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Umfang: 1 online resource (xxvii, 815 pages) : , digital, PDF file(s).

ISBN: 9780511754081 (ebook)

Inhalt: This major textbook from a distinguished econometrician is intended for students taking introductory courses in probability theory and statistical inference. No prior knowledge other than a basic familiarity with descriptive statistics is assumed. The primary objective of this book is to establish the framework for the empirical modelling of observational (non-experimental) data. This framework known as 'Probabilistic Reduction' is formulated with a view to accommodating the peculiarities of observational (as opposed to experimental) data in a unifying and logically coherent way. Probability Theory and Statistical Inference differs from traditional textbooks in so far as it emphasizes concepts, ideas, notions and procedures which are appropriate for modelling observational data. Aimed at students at second-year undergraduate level and above studying econometrics and economics, this textbook will also be useful for students in other disciplines which make extensive use of observational data, including finance, biology, sociology and psychology and climatology.

Anmerkung: Title from publisher's bibliographic system (viewed on 05 Oct 2015). , 1. An introduction to empirical modeling -- 2. Probability theory : a modeling framework -- 3. The notion of a probability model -- App. A. Univariate probability models -- 4. The notion of a random sample -- App. B. Bivariate distributions -- 5. Probabilistic concepts and real data -- 6. The notion of non-random sample -- 7. Regression and related notions -- 8. Stochastic processes -- 9. Limit theorems -- 10. From probability theory to statistical inference -- 11. An introduction to statistical inference -- 12. Estimation I: Properties of estimators -- 13. Estