{"product_id":"methods-and-applications-of-statistics-in-business-finance-and-management-science-isbn-9780470405109","title":"Methods and Applications of Statistics in Business, Finance, and Management Science","description":"\u003cb\u003eInspired by the \u003ci\u003eEncyclopedia of Statistical Sciences, Second Edition\u003c\/i\u003e, this volume presents the tools and techniques that are essential for carrying out best practices in the modern business world\u003c\/b\u003e  \u003cp\u003eThe collection and analysis of quantitative data drives some of the most important conclusions that are drawn in today's business world, such as the preferences of a customer base, the quality of manufactured products, the marketing of products, and the availability of financial resources. As a result, it is essential for individuals working in this environment to have the knowledge and skills to interpret and use statistical techniques in various scenarios. Addressing this need, \u003ci\u003eMethods and Applications of Statistics in Business, Finance, and Management Science\u003c\/i\u003e serves as a single, one-of-a-kind resource that guides readers through the use of common statistical practices by presenting real-world applications from the fields of business, economics, finance, operations research, and management science.\u003c\/p\u003e \u003cp\u003eUniting established literature with the latest research, this volume features classic articles from the acclaimed Encyclopedia of Statistical Sciences, Second Edition along with brand-new contributions written by today's leading academics and practitioners. The result is a compilation that explores classic methodology and new topics, including:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAnalytical methods for risk management\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eStatistical modeling for online auctions\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eRanking and selection in mutual funds\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eUses of Black-Scholes formula in finance\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eData mining in prediction markets\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eFrom auditing and marketing to stock market price indices and banking, the presented literature sheds light on the use of quantitative methods in research relating to common financial applications. In addition, the book supplies insight on common uses of statistical techniques such as Bayesian methods, optimization, simulation, forecasting, mathematical modeling, financial time series, and data mining in modern research.\u003c\/p\u003e \u003cp\u003eProviding a blend of traditional methodology and the latest research, \u003ci\u003eMethods and Applications of Statistics in Business, Finance, and Management Science\u003c\/i\u003e is an excellent reference for researchers, managers, consultants, and students in the fields of business, management science, operations research, supply chain management, mathematical finance, and economics who must understand statistical literature and carry out quantitative practices to make smart business decisions in their everyday work.\u003c\/p\u003e  Preface.  \u003cp\u003eContributors.\u003c\/p\u003e \u003cp\u003e1 Alternatives to Black-Scholes Formulation in Finance.\u003c\/p\u003e \u003cp\u003e1.1 Introduction.\u003c\/p\u003e \u003cp\u003e1.2 Motivation for Alternative Models.\u003c\/p\u003e \u003cp\u003e1.3 Methods of Valuation.\u003c\/p\u003e \u003cp\u003e1.4 Stochastic Interest-Rate Models.\u003c\/p\u003e \u003cp\u003e1.5 Stochastic Volatility Models.\u003c\/p\u003e \u003cp\u003e1.6 Models with Lévy Processes.\u003c\/p\u003e \u003cp\u003e2 Analytical Methods of Risk Management: An Engineering Systems Perspective.\u003c\/p\u003e \u003cp\u003e2.1 Introduction.\u003c\/p\u003e \u003cp\u003e2.2 Risk Management in Engineering Systems.\u003c\/p\u003e \u003cp\u003e2.3 Risk Assessment and Analysis.\u003c\/p\u003e \u003cp\u003e2.4 Allocating Resources.\u003c\/p\u003e \u003cp\u003e2.5 Conclusion.\u003c\/p\u003e \u003cp\u003e3 ARCH and GARCH Models.