%{search_type} search results

• 1. Introduction
• 2. Sampling distributions
• 3. Logistic regression
• 4. Log-linear models
• 5. Coordinate-free models
• 6. Additional topics
• Appendix A: Power for chi-squared tests
• Appendix B: Program for exact tests
• Appendix C: The hypergeometric distribution
• References
• Selected solutions and hints
• Index
• Examples index
• Author index
• Subject index.
• (source: Nielsen Book Data)

Discrete or count data arise in experiments where the outcome variables are the numbers of individuals classified into unique, non-overlapping categories. This revised edition describes the statistical models used in the analysis and summary of such data, and provides a sound introduction to the subject for graduate students and practitioners needing a review of the methodology. With many numerical examples throughout, it includes topics not covered in depth elsewhere, such as the negative multinomial distribution; the many forms of the hypergeometric distribution; and coordinate free models. A detailed treatment of sample size estimation and power are given in terms of both exact inference and asymptotic, non-central chi-squared methods. A new section covering Poisson regression has also been included. An important feature of this book, missing elsewhere, is the integration of the software into the text. Many more exercises are provided (including 84% more applied exercises) than in the previous edition, helping consolidate the reader's understanding of all subjects covered, and making the book highly suitable for use in a classroom setting. Several new datasets, mostly from the health and medical sector, are discussed, including previously unpublished data from a study of Tourette's Syndrome in children.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Sampling distributions
• 3. Logistic regression
• 4. Log-linear models
• 5. Coordinate-free models
• 6. Additional topics
• Appendix A: Power for chi-squared tests
• Appendix B: Program for exact tests
• Appendix C: The hypergeometric distribution
• References
• Selected solutions and hints
• Index
• Examples index
• Author index
• Subject index.
• (source: Nielsen Book Data)

Discrete or count data arise in experiments where the outcome variables are the numbers of individuals classified into unique, non-overlapping categories. This revised edition describes the statistical models used in the analysis and summary of such data, and provides a sound introduction to the subject for graduate students and practitioners needing a review of the methodology. With many numerical examples throughout, it includes topics not covered in depth elsewhere, such as the negative multinomial distribution; the many forms of the hypergeometric distribution; and coordinate free models. A detailed treatment of sample size estimation and power are given in terms of both exact inference and asymptotic, non-central chi-squared methods. A new section covering Poisson regression has also been included. An important feature of this book, missing elsewhere, is the integration of the software into the text. Many more exercises are provided (including 84% more applied exercises) than in the previous edition, helping consolidate the reader's understanding of all subjects covered, and making the book highly suitable for use in a classroom setting. Several new datasets, mostly from the health and medical sector, are discussed, including previously unpublished data from a study of Tourette's Syndrome in children.
(source: Nielsen Book Data)

• Part I. Subdivision
• Subdivision of box-splines / Malcolm Sabin
• Interpolatory subdivision schemes ; Analysis of convergence and smoothness by the formalism of Laurent polynomials / Nira Dyn
• Eigenanalysis and artifacts of subdivision curves and surfaces / Malcolm Sabin
• Nonlinear subdivision schemes: applications to image processing / Albert Cohen, Basarab Matei
• Part II. Wavelets
• Nonuniform B-splines and B-wavelets / Ewald Quak
• BLaC wavelets and non-nested wavelets / Georges-Pierre Bonneau
• Multiresolution on the sphere / Matthias Conrad, Jürgen Prestin
• Part III. Scattered data modellinhg
• Scattered data modelling using radial basis functions / Armin Iske
• Scattered data fitting with bivariate splines / Frank Zeilfelder
• Parameterization of triangulations and unorganized points / Michael Floater, Kai Hormann
• Part IV. Coding and data structures
• Simplification and compression of 3D meshes / Craig Gotsman, Stefan Gumhold, Leif Kobbelt
• Multiresolution mesh representation: models and data structures / Leila De Floriani, Paola Magillo.

