Writings in Probability, Statistics and Economics Vol 3
Author: F.Y. Edgeworth
Publisher:
Published: 1996
Total Pages:
ISBN-13:
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Author: F.Y. Edgeworth
Publisher:
Published: 1996
Total Pages:
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DOWNLOAD EBOOKAuthor: Frank H. Knight
Publisher: Cosimo, Inc.
Published: 2006-11-01
Total Pages: 401
ISBN-13: 1602060053
DOWNLOAD EBOOKA timeless classic of economic theory that remains fascinating and pertinent today, this is Frank Knight's famous explanation of why perfect competition cannot eliminate profits, the important differences between "risk" and "uncertainty," and the vital role of the entrepreneur in profitmaking. Based on Knight's PhD dissertation, this 1921 work, balancing theory with fact to come to stunning insights, is a distinct pleasure to read. FRANK H. KNIGHT (1885-1972) is considered by some the greatest American scholar of economics of the 20th century. An economics professor at the University of Chicago from 1927 until 1955, he was one of the founders of the Chicago school of economics, which influenced Milton Friedman and George Stigler.
Author: Francis Ysidro Edgeworth
Publisher:
Published: 1996
Total Pages: 568
ISBN-13:
DOWNLOAD EBOOKAuthor: Francis Ysidro Edgeworth
Publisher: Edward Elgar Publishing
Published: 1996
Total Pages: 488
ISBN-13:
DOWNLOAD EBOOKA compilation of Edgeworth's published articles in probability theory and mathematical statistics, highlighting the evolution of the economist's theories beginning in 1925. Credited with being the nominal inventor of the Edgeworth Box diagram and the contract curve, this three volume set shows to an advantage his work in probability, the law of error, and the applications of probability and statistical theory to economics and the social sciences. The articles are presented as "photocopies" of the originals, including accompanying illustrations. Annotation copyright by Book News, Inc., Portland, OR
Author: Walter P. Heller
Publisher: Cambridge University Press
Published: 1986-09-26
Total Pages: 316
ISBN-13: 9780521327046
DOWNLOAD EBOOKThe third in a series of volumes published in honour of Professor Kenneth J. Arrow, each covering a different area of economic theory.
Author: Michael J. Evans
Publisher: Macmillan
Published: 2004
Total Pages: 704
ISBN-13: 9780716747420
DOWNLOAD EBOOKUnlike traditional introductory math/stat textbooks, Probability and Statistics: The Science of Uncertainty brings a modern flavor based on incorporating the computer to the course and an integrated approach to inference. From the start the book integrates simulations into its theoretical coverage, and emphasizes the use of computer-powered computation throughout.* Math and science majors with just one year of calculus can use this text and experience a refreshing blend of applications and theory that goes beyond merely mastering the technicalities. They'll get a thorough grounding in probability theory, and go beyond that to the theory of statistical inference and its applications. An integrated approach to inference is presented that includes the frequency approach as well as Bayesian methodology. Bayesian inference is developed as a logical extension of likelihood methods. A separate chapter is devoted to the important topic of model checking and this is applied in the context of the standard applied statistical techniques. Examples of data analyses using real-world data are presented throughout the text. A final chapter introduces a number of the most important stochastic process models using elementary methods. *Note: An appendix in the book contains Minitab code for more involved computations. The code can be used by students as templates for their own calculations. If a software package like Minitab is used with the course then no programming is required by the students.