\u003c\/p\u003e \u003cp\u003e3.1 Introduction.\u003c\/p\u003e \u003cp\u003e3.2 Volatility Clustering.\u003c\/p\u003e \u003cp\u003e3.3 GARCH.\u003c\/p\u003e \u003cp\u003e3.4 IGARCH.\u003c\/p\u003e \u003cp\u003e3.5 EGARCH.\u003c\/p\u003e \u003cp\u003e3.6 Alternative Parameterizations.\u003c\/p\u003e \u003cp\u003e3.7 Time-Varying Parameter and Bilinear Models.\u003c\/p\u003e \u003cp\u003e3.8 Estimation and Inference.\u003c\/p\u003e \u003cp\u003e3.9 Testing.\u003c\/p\u003e \u003cp\u003e3.10 Empirical Example.\u003c\/p\u003e \u003cp\u003e3.11 Future Developments.\u003c\/p\u003e \u003cp\u003e4 Bayesian Forecasting.\u003c\/p\u003e \u003cp\u003e4.1 Introduction.\u003c\/p\u003e \u003cp\u003e4.2 Background.\u003c\/p\u003e \u003cp\u003e4.3 Dynamic Bayesian Models.\u003c\/p\u003e \u003cp\u003e4.4 Normal Dynamic Linear Models.\u003c\/p\u003e \u003cp\u003e4.5 Component Dynamic Linear Models.\u003c\/p\u003e \u003cp\u003e4.6 Discounting.\u003c\/p\u003e \u003cp\u003e4.7 Intervention.\u003c\/p\u003e \u003cp\u003e4.8 Monitoring and Adaptation.\u003c\/p\u003e \u003cp\u003e4.9 Mixtures of Dynamic Models.\u003c\/p\u003e \u003cp\u003e4.10 Non-normal Nonlinear Models.\u003c\/p\u003e \u003cp\u003e4.11 Multivariate Models.\u003c\/p\u003e \u003cp\u003e4.12 Computation and Simulation.\u003c\/p\u003e \u003cp\u003e4.13 Related Areas.\u003c\/p\u003e \u003cp\u003e5 Bayesian Networks.\u003c\/p\u003e \u003cp\u003e5.1 Examples and Definitions.\u003c\/p\u003e \u003cp\u003e5.2 Constructing Bayesian-Network Models.\u003c\/p\u003e \u003cp\u003e5.3 Models Specified by Input Lists.\u003c\/p\u003e \u003cp\u003e5.4 Graphically Specified Models.\u003c\/p\u003e \u003cp\u003e5.5 Conditionally Specified Models.\u003c\/p\u003e \u003cp\u003e5.6 Learning Models from Data.\u003c\/p\u003e \u003cp\u003e5.7 Propagation in Bayesian Networks.\u003c\/p\u003e \u003cp\u003e5.8 Available Software.\u003c\/p\u003e \u003cp\u003e6 Box–Jenkins Model.\u003c\/p\u003e \u003cp\u003e6.1 Introduction.\u003c\/p\u003e \u003cp\u003e7 Business Forecasting Methods.\u003c\/p\u003e \u003cp\u003e7.1 Introduction.\u003c\/p\u003e \u003cp\u003e7.2 Trend Curves.\u003c\/p\u003e \u003cp\u003e7.3 Exponential Smoothing.\u003c\/p\u003e \u003cp\u003e7.4 Exponential Smoothing and Arima Model Building.\u003c\/p\u003e \u003cp\u003e7.5 Regression and Econometric Methods.\u003c\/p\u003e \u003cp\u003e7.6 Regression and Time-Series Principles.\u003c\/p\u003e \u003cp\u003e7.7 Combination of Forecasts.\u003c\/p\u003e \u003cp\u003e7.8 Evaluation of Forecasts.\u003c\/p\u003e \u003cp\u003e7.9 Summary.\u003c\/p\u003e \u003cp\u003e8 Combination of Forecasts.\u003c\/p\u003e \u003cp\u003e8.1 Introduction.\u003c\/p\u003e \u003cp\u003e8.2 The Theory of Combining.\u003c\/p\u003e \u003cp\u003e8.3 Estimators of the Weights.\u003c\/p\u003e \u003cp\u003e8.4 An Example.\u003c\/p\u003e \u003cp\u003e8.5 Further Extensions.\u003c\/p\u003e \u003cp\u003e9 Decision Theory.\u003c\/p\u003e \u003cp\u003e9.1 Introduction.\u003c\/p\u003e \u003cp\u003e9.2 Parameters, Decisions, and Consequences.\u003c\/p\u003e \u003cp\u003e9.3 Utility.\u003c\/p\u003e \u003cp\u003e9.4 Components of a Decision Problem.\u003c\/p\u003e \u003cp\u003e9.5 Subjective Probability.\u003c\/p\u003e \u003cp\u003e9.6 Decision Analysis.\u003c\/p\u003e \u003cp\u003e9.7 Statistical Decision Problems.\u003c\/p\u003e \u003cp\u003e9.8 Conjugate Families of Prior Distributions.\u003c\/p\u003e \u003cp\u003e9.9 Improper Prior Distributions.\u003c\/p\u003e \u003cp\u003e9.10 Estimation and Tests of Hypothesis.\u003c\/p\u003e \u003cp\u003e9.11 Sequential Decision Problems.\u003c\/p\u003e \u003cp\u003e10 Dynamic Programming.\u003c\/p\u003e \u003cp\u003e10.1 Introduction.\u003c\/p\u003e \u003cp\u003e10.2 Definitions and Examples.\u003c\/p\u003e \u003cp\u003e10.3 Some Fundamental Principles.\u003c\/p\u003e \u003cp\u003e10.4 The Optimality of Equation and Backward Induction.\u003c\/p\u003e \u003cp\u003e10.5 Stationary Plans.\u003c\/p\u003e \u003cp\u003e11 Estimation of Travel Distance.\u003c\/p\u003e \u003cp\u003e11.1 Introduction.\u003c\/p\u003e \u003cp\u003e11.2 Distance Functions.