Multiresolution methods in geometric modelling are concerned with the generation, representation, and manipulation of geometric objects at several levels of detail. Applications include fast visualization and rendering as well as coding, compression and digital transmission of 3D geometric objects.This book is based on thirteen tutorials presented during the European Summer School "Principles of Multiresolution in Geometric Modelling", held at the Munich University of Technology, Germany, during August 22-30, 2001. The book covers: subdivision; wavelets; scattered data modelling; and coding and data structures.The tutorials are designed to be introductory in character, and include supporting exercises. Other supplementary material and software can be downloaded from the Web Site www ma.tum.de/primus 2001/.
(source: Nielsen Book Data)

• Physics
• 1. Quantum Probability and the Interpretation of Quantum Mechanics: A Crucial Experiment
• 2. An Optical Geometric Model of the Betatronic Motion
• 3. Some Remarks on the Theory of Relativity and the Naïve Realism
• 4. Pattern Induced by Parameter Modulation in Spatiotemporal Chaos
• 5. Chaos and Orbit Complexity
• 6. On the Riemann-Mangoldt Constant
• 7. Long-Term Stability in Circular Accelerators
• 8. Mathematical Models in Beam Dynamics
• Biology
• 9. An Axiomatic Approach to Some Biological Themes
• 10. For an Axiomatic Theory of the Evolutionary Darwinian Ideas: A Proposal
• 11. Fractal Complexity of Membrane Structures in Normal and Neoplastic Cells During Growth and Apoptotic Cell Death
• 12. The Arc, an Unexpected and Still Not Explained Element of the Tracks of Creeping Ciliates
• 13. Natural Population and Community Structure and Dynamics: The "Supply-Side Ecology", Theory and the Field Data
• History of science
• 14. The History of Theoretical Population Ecology: Which Role for Mathematical Modeling?
• 15. The Mathematics Implied in the Laws of Nature and Realism, or the Role of Functions Around 1750
• 16. Geometry, the Calculus and the Use of Limits in Newton's Principia
• 17. The Two Faces of Mathematical Modeling: Objectivism vs. Subjectivism, Simplicity vs. Complexity
• 18. The Search for the Mathematization of the Social Disciplines
• 19. Mathematization of the Science of Motion and the Birth of Analytical Mechanics: A Historiographical Note
• 20. Models, Analogies, and Statistical Reason, 1760
• 1900
• 21. Is Music Relevant for the History of Science?

The increasing use of mathematical models in the sciences, primarily in biology, chemistry, geology, and physics, generates much interest and attention. This book brings together evolutionary and population biologists, physicists, mathematicians, and historians of science and allows them to discuss how, in an increasingly interdisciplinary manner, mathematics and mathematical models are used today in the natural sciences.

• 1 Advertising and Competition.- 2 Analytical Models and Strategy Concepts.- 3 Analysis of a Lanchester Duopoly.- 4 Analysis of a Vidale-Wolfe Duopoly.- 5 Analysis of a Diffusion Duopoly.- 6 Summary and Final Considerations.- References.- Author Index.
• (source: Nielsen Book Data)

For years, I have been impressed by how dynamic markets are. Marketing strategies are rarely successful without adjustments over time, and marketing managers need constantly to monitor, and anticipate when possible, important changes in the marketing environment, so that they can adapt their marketing strategies to changing market conditions. It strikes me as important that the dynamic elements of marketing be modeled and studied, and a significant part of my research activity has been dedicated to exploring the nature and implications of dynamic marketing strategies. The marketing field has used various models and methodologies in the attempt to understand dynamic markets. I must thank my dissertation advisor, Dave Montgomery at Stanford, for originally turning my attention toward time-varying parameter models some 14 years ago. From that plat- form, I have proceeded to search for models that capture the essence of dynamic marketing, as well as for methodological tools, empirical as well as analytical, that allow insightful study of such models.
(source: Nielsen Book Data)

• 1. Introduction
• 2. Linear Quadratic Approximations: An Introduction
• 3. A Toolkit for Analyzing Nonlinear Dynamic Stochastic Models Easily
• 4. Solving Nonlinear Rational Expectations Models by Eigenvalue-Eigenvector Decompositions
• Part II. Non-Linear Methods
• 6. Application of Weighted Residual Methods to Dynamic Economic Models
• 7. The Parametrized Expectations Approach: Some Practical Issues
• 8. Finite-Difference Methods for Continuous-Time Dynamic Programming
• Part III. Solving some dynamic economies
• 10. Computing Models of Social Security
• 11. Computation of Equilibria in Heterogenous Agent Economies.
• (source: Nielsen Book Data)

Macroeconomics increasingly uses stochastic dynamic general equilibrium models to understand theoretical and policy issues. Unless very strong assumptions are made, understanding the properties of particular models requires solving the model using a computer. This volume brings together leading contributors in the field who explain in detail how to implement the computational techniques needed to solve dynamic economics models. A broad spread of techniques are covered, and their application in a wide range of subjects discussed. The book provides the basics of a toolkit which researchers and graduate students can use to solve and analyse their own theoretical models.
(source: Nielsen Book Data)

