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Methods and Applications of Statistics in Business, Finance, and Management Science presents a concise, well-rounded focus on the statistical concepts and applications that are essential for understanding gathered data in these areas. Inspired by the Encyclopedia of Statistical Sciences, Second Edition, this succinct reference features both established ESS2e articles and newly-acquired contributions from over 100 leading experts in academia and industry. Students, academics, and researchers in the fields of business, finance, economics, management science, and the related client disciplines will not want to be without this accessible resource.
N. Balakrishnan, PhD, is Professor in the Department of Mathematics and Statistics at McMaster University, Canada. Dr. Balakrishnan is coeditor of Wiley's Encyclopedia of Statistical Sciences, Second Edition and also serves as Editor in Chief of Communications in Statistics. A Fellow of the American Statistical Association and the Institute of Mathematical Statistics, Dr. Balakrishnan is the coauthor of Precedence-Type Tests and Applications and A Primer on Statistical Distributions, both published by Wiley.
Table of Contents
1 Alternatives to Black-Scholes Formulation in Finance.
1.2 Motivation for Alternative Models.
1.3 Methods of Valuation.
1.4 Stochastic Interest-Rate Models.
1.5 Stochastic Volatility Models.
1.6 Models with Lévy Processes.
2 Analytical Methods of Risk Management: An Engineering Systems Perspective.
2.2 Risk Management in Engineering Systems.
2.3 Risk Assessment and Analysis.
2.4 Allocating Resources.
3 ARCH and GARCH Models.
3.2 Volatility Clustering.
3.6 Alternative Parameterizations.
3.7 Time-Varying Parameter and Bilinear Models.
3.8 Estimation and Inference.
3.10 Empirical Example.
3.11 Future Developments.
4 Bayesian Forecasting.
4.3 Dynamic Bayesian Models.
4.4 Normal Dynamic Linear Models.
4.5 Component Dynamic Linear Models.
4.8 Monitoring and Adaptation.
4.9 Mixtures of Dynamic Models.
4.10 Non-normal Nonlinear Models.
4.11 Multivariate Models.
4.12 Computation and Simulation.
4.13 Related Areas.
5 Bayesian Networks.
5.1 Examples and Definitions.
5.2 Constructing Bayesian-Network Models.
5.3 Models Specified by Input Lists.
5.4 Graphically Specified Models.
5.5 Conditionally Specified Models.
5.6 Learning Models from Data.
5.7 Propagation in Bayesian Networks.
5.8 Available Software.
6 Box–Jenkins Model.
7 Business Forecasting Methods.
7.2 Trend Curves.
7.3 Exponential Smoothing.
7.4 Exponential Smoothing and Arima Model Building.
7.5 Regression and Econometric Methods.
7.6 Regression and Time-Series Principles.
7.7 Combination of Forecasts.
7.8 Evaluation of Forecasts.
8 Combination of Forecasts.
8.2 The Theory of Combining.
8.3 Estimators of the Weights.
8.4 An Example.
8.5 Further Extensions.
9 Decision Theory.
9.2 Parameters, Decisions, and Consequences.
9.4 Components of a Decision Problem.
9.5 Subjective Probability.
9.6 Decision Analysis.
9.7 Statistical Decision Problems.
9.8 Conjugate Families of Prior Distributions.
9.9 Improper Prior Distributions.
9.10 Estimation and Tests of Hypothesis.
9.11 Sequential Decision Problems.
10 Dynamic Programming.
10.2 Definitions and Examples.
10.3 Some Fundamental Principles.
10.4 The Optimality of Equation and Backward Induction.
10.5 Stationary Plans.
11 Estimation of Travel Distance.
11.2 Distance Functions.
11.3 Goodness-of-Fit Criteria.
11.4 Areas of Future Research.
12 Financial Time Series.
12.1 Asset Price and Return.
12.2 Fundamental and Technical Analyses.
12.3 Volatility Model.
12.4 High-Frequency Data.
12.5 Continuous-Time Model.
13.2 Model Components.
13.3 Model Fitting for Forecasting.
13.4 Forecasting Methods.
13.5 Forecast Quality.
14 Foundations of Risk Measurement.
15 Functional Networks.
15.2 Elements of Functional Networks.
15.3 Differences Between Standard NNs and FNs.
15.4 Development and Implentation of FNs.
15.5 An Example of Application.
16 Game Theory.
16.2 Strategies and Payoffs.
16.3 Applications to Statistics.
17 Intervention Model Analysis.
17.2 Time-Series and Intervention Models.
17.3 Applications and Extensions.
18 Inventory Theory.
18.2 Historical Background.
18.3 Models with Known Demand.
18.4 Models with Uncertain Demand.
19 Manpower Planning.
19.2 Statistical Analysis of Wastage.
19.3 Markov Models for Graded Systems.
19.4 Renewal Models for Graded Systems.
20 Markov Networks.
20.1 Statement of the Problem.
20.2 Some Basic Concepts of Graphs.
20.3 Constructing Markov Network Models.
20.4 Propagation in markov Networks.
20.5 Available Software.
21 Methods of Estimation of Risks and Analysis of Business Processes.
21.2 Mathematical Models of Economic Systems in the Form of the Business Processes Portfolio.
21.3 Risks of Economic Systems.
21.4 Economic Systems Factors Analysis.
22 Mining Functional Data in Prediction Markets.
22.2 Prediction Markets.
22.4 Functional Data Analysis.
23 Models for Bid Arrivals and Bidder Arrivals in Online Auctions.
23.3 Features of Bid Arrivals.
23.4 The BARISTA: A Three-Stage Nohomogeneous Poisson Process.
23.5 Relating Bidder Arrivals and Bid Arrivals.
24 Multiserver Queues.
24.2 Markovian Queues.
24.3 Non-Markovian Queues.
24.4 Other Methods.
25 Multivariate Time-Series Analysis.
25.2 Stationary Mutivariate Time Series and Their Covariance Properties.
25.3 Some Spectral Characteristics for Stationary Vector Processes.
25.4 Linear Filtering Relations for Stationary Vector Processes.
25.5 Linear Model Representations for Stationary Vector Processes.
25.6 Vecotr Autoregressive Moving Average (ARMA) Model Representations.