\u003c\/p\u003e \u003cp\u003e11.3 Goodness-of-Fit Criteria.\u003c\/p\u003e \u003cp\u003e11.4 Areas of Future Research.\u003c\/p\u003e \u003cp\u003e12 Financial Time Series.\u003c\/p\u003e \u003cp\u003e12.1 Asset Price and Return.\u003c\/p\u003e \u003cp\u003e12.2 Fundamental and Technical Analyses.\u003c\/p\u003e \u003cp\u003e12.3 Volatility Model.\u003c\/p\u003e \u003cp\u003e12.4 High-Frequency Data.\u003c\/p\u003e \u003cp\u003e12.5 Continuous-Time Model.\u003c\/p\u003e \u003cp\u003e13 Forecasting.\u003c\/p\u003e \u003cp\u003e13.1 Introduction.\u003c\/p\u003e \u003cp\u003e13.2 Model Components.\u003c\/p\u003e \u003cp\u003e13.3 Model Fitting for Forecasting.\u003c\/p\u003e \u003cp\u003e13.4 Forecasting Methods.\u003c\/p\u003e \u003cp\u003e13.5 Forecast Quality.\u003c\/p\u003e \u003cp\u003e14 Foundations of Risk Measurement.\u003c\/p\u003e \u003cp\u003e14.1 Introduction.\u003c\/p\u003e \u003cp\u003e15 Functional Networks.\u003c\/p\u003e \u003cp\u003e15.1 Introduction.\u003c\/p\u003e \u003cp\u003e15.2 Elements of Functional Networks.\u003c\/p\u003e \u003cp\u003e15.3 Differences Between Standard NNs and FNs.\u003c\/p\u003e \u003cp\u003e15.4 Development and Implentation of FNs.\u003c\/p\u003e \u003cp\u003e15.5 An Example of Application.\u003c\/p\u003e \u003cp\u003e16 Game Theory.\u003c\/p\u003e \u003cp\u003e16.1 Introduction.\u003c\/p\u003e \u003cp\u003e16.2 Strategies and Payoffs.\u003c\/p\u003e \u003cp\u003e16.3 Applications to Statistics.\u003c\/p\u003e \u003cp\u003e17 Intervention Model Analysis.\u003c\/p\u003e \u003cp\u003e17.1 Introduction.\u003c\/p\u003e \u003cp\u003e17.2 Time-Series and Intervention Models.\u003c\/p\u003e \u003cp\u003e17.3 Applications and Extensions.\u003c\/p\u003e \u003cp\u003e18 Inventory Theory.\u003c\/p\u003e \u003cp\u003e18.1 Introduction.\u003c\/p\u003e \u003cp\u003e18.2 Historical Background.\u003c\/p\u003e \u003cp\u003e18.3 Models with Known Demand.\u003c\/p\u003e \u003cp\u003e18.4 Models with Uncertain Demand.\u003c\/p\u003e \u003cp\u003e18.5 Conclusion.\u003c\/p\u003e \u003cp\u003e19 Manpower Planning.\u003c\/p\u003e \u003cp\u003e19.1 Introduction.\u003c\/p\u003e \u003cp\u003e19.2 Statistical Analysis of Wastage.\u003c\/p\u003e \u003cp\u003e19.3 Markov Models for Graded Systems.\u003c\/p\u003e \u003cp\u003e19.4 Renewal Models for Graded Systems.\u003c\/p\u003e \u003cp\u003e19.5 Literature.\u003c\/p\u003e \u003cp\u003e20 Markov Networks.\u003c\/p\u003e \u003cp\u003e20.1 Statement of the Problem.\u003c\/p\u003e \u003cp\u003e20.2 Some Basic Concepts of Graphs.\u003c\/p\u003e \u003cp\u003e20.3 Constructing Markov Network Models.\u003c\/p\u003e \u003cp\u003e20.4 Propagation in markov Networks.\u003c\/p\u003e \u003cp\u003e20.5 Available Software.\u003c\/p\u003e \u003cp\u003e21 Methods of Estimation of Risks and Analysis of Business Processes.\u003c\/p\u003e \u003cp\u003e21.1 Introduction.\u003c\/p\u003e \u003cp\u003e21.2 Mathematical Models of Economic Systems in the Form of the Business Processes Portfolio.\u003c\/p\u003e \u003cp\u003e21.3 Risks of Economic Systems.\u003c\/p\u003e \u003cp\u003e21.4 Economic Systems Factors Analysis.\u003c\/p\u003e \u003cp\u003e22 Mining Functional Data in Prediction Markets.\u003c\/p\u003e \u003cp\u003e22.1 Introduction.\u003c\/p\u003e \u003cp\u003e22.2 Prediction Markets.\u003c\/p\u003e \u003cp\u003e22.3 Data.\u003c\/p\u003e \u003cp\u003e22.4 Functional Data Analysis.\u003c\/p\u003e \u003cp\u003e22.5 Discussion.\u003c\/p\u003e \u003cp\u003e23 Models for Bid Arrivals and Bidder Arrivals in Online Auctions.\u003c\/p\u003e \u003cp\u003e23.1 Introduction.\u003c\/p\u003e \u003cp\u003e23.2 Motivation.\u003c\/p\u003e \u003cp\u003e23.3 Features of Bid Arrivals.\u003c\/p\u003e \u003cp\u003e23.4 The BARISTA: A Three-Stage Nohomogeneous Poisson Process.\u003c\/p\u003e \u003cp\u003e23.5 Relating Bidder Arrivals and Bid Arrivals.\u003c\/p\u003e \u003cp\u003e24 Multiserver Queues.\u003c\/p\u003e \u003cp\u003e24.1 Introduction.\u003c\/p\u003e \u003cp\u003e24.2 Markovian Queues.\u003c\/p\u003e \u003cp\u003e24.3 Non-Markovian Queues.\u003c\/p\u003e \u003cp\u003e24.4 Other Methods.