• Series Editor's Introduction Acknowledgments 1. Dynamic Models and Social Change Theoretical Reasons for Using Differential Equations in the Social Sciences An Example The Use of Differential Equations in the Natural and Physical Sciences Deterministic Versus Probabilistic Differential Equation Models What Is a Differential Equation? What This Book Is and Is Not 2. First-Order Differential Equations Analytical Solutions to Linear First-Order Differential Equations Solving First-Order Differential Equations Using Separation of Variables An Example From Sociology Numerical Methods Used to Solve Differential Equations Summary
• Chapter 2
• Appendix 3. Systems of First-Order Differential Equations The Predator-Prey Model The Phase Diagram Vector Field and Direction Field Diagrams The Equilibrium Marsh and Flow Diagrams Summary
• Chapter 3
• Appendix 4. Some Classic Social Science Examples of First-Order Systems Richardson's Arms Race Model Lanchester's Combat Model Rapoport's Production and Exchange Model Summary 5. Transforming Second-Order and Nonautonomous Differential Equations Into Systems of First-Order Differential Equations Second- and Higher-Order Differential Equations Nonautonomous Differential Equations Summary 6. Stability Analyses of Linear Differential Equation Systems A Motivating Example of How Stability Can Dramatically Change in One System Scalar Methods Matrix Methods Equilibrium Categories Summarizing the Stability Criteria 7. Stability Analyses of Nonlinear Differential Equation Systems The Jacobian Summary 8. Frontiers of Exploration References Index About the Author.
• (source: Nielsen Book Data)

Differential Equations: A Modeling Approach introduces differential equations and differential equation modeling to students and researchers in the social sciences. Key Features: - The text is accessibly written, so that students with minimal mathematical training can understand all of the basic concepts and techniques presented. - The author uses social sciences examples to illustrate the relevance of differential equation modeling to readers. - Readers can use graphical methods to produce penetrating analysis of differential equation systems. - Linear and nonlinear model specifications are explained from a social science perspective. Most interesting differential equation models are nonlinear, and readers need to know how to specify and work with such models in the social sciences.
(source: Nielsen Book Data)

• Preface. Univariate Stochastic Orders Theory of Integral Stochastic Orders Multivariate Stochastic Orders Stochastic Models, Comparison and Monotonicity Monotonicity and Comparability of Stochastic Processes Monotonicity Properties and Bounds for Queueing Systems Applications to Various Stochastic Models Comparing Risks. List of Symbols. References. Index.
• (source: Nielsen Book Data)

Stochastic order relations prprovide a valuable insight into the behaviour of complex stochastic (random) systems and enable the user to collect meaningful comparative data. Application areas include queueing systems, actuarial and financial risk, decision making and stochastic simulation. Applicable to a broad range of scientific disciplines, including economics, finance, insurance and operations research Provides coverage of the latest research and applications An essential resource for researchers and postgraduate students appliying stochastic order relations, and scientisits from applied statistics, operations research, economics and finance.
(source: Nielsen Book Data)

