

ggf. zzgl. Versand

High Quality Content by WIKIPEDIA articles! In probability and statistics the negative binomial distribution (including the Pascal distribution or Polya distribution) is a discrete probability distribution. It arises as the probability distribution of the number of failures in a sequence of Bernoulli trials needed to get a specified (non-random) number of successes. If one throws a die repeatedly until the third time a "1" appears, then the probability distribution of the number of non-"1"s that appear before the third "1" is a negative binomial distribution. The Pascal distribution and Polya distribution are special cases of the negative binomial. There is a convention among engineers, climatologists, and others to reserve "negative binomial" in a strict sense or "Pascal" (after Blaise Pascal) for the case of an integer-valued parameter r, to the right, and use "Polya" (for George Pólya) for the real-valued case. The Polya distribution more accurately models occurrences of "contagious" discrete events, like tornado outbreaks, than does the Poisson distribution.

Anbieter: Dodax

Stand: 08.08.2020 Zum Angebot

Stand: 08.08.2020 Zum Angebot

39,00 € *

ggf. zzgl. Versand

ggf. zzgl. Versand

High Quality Content by WIKIPEDIA articles! In probability theory and statistics, the Poisson distribution (pronounced [pwasõ]) (or Poisson law of large numbers) is a discrete probability distribution that expresses the probability of a number of events occurring in a fixed period of time if these events occur with a known average rate and independently of the time since the last event. The Poisson distribution can also be used for the number of events in other specified intervals such as distance, area or volume.

Anbieter: Dodax

Stand: 08.08.2020 Zum Angebot

Stand: 08.08.2020 Zum Angebot

79,00 € *

ggf. zzgl. Versand

ggf. zzgl. Versand

The self-controlled case series method (SCCSM) is a novel study design to investigate associations between acute responses with transient point exposures (for example vaccination). The method provides an attractive alternative to cohort and case-control designs. The method is unusual in that it requires data only on individuals who experience a response (the cases ). The data are then analysed using a Poisson model, conditional on the total number of events occurring for each individual. This conditioning ensures that including only cases does not bias the relative risk estimator. The self-controlled case series method has been used to good effect in many settings, such as MMR vaccine and autism, oral polio vaccine and intussusception, MMR vaccine and idiopathic thrombocytopenic purpura, influenza vaccine and asthma exacerbations. However, hitherto, limited research had been undertaken on the statistical properties of the method in general, and virtually no work had been undertaken on design issues. All these issues were explored and consolidated by analysing data on intussusception and oral polio vaccine from GlaxoSmithKline (GSK).

Anbieter: Dodax

Stand: 08.08.2020 Zum Angebot

Stand: 08.08.2020 Zum Angebot