\u003c\/p\u003e \u003cp\u003e25 Multivariate Time-Series Analysis.\u003c\/p\u003e \u003cp\u003e25.1 Introduction.\u003c\/p\u003e \u003cp\u003e25.2 Stationary Mutivariate Time Series and Their Covariance Properties.\u003c\/p\u003e \u003cp\u003e25.3 Some Spectral Characteristics for Stationary Vector Processes.\u003c\/p\u003e \u003cp\u003e25.4 Linear Filtering Relations for Stationary Vector Processes.\u003c\/p\u003e \u003cp\u003e25.5 Linear Model Representations for Stationary Vector Processes.\u003c\/p\u003e \u003cp\u003e25.6 Vecotr Autoregressive Moving Average (ARMA) Model Representations.\u003c\/p\u003e \u003cp\u003e25.7 Nonstationary Vector Autoregressive Moving-Average Models.\u003c\/p\u003e \u003cp\u003e25.8 Forecasting for Vector Autoregressive Moving-Average Processes.\u003c\/p\u003e \u003cp\u003e25.9 Statistical Analysis of Vector Autoregressive Moving-Average Models.\u003c\/p\u003e \u003cp\u003e26 Network Analysis.\u003c\/p\u003e \u003cp\u003e26.1 Introduction.\u003c\/p\u003e \u003cp\u003e27 Network of Queues.\u003c\/p\u003e \u003cp\u003e27.1 Introduction.\u003c\/p\u003e \u003cp\u003e27.2 Some Background.\u003c\/p\u003e \u003cp\u003e27.3 Some Results.\u003c\/p\u003e \u003cp\u003e27.4 More General Networks.\u003c\/p\u003e \u003cp\u003e27.5 Sojourn Times in Queueing Networks.\u003c\/p\u003e \u003cp\u003e27.6 Customer Flow in Networks.\u003c\/p\u003e \u003cp\u003e27.7 Other Approaches and Topics.\u003c\/p\u003e \u003cp\u003e28 Neural Networks.\u003c\/p\u003e \u003cp\u003e28.1 Introduction.\u003c\/p\u003e \u003cp\u003e28.2 Feed-Forward Networks.\u003c\/p\u003e \u003cp\u003e28.3 Recurrent Networks.\u003c\/p\u003e \u003cp\u003e28.4 Associative-Memory Networks and Boltzmann Machines.\u003c\/p\u003e \u003cp\u003e28.5 Networks Trained by Unsupervised Learning.\u003c\/p\u003e \u003cp\u003e28.6 Use of the Bayesian Approach.\u003c\/p\u003e \u003cp\u003e28.7 Conclusion.\u003c\/p\u003e \u003cp\u003e29 Newsboy Inventory Problem.\u003c\/p\u003e \u003cp\u003e29.1 Introduction.\u003c\/p\u003e \u003cp\u003e30 Nonlinear Time Series.\u003c\/p\u003e \u003cp\u003e30.1 Introduction.\u003c\/p\u003e \u003cp\u003e30.2 Reviews of Linear Time Series.\u003c\/p\u003e \u003cp\u003e30.3 Nonparametric Methods.\u003c\/p\u003e \u003cp\u003e30.4 Parametric Models.\u003c\/p\u003e \u003cp\u003e30.5 Other Surveys and Comparisons.\u003c\/p\u003e \u003cp\u003e31 Nonstationary Time Series.\u003c\/p\u003e \u003cp\u003e31.1 Introduction.\u003c\/p\u003e \u003cp\u003e31.2 Removing Nonstationary Menas and Variances.\u003c\/p\u003e \u003cp\u003e31.3 Extensions.\u003c\/p\u003e \u003cp\u003e31.4 Homogeneous and Explosive Nonstationarity.\u003c\/p\u003e \u003cp\u003e31.5 Differencing.\u003c\/p\u003e \u003cp\u003e31.6 Starting Values and Nonstationarity.\u003c\/p\u003e \u003cp\u003e31.7 ARIMA Models.\u003c\/p\u003e \u003cp\u003e31.8 Sample Autocorrelations--Identifying the Degree of Differencing.\u003c\/p\u003e \u003cp\u003e31.9 Estimation of Unit and Explosive Roots.\u003c\/p\u003e \u003cp\u003e31.10 Forecasting.\u003c\/p\u003e \u003cp\u003e31.11 Variations and Extensions.\u003c\/p\u003e \u003cp\u003e31.12 Nonstationary Spectral Analysis.\u003c\/p\u003e \u003cp\u003e32 PERT.\u003c\/p\u003e \u003cp\u003e32.1 Introduction.\u003c\/p\u003e \u003cp\u003e32.2 Finding the Expected Critical Path Length.\u003c\/p\u003e \u003cp\u003e32.3 Simulation and Statistical Computations.\u003c\/p\u003e \u003cp\u003e32.4 Estimation of Individual Activity Times.\u003c\/p\u003e \u003cp\u003e32.5 Conclusions.\u003c\/p\u003e \u003cp\u003e33 Prediction and Forecasting.\u003c\/p\u003e \u003cp\u003e33.1 Introduction.\u003c\/p\u003e \u003cp\u003e33.2 Regression Models.\u003c\/p\u003e \u003cp\u003e33.3 Regression and Smoothing Methods for Extrapolating a Single Time Series.\u003c\/p\u003e \u003cp\u003e33.4 Forecasts from Univariate Time-Series Models.\u003c\/p\u003e \u003cp\u003e33.5 Forecasts from Multivariate Time-Series Models.\u003c\/p\u003e \u003cp\u003e33.6 State-Space Models, Kalman Flitering and Bayesian Forecasting.\u003c\/p\u003e \u003cp\u003e33.7 Econometric Models.