• Preface ix
• Chapter 1: Introduction 1 Notes 12 Part I: Mathematical Preliminaries 15
• Chapter 2: Stochastic Processes, Brownian Motions, and Diffusions 17 2.1. Random Variables and Stochastic Processes 17 2.2. Independence 18 2.3. Wiener Processes and Brownian Motions 19 2.4. Random Walk Approximation of a Brownian Motion 20 2.5. Stopping Times 24 2.6. Strong Markov Property 24 2.7. Diffusions 25 2.8. Discrete Approximation of an Ornstein-Uhlenbeck Process 27 Notes 28
• Chapter 3: Stochastic Integrals and Ito's Lemma 30 3.1. The Hamilton-Jacobi-Bellman Equation 31 3.2. Stochastic Integrals 34 3.3. Ito's Lemma 37 3.4. Geometric Brownian Motion 38 3.5. Occupancy Measure and Local Time 41 3.6. Tanaka's Formula 43 3.7. The Kolmogorov Backward Equation 47 3.8. The Kolmogorov Forward Equation 50 Notes 51
• Chapter 4: Martingales 53 4.1. Definition and Examples 53 4.2. Martingales Based on Eigenvalues 57 4.3. The Wald Martingale 58 4.4. Sub- and Supermartingales 60 4.5. Optional Stopping Theorem 63 4.6. Optional Stopping Theorem, Extended 67 4.7. Martingale Convergence Theorem 70 Notes 74
• Chapter 5: Useful Formulas for Brownian Motions 75 5.1. Stopping Times Defined by Thresholds 78 5.2. Expected Values for Wald Martingales 79 5.3. The Functions psi and PSI 82 5.4. ODEs for Brownian Motions 87 5.5. Solutions for Brownian Motions When r = 0 88 5.6. Solutions for Brownian Motions When r > 0 93 5.7. ODEs for Diffusions 98 5.8. Solutions for Diffusions When r = 0 98 5.9. Solutions for Diffusions When r > 0 102 Notes 106 Part II: Impulse Control Models 107
• Chapter 6: Exercising an Option 109 6.1. The Deterministic Problem 110 6.2. The Stochastic Problem: A Direct Approach 116 6.3. Using the Hamilton-Jacobi-Bellman Equation 119 6.4. An Example 125 Notes 128
• Chapter 7: Models with Fixed Costs 129 7.1. A Menu Cost Model 130 7.2. Preliminary Results 133 7.3. Optimizing: A Direct Approach 136 7.4. Using the Hamilton-Jacobi-Bellman Equation 140 7.5. Random Opportunities for Costless Adjustment 145 7.6. An Example 146 Notes 152
• Chapter 8: Models with Fixed and Variable Costs 153 8.1. An Inventory Model 154 8.2. Preliminary Results 157 8.3. Optimizing: A Direct Approach 160 8.4. Using the Hamilton-Jacobi-Bellman Equation 162 8.5. Long-Run Averages 164 8.6. Examples 166 8.7. Strictly Convex Adjustment Costs 174 Notes 175
• Chapter 9: Models with Continuous Control Variables 176 9.1. Housing and Portfolio Choice with No Transaction Cost 178 9.2. The Model with Transaction Costs 182 9.3. Using the Hamilton-Jacobi-Bellman Equation 184 9.4. Extensions 191 Notes 196 Part III: Instantaneous Control Models 197
• Chapter 10: Regulated Brownian Motion 199 10.1. One- and Two-Sided Regulators 201 10.2. Discounted Values 205 10.3. The Stationary Distribution 212 10.4. An Inventory Example 218 Notes 224
• Chapter 11: Investment: Linear and Convex Adjustment Costs 225 11.1. Investment with Linear Costs 227 11.2. Investment with Convex Adjustment Costs 232 11.3. Some Special Cases 236 11.4. Irreversible Investment 239 11.5. Irreversible Investment with Two Shocks 243 11.6. A Two-Sector Economy 247 Notes 248 Part IV: Aggregation 251
• Chapter 12: An Aggregate Model with Fixed Costs 253 12.1. The Economic Environment 256 12.2. An Economy with Monetary Neutrality 259 12.3. An Economy with a Phillips Curve 261 12.4. Optimizing Behavior and the Phillips Curve 265 12.5. Motivating the Loss Function 278 Notes 280 A Continuous Stochastic Processes 283 A.1. Modes of Convergence 283 A.2. Continuous Stochastic Processes 285 A.3. Wiener Measure 287 A.4. Nondifferentiability of Sample Paths 288 Notes 289 B Optional Stopping Theorem 290 B.1. Stopping with a Uniform Bound, T <= N 290 B.2. Stopping with Pr {T < } = 1 292 Notes 294 References 295 Part Index 303.
• (source: Nielsen Book Data)

In economic situations where action entails a fixed cost, inaction is the norm. Action is taken infrequently, and adjustments are large when they occur. Interest in economic models that exhibit "lumpy" behavior of this kind has exploded in recent years, spurred by growing evidence that it is typical in many important economic decisions, including price setting, investment, hiring, durable goods purchases, and portfolio management. In The Economics of Inaction, leading economist Nancy Stokey shows how the tools of stochastic control can be applied to dynamic problems of decision making under uncertainty when fixed costs are present. Stokey provides a self-contained, rigorous, and clear treatment of two types of models, impulse and instantaneous control. She presents the relevant results about Brownian motion and other diffusion processes, develops methods for analyzing each type of problem, and discusses applications to price setting, investment, and durable goods purchases. This authoritative book will be essential reading for graduate students and researchers in macroeconomics.
(source: Nielsen Book Data)

• pt. I. Continuous and Discrete Modelling. Ch. 1. Formulation of Mathematical Models. Ch. 2. Compartment Problems. Ch. 3. Models in Dynamics and Vibration. Ch. 4. Discrete Models. Ch. 5. Numerical Techniques for Model Nonlinear PDE's
• pt. II. Case Studies. Case Study A. The Design of Biotreatment Systems. Case Study B. Dynamics of Contagious Disease. Case Study C. Numerical Cam Design. Case Study D. Dynamics and Force Analysis of a Mechanism.