Author: Bruce Hansen
Publisher: Princeton University Press
Published: 2022-06-28
Total Pages: 417
ISBN-13: 0691236143
DOWNLOAD EBOOKA comprehensive and up-to-date introduction to the mathematics that all economics students need to know Probability theory is the quantitative language used to handle uncertainty and is the foundation of modern statistics. Probability and Statistics for Economists provides graduate and PhD students with an essential introduction to mathematical probability and statistical theory, which are the basis of the methods used in econometrics. This incisive textbook teaches fundamental concepts, emphasizes modern, real-world applications, and gives students an intuitive understanding of the mathematics that every economist needs to know. Covers probability and statistics with mathematical rigor while emphasizing intuitive explanations that are accessible to economics students of all backgrounds Discusses random variables, parametric and multivariate distributions, sampling, the law of large numbers, central limit theory, maximum likelihood estimation, numerical optimization, hypothesis testing, and more Features hundreds of exercises that enable students to learn by doing Includes an in-depth appendix summarizing important mathematical results as well as a wealth of real-world examples Can serve as a core textbook for a first-semester PhD course in econometrics and as a companion book to Bruce E. Hansen’s Econometrics Also an invaluable reference for researchers and practitioners
Author: C. Jotin Khisty
Publisher: J. Ross Publishing
Published: 2012
Total Pages: 625
ISBN-13: 1604270551
DOWNLOAD EBOOKThis title offers an overview of the fundamentals and practice applications of probability and statistics, microeconomics, engineering economics, hard and soft systems analysis, and sustainable development and sustainability applications in engineering planning.
Author:
Publisher: John Wiley & Sons
Published: 2005-12-16
Total Pages: 706
ISBN-13: 0471743844
DOWNLOAD EBOOKENCYCLOPEDIA OF STATISTICAL SCIENCES
Author: Krishna B. Athreya
Publisher: Springer Science & Business Media
Published: 2006-07-27
Total Pages: 625
ISBN-13: 038732903X
DOWNLOAD EBOOKThis is a graduate level textbook on measure theory and probability theory. The book can be used as a text for a two semester sequence of courses in measure theory and probability theory, with an option to include supplemental material on stochastic processes and special topics. It is intended primarily for first year Ph.D. students in mathematics and statistics although mathematically advanced students from engineering and economics would also find the book useful. Prerequisites are kept to the minimal level of an understanding of basic real analysis concepts such as limits, continuity, differentiability, Riemann integration, and convergence of sequences and series. A review of this material is included in the appendix. The book starts with an informal introduction that provides some heuristics into the abstract concepts of measure and integration theory, which are then rigorously developed. The first part of the book can be used for a standard real analysis course for both mathematics and statistics Ph.D. students as it provides full coverage of topics such as the construction of Lebesgue-Stieltjes measures on real line and Euclidean spaces, the basic convergence theorems, L^p spaces, signed measures, Radon-Nikodym theorem, Lebesgue's decomposition theorem and the fundamental theorem of Lebesgue integration on R, product spaces and product measures, and Fubini-Tonelli theorems. It also provides an elementary introduction to Banach and Hilbert spaces, convolutions, Fourier series and Fourier and Plancherel transforms. Thus part I would be particularly useful for students in a typical Statistics Ph.D. program if a separate course on real analysis is not a standard requirement. Part II (chapters 6-13) provides full coverage of standard graduate level probability theory. It starts with Kolmogorov's probability model and Kolmogorov's existence theorem. It then treats thoroughly the laws of large numbers including renewal theory and ergodic theorems with applications and then weak convergence of probability distributions, characteristic functions, the Levy-Cramer continuity theorem and the central limit theorem as well as stable laws. It ends with conditional expectations and conditional probability, and an introduction to the theory of discrete time martingales. Part III (chapters 14-18) provides a modest coverage of discrete time Markov chains with countable and general state spaces, MCMC, continuous time discrete space jump Markov processes, Brownian motion, mixing sequences, bootstrap methods, and branching processes. It could be used for a topics/seminar course or as an introduction to stochastic processes. Krishna B. Athreya is a professor at the departments of mathematics and statistics and a Distinguished Professor in the College of Liberal Arts and Sciences at the Iowa State University. He has been a faculty member at University of Wisconsin, Madison; Indian Institute of Science, Bangalore; Cornell University; and has held visiting appointments in Scandinavia and Australia. He is a fellow of the Institute of Mathematical Statistics USA; a fellow of the Indian Academy of Sciences, Bangalore; an elected member of the International Statistical Institute; and serves on the editorial board of several journals in probability and statistics. Soumendra N. Lahiri is a professor at the department of statistics at the Iowa State University. He is a fellow of the Institute of Mathematical Statistics, a fellow of the American Statistical Association, and an elected member of the International Statistical Institute.