\u003c\/p\u003e \u003cp\u003e33.8 Input-Output Tables.\u003c\/p\u003e \u003cp\u003e33.9 Turning Points and Business Cycle Indicators.\u003c\/p\u003e \u003cp\u003e33.10 Surveys of Anticipations and Intentions.\u003c\/p\u003e \u003cp\u003e33.11 Combination of Forecasts.\u003c\/p\u003e \u003cp\u003e33.12 Prediction of Qualitative Characteristics.\u003c\/p\u003e \u003cp\u003e33.13 Forecast Quality and the Evaluation of Forecasts.\u003c\/p\u003e \u003cp\u003e34 Pricing Foreign Exchange Options with Stochastic Volatility.\u003c\/p\u003e \u003cp\u003e34.1 Introduction.\u003c\/p\u003e \u003cp\u003e34.2 Arbitrage-Free Cross-Currency Markets.\u003c\/p\u003e \u003cp\u003e34.3 Stein and Stein Stochastic Volatility Model with Vasicêk Interest Rates.\u003c\/p\u003e \u003cp\u003e34.4 Heston's Stochastic Volatility Model with CIR Interest Rates.\u003c\/p\u003e \u003cp\u003e34.5 Foreign Exchange Option under Heston Volatility with Constant Interest Rates.\u003c\/p\u003e \u003cp\u003e34.6 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e35 Probabilistic Expert Systems.\u003c\/p\u003e \u003cp\u003e35.1 Introduction.\u003c\/p\u003e \u003cp\u003e35.2 Graph Types.\u003c\/p\u003e \u003cp\u003e35.3 Conditioal Independence and Markov Properties.\u003c\/p\u003e \u003cp\u003e35.4 Specification of Joint Distribution.\u003c\/p\u003e \u003cp\u003e35.5 Local Computation Algorithm.\u003c\/p\u003e \u003cp\u003e35.6 Extensions.\u003c\/p\u003e \u003cp\u003e36 Problem Solving in Statistics.\u003c\/p\u003e \u003cp\u003e36.1 Introduction.\u003c\/p\u003e \u003cp\u003e36.2 Phase 1: Study Design.\u003c\/p\u003e \u003cp\u003e36.3 Phase 2: Data Collection.\u003c\/p\u003e \u003cp\u003e36.4 Phase 3: Data Analysis.\u003c\/p\u003e \u003cp\u003e36.5 Postprocess Responsibilities.\u003c\/p\u003e \u003cp\u003e36.6 Conclusions.\u003c\/p\u003e \u003cp\u003e37 Queueing Theory.\u003c\/p\u003e \u003cp\u003e37.1 Introduction.\u003c\/p\u003e \u003cp\u003e37.2 Subsequent Development of the Simple Queue Model.\u003c\/p\u003e \u003cp\u003e37.3 Variants of the Simple Queueing Model.\u003c\/p\u003e \u003cp\u003e37.4 Concluding Remarks.\u003c\/p\u003e \u003cp\u003e38 Queues and Networks.\u003c\/p\u003e \u003cp\u003e38.1 Introduction.\u003c\/p\u003e \u003cp\u003e38.2 A Glimpse on Queueing Theory by Example.\u003c\/p\u003e \u003cp\u003e38.3 The Vocabulary of Queueing Theory.\u003c\/p\u003e \u003cp\u003e38.4 Little's Formulas.\u003c\/p\u003e \u003cp\u003e38.5 Markovian Queueing Systems of BD Type.\u003c\/p\u003e \u003cp\u003e38.6 General Service Times: The System M\/G\/1.\u003c\/p\u003e \u003cp\u003e38.7 The Systems M\/G\/c and G\/G\/c.\u003c\/p\u003e \u003cp\u003e38.8 Networks of Queues.\u003c\/p\u003e \u003cp\u003e38.9 Approximations and Numerical Methods.\u003c\/p\u003e \u003cp\u003e38.10 Simulation.\u003c\/p\u003e \u003cp\u003e39 Ranking and Selection Among Mutual Funds.\u003c\/p\u003e \u003cp\u003e39.1 Introduction.\u003c\/p\u003e \u003cp\u003e39.2 Statistical Underpinnings of Data Mining Using Combinatorial Fusion Algorithm.\u003c\/p\u003e \u003cp\u003e39.3 Stochastic Dominance and Asymmetric Attitude Towards Risk.\u003c\/p\u003e \u003cp\u003e39.4 Summary and Final Remarks.\u003c\/p\u003e \u003cp\u003e40 Risk Theory.\u003c\/p\u003e \u003cp\u003e40.1 Introduction.\u003c\/p\u003e \u003cp\u003e41 Statistical Consulting.\u003c\/p\u003e \u003cp\u003e41.1 Definition.\u003c\/p\u003e \u003cp\u003e41.2 What Consultants Do.\u003c\/p\u003e \u003cp\u003e41.3 Historical Perspective.\u003c\/p\u003e \u003cp\u003e41.4 Skills Needed by a Consultant.\u003c\/p\u003e \u003cp\u003e41.5 Consulting and Communication.\u003c\/p\u003e \u003cp\u003e41.6 Computers and Consultants.\u003c\/p\u003e \u003cp\u003e41.7 Keeping Up with Statistics.\u003c\/p\u003e \u003cp\u003e41.8 Ethics.\u003c\/p\u003e \u003cp\u003e41.9 Teaching Consulting.\u003c\/p\u003e \u003cp\u003e41.10 Rewards of Consulting.\u003c\/p\u003e \u003cp\u003e42 Statistical Methods in Inventory Effect and Analysis.\u003c\/p\u003e \u003cp\u003e42.1 Introduction.\u003c\/p\u003e \u003cp\u003e42.2 Futures Markets.\u003c\/p\u003e \u003cp\u003e42.3 Backwardation and Inventory Effect.