Over the past decade there has been an increasing demand for suitable material in the area of mathematical modelling as applied to science and engineering. There has been a constant movement in the emphasis from developing proficiency in purely mathematical techniques to an approach which caters for industrial and scientific applications in emerging new technologies. In this textbook we have attempted to present the important fundamental concepts of mathematical modelling and to demonstrate their use in solving certain scientific and engineering problems. This text, which serves as a general introduction to the area of mathematical modelling, is aimed at advanced undergraduate students in mathematics or closely related disciplines, e.g., students who have some prerequisite knowledge such as one-variable calculus, linear algebra and ordinary differential equations. Some prior knowledge of computer programming would be useful but is not considered essential. The text also contains some more challenging material which could prove attractive to graduate students in engineering or science who are involved in mathematical modelling. In preparing the text we have tried to use our experience of teaching mathematical modelling to undergraduate students in a wide range of areas including mathematics and computer science and disciplines in engineering and science. An important aspect of the text is the use made of scientific computer software packages such as MAPLE for symbolic algebraic manipulations and MA TLAB for numerical simulation.
(source: Nielsen Book Data)

• Tomáš Havránek
• Bernd Streitberg
• Change Point Problem
• Robust Estimation in Linear Model and its Computational Aspects
• Constructing Prediction Trees from Data: The RECPAM Approach
• Stochastic Approximation and Optimization
• Comparison of the Stochastic Approximation Software
• Models, Algorithms and Software of Stochastic Optimization
• Some Computational Aspects of Graphical Model Selection
• Multiple Hypotheses Testing
• Statistical Applications of Artificial Intelligence.

This volume contains complete texts of the lectures held during the Summer School on "Computational Aspects of Model Choice", organized jointly by International Association for Statistical Computing and Charles University, Prague, on July 1 - 14, 1991, in Prague. Main aims of the Summer School were to review and analyse some of the recent developments concerning computational aspects of the model choice as well as their theoretical background. The topics cover the problems of change point detection, robust estimating and its computational aspecets, classification using binary trees, stochastic approximation and optimizationincluding the discussion about available software, computational aspectsof graphical model selection and multiple hypotheses testing. The bridge between these different approaches is formed by the survey paper about statistical applications of artificial intelligence

• A dynamic model of multidimensional collective choice / David P. Baron and Michael C. Herron
• Fluidity of social norms in a hierarchical system / Joseph E. Harrington, Jr.
• Learning, central bank independence, and the politics of excess foreign returns / Jude C. Hays and Diana Richards
• A computational theory of electoral competition / John E. Jackson
• A computational model of voter sophistication, ideology, and candidate position taking / Scott de Marchi
• The other side of the coin: a computational analysis of publicly financed campaigns / Michael Bailey
• Political institutions and sorting in a Tiebout model / Ken Kollman, John H. Miller, and Scott E. Page
• Recovering behavioralism: adaptively rational strategic behavior with endogenous aspirations / Jonathan Bendor, Daniel Diermeier, and Michael Ting.

The use of innovative computational models in political economic research as a complement to traditional analytical methodologies. Researchers are increasingly turning to computational methods to study the dynamic properties of political and economic systems. Politicians, citizens, interest groups, and organizations interact in dynamic, complex environments, and the static models that are predominant in political economy are limited in capturing fundamental features of economic decision making in modern democracies. Computational models-numerical approximations of equilibria and dynamics that cannot be solved analytically-provide useful insight into the behavior of economic agents and the aggregate properties of political systems. They serve as a valuable complement to existing mathematical tools.This book offers some of the latest research on computational political economy. The focus is on theoretical models of traditional problems in the field. Each chapter presents an innovative model of interaction between economic agents. Topics include voting behavior, candidate position taking, special interest group contributions, macroeconomic policy making, and corporate decision making.
(source: Nielsen Book Data)

• Dedication; Contents; Preface;
• Chapter 1 The Fisher Model with Certainty;
• Chapter 2 The Fisher Model;
• Chapter 3 Financial Values;
• Chapter 4 Fisher Separation;
• Chapter 5 More Values;
• Chapter 6 Corporate Finance Theorems;
• Chapter 7 Agency Problems;
• Chapter 8 Information Problems: Hidden Knowledge;
• Chapter 9 Corporate Risk Management;
• Chapter 10 Concluding Remarks; Bibliography; Index.

This monograph represents a unified coherent perspective of financial markets and the theory of corporate finance. The Fisher model is used in corporate finance texts to note the foundations of the net present value rule, but has not been developed further in textbooks as a perspective for students of the finance discipline. This book articulates corporate finance from a common perspective and model: by generalizing the Fisher model to include risks, it is possible to exposit and prove the classic corporate finance theorems and to establish a common foundation for the discipline. The classic t.