\u003c\/p\u003e \u003cp\u003e42.4 Inventory Effect: A Preliminary Analysis.\u003c\/p\u003e \u003cp\u003e42.5 Ordered Bivariate Normal Distribution.\u003c\/p\u003e \u003cp\u003e42.6 Bivariate Lognormal Distribution.\u003c\/p\u003e \u003cp\u003e42.7 Ordered Bivariate Lognormal Distribution.\u003c\/p\u003e \u003cp\u003e42.8 Conclusions.\u003c\/p\u003e \u003cp\u003e43 Statistical Methods in Risk Management by Futures Clearinghouses.\u003c\/p\u003e \u003cp\u003e43.1 Introduction.\u003c\/p\u003e \u003cp\u003e43.2 Margin Requirements.\u003c\/p\u003e \u003cp\u003e43.3 Settlement Frequency.\u003c\/p\u003e \u003cp\u003e43.4 Capital Requirements.\u003c\/p\u003e \u003cp\u003e43.5 Price Limits.\u003c\/p\u003e \u003cp\u003e43.6 Position Limits.\u003c\/p\u003e \u003cp\u003e43.7 Conclusion.\u003c\/p\u003e \u003cp\u003e44 Statistics in Auditing.\u003c\/p\u003e \u003cp\u003e44.1 Introduction.\u003c\/p\u003e \u003cp\u003e44.2 Study of Internal Control System.\u003c\/p\u003e \u003cp\u003e44.3 Study of Account Balances.\u003c\/p\u003e \u003cp\u003e44.4 Analytical Review.\u003c\/p\u003e \u003cp\u003e45 Statistics in Banking.\u003c\/p\u003e \u003cp\u003e45.1 Introduction.\u003c\/p\u003e \u003cp\u003e45.2 Further Reading.\u003c\/p\u003e \u003cp\u003e46 Statistics in Finance.\u003c\/p\u003e \u003cp\u003e46.1 Introduction.\u003c\/p\u003e \u003cp\u003e46.2 Regression Analysis and the Market Model.\u003c\/p\u003e \u003cp\u003e46.3 Factor, Multiple Discriminant and Logit Applications.\u003c\/p\u003e \u003cp\u003e46.4 Time-Series Analyses of Financial Information.\u003c\/p\u003e \u003cp\u003e46.5 Statistical Decision Theory and Finance.\u003c\/p\u003e \u003cp\u003e47 Statistics in Management Science.\u003c\/p\u003e \u003cp\u003e47.1 Introduction.\u003c\/p\u003e \u003cp\u003e47.2 Using Regression to Estimate Managerial Decision Rules.\u003c\/p\u003e \u003cp\u003e47.3 Using Regression for Input Data in Modeling.\u003c\/p\u003e \u003cp\u003e47.4 Construction of Causal Models by Regression.\u003c\/p\u003e \u003cp\u003e47.5 Statistical Analysis of Algorithmic Performance Data.\u003c\/p\u003e \u003cp\u003e47.6 Sampling Theory.\u003c\/p\u003e \u003cp\u003e47.7 Other Statistical Tools.\u003c\/p\u003e \u003cp\u003e48 Statistics in Marketing.\u003c\/p\u003e \u003cp\u003e48.1 Introduction.\u003c\/p\u003e \u003cp\u003e48.2 Some Early Contributions.\u003c\/p\u003e \u003cp\u003e48.3 The Uses of Statistics in Marketing Research.\u003c\/p\u003e \u003cp\u003e48.4 Sample Survey Methods.\u003c\/p\u003e \u003cp\u003e48.5 Multivariate Techniques.\u003c\/p\u003e \u003cp\u003e48.6 Forecasting Methods.\u003c\/p\u003e \u003cp\u003e48.7 Psychometric Methods in the Measurement of Consumer Perceptions and Preferences.\u003c\/p\u003e \u003cp\u003e48.8 Experimentation.\u003c\/p\u003e \u003cp\u003e48.9 Probability Models.\u003c\/p\u003e \u003cp\u003e49 Statistics of Risk Management.\u003c\/p\u003e \u003cp\u003e49.1 Introduction.\u003c\/p\u003e \u003cp\u003e49.2 General Concept of Risk Management and Monitoring.\u003c\/p\u003e \u003cp\u003e49.3 Scope.\u003c\/p\u003e \u003cp\u003e49.4 Evolution of Risk Management.\u003c\/p\u003e \u003cp\u003e49.5 Insurance.\u003c\/p\u003e \u003cp\u003e49.6 Gambling, Capital Budgeting and Investments.\u003c\/p\u003e \u003cp\u003e49.7 Technological Risk Management.\u003c\/p\u003e \u003cp\u003e49.8 Low-Probability-High-Consequence Risk Management.\u003c\/p\u003e \u003cp\u003e49.9 Environmental Risk and Monitoring Systems.\u003c\/p\u003e \u003cp\u003e49.10 Epidemiology and Disease Detection.\u003c\/p\u003e \u003cp\u003e49.11 Principles of Statistical Monitoring.\u003c\/p\u003e \u003cp\u003e50 Stochastic Differential Equations: Applications in Economics and Management Science.\u003c\/p\u003e \u003cp\u003e50.1 Introduction.\u003c\/p\u003e \u003cp\u003e50.2 Option Pricing.\u003c\/p\u003e \u003cp\u003e50.3 Stochastic Optimal Control.\u003c\/p\u003e \u003cp\u003e50.4 Final Remarks.\u003c\/p\u003e \u003cp\u003e51 Stochastic Games.\u003c\/p\u003e \u003cp\u003e51.1 Introduction.\u003c\/p\u003e \u003cp\u003e51.2 Special Cases.\u003c\/p\u003e \u003cp\u003e51.3 Computation.\u003c\/p\u003e \u003cp\u003e52 Stock Market Price Indexes.\u003c\/p\u003e \u003cp\u003e52.1 Introduction.\u003c\/p\u003e \u003cp\u003e52.2 Definition and Uses.\u003c\/p\u003e \u003cp\u003e52.3 Brief History.\u003c\/p\u003e \u003cp\u003e52.4 Main Issues.\u003c\/p\u003e \u003cp\u003e52.5 A Numerical Example.\u003c\/p\u003e \u003cp\u003e52.6 Two Major Stock Market Price Indexes.\u003c\/p\u003e \u003cp\u003e52.7 The S\u0026amp;P 500.\u003c\/p\u003e \u003cp\u003e52.8 Comparison of Four International Indexes.\u003c\/p\u003e \u003cp\u003e52.9 Stock Market Indexes and Portfolio Analysis.\u003c\/p\u003e \u003cp\u003e52.10 Summary.\u003c\/p\u003e \u003cp\u003e53 The Black-Scholes Formula and Its Applications in Finance.\u003c\/p\u003e \u003cp\u003e53.1 Introduction.\u003c\/p\u003e \u003cp\u003e53.2 The Black-Scholes Model.\u003c\/p\u003e \u003cp\u003e53.3 European Call and Put Options.\u003c\/p\u003e \u003cp\u003e53.4 Some Exotic Options.\u003c\/p\u003e \u003cp\u003e53.5 American Options.\u003c\/p\u003e \u003cp\u003e53.6 Application to the Modeling of Credit Risk.\u003c\/p\u003e \u003cp\u003e53.7 Real Options.\u003c\/p\u003e \u003cp\u003e54 Time Series.\u003c\/p\u003e \u003cp\u003e54.1 Introduction.\u003c\/p\u003e \u003cp\u003e54.2 Examples of Time Series.\u003c\/p\u003e \u003cp\u003e54.3 A Historical Perspective.\u003c\/p\u003e \u003cp\u003e54.4 Stationarity.\u003c\/p\u003e \u003cp\u003e54.5 The Frequency Domain.\u003c\/p\u003e \u003cp\u003e54.6 The Time Domain.\u003c\/p\u003e \u003cp\u003e54.7 State-Space Models.\u003c\/p\u003e \u003cp\u003e54.8 Transfer Functions and Interventions.\u003c\/p\u003e \u003cp\u003e54.9 Other Topics.\u003c\/p\u003e \u003cp\u003e54.10 Literature.\u003c\/p\u003e \u003cp\u003e54.11 Computer Programs.\u003c\/p\u003e \u003cp\u003e54.12 Future Developments.\u003c\/p\u003e \u003cp\u003eIndex.\u003c\/p\u003e \"Providing a blend of traditional methodology and the latest research, the book may well be used as a reference guide for researchers, managers, consultants and students in the fields of business, management science, operations research, supply chain management, mathematical finance and economics, who must under-stand the statistical literature and carry out quantitative practices to make smart businessdecisions in their everyday work.\" (Zentralblatt MATH, 2011)  \u003cp\u003e \"As a result, it is essential for individuals working in this environment to have the knowledge and skills to interpret and use statistical techniques in various scenarios. Addressing this need, Methods and Applications of Statistics in Business, Finance, and Management Science serves as a single, one-of-a-kind resource that guides readers through the use of common statistical practices by presenting real-world applications from the fields of business, economics, finance, operations research, and management science.\" (Yahoo Finance Canada, 28 October 2010)\u003c\/p\u003e  \u003cp\u003e \"As a result, it is essential for individuals working in this environment to have the knowledge and skills to interpret and use statistical techniques in various scenarios. Addressing this need, Methods and Applications of Statistics in Business, Finance, and Management Science serves as a single, one-of-a-kind resource that guides readers through the use of common statistical practices by presenting real-world applications from the fields of business, economics, finance, operations research, and management science.\" (Forbes.com, 28 October 2010)\u003c\/p\u003e  \u003cp\u003e \"Providing a blend of traditional methodology and the latest research, Methods and Applications of Statistics in Business, Finance, and Management Science is an excellent reference for researchers, managers, consultants, and students in the fields of business, management science, operations research, supply chain management, mathematical finance, and economics who must understand statistical literature and carry out quantitative practices to make smart business decisions in their everyday work.\" (Green.TMCnet.com, 28 October 2010)\u003c\/p\u003e \u003cb\u003eN. Balakrishnan\u003c\/b\u003e, PhD, is Professor in the Department of Mathematics and Statistics at McMaster University, Canada. Dr. Balakrishnan is coeditor of \u003ci\u003eWiley's Encyclopedia of Statistical Sciences, Second Edition\u003c\/i\u003e and also serves as Editor in Chief of \u003ci\u003eCommunications in Statistics.\u003c\/i\u003e A Fellow of the American Statistical Association and the Institute of Mathematical Statistics, Dr. Balakrishnan is the coauthor of \u003ci\u003ePrecedence-Type Tests and Applications\u003c\/i\u003e and \u003ci\u003eA Primer on Statistical Distributions,\u003c\/i\u003e both published by Wiley.  Inspired by the \u003ci\u003eEncyclopedia of Statistical Sciences, Second Edition\u003c\/i\u003e, this volume presents the tools and techniques that are essential for carrying out best practices in the modern business world  \u003cp\u003eThe collection and analysis of quantitative data drives some of the most important conclusions that are drawn in today's business world, such as the preferences of a customer base, the quality of manufactured products, the marketing of products, and the availability of financial resources. As a result, it is essential for individuals working in this environment to have the knowledge and skills to interpret and use statistical techniques in various scenarios. Addressing this need, \u003ci\u003eMethods and Applications of Statistics in Business, Finance, and Management Science\u003c\/i\u003e serves as a single, one-of-a-kind resource that guides readers through the use of common statistical practices by presenting real-world applications from the fields of business, economics, finance, operations research, and management science.\u003c\/p\u003e \u003cp\u003eUniting established literature with the latest research, this volume features classic articles from the acclaimed \u003ci\u003eEncyclopedia of Statistical Sciences, Second Edition\u003c\/i\u003e along with brand-new contributions written by today's leading academics and practitioners. The result is a compilation that explores classic methodology and new topics, including:\u003c\/p\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAnalytical methods for risk management\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eStatistical modeling for online auctions\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eRanking and selection in mutual funds\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eUses of Black-Scholes formula in finance\u003c\/p\u003e \u003c\/li\u003e \u003cli\u003e \u003cp\u003eData mining in prediction markets\u003c\/p\u003e \u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003eFrom auditing and marketing to stock market price indices and banking, the presented literature sheds light on the use of quantitative methods in research relating to common financial applications. In addition, the book supplies insight on common uses of statistical techniques such as Bayesian methods, optimization, simulation, forecasting, mathematical modeling, financial time series, and data mining in modern research.\u003c\/p\u003e \u003cp\u003eProviding a blend of traditional methodology and the latest research, \u003ci\u003eMethods and Applications of Statistics in Business, Finance, and Management Science\u003c\/i\u003e is an excellent reference for researchers, managers, consultants, and students in the fields of business, management science, operations research, supply chain management, mathematical finance, and economics who must understand statistical literature and carry out quantitative practices to make smart business decisions in their everyday work.\u003c\/p\u003e","brand":"Wiley","offers":[{"title":"Default Title","offer_id":47989615067365,"sku":"NP9780470405109","price":288.95,"currency_code":"USD","in_stock":false}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/1842\/7735\/files\/9780470405109.jpg?v=1761784818","url":"https:\/\/k12savings.com\/products\/methods-and-applications-of-statistics-in-business-finance-and-management-science-isbn-9780470405109","provider":"K12savings","version":"1.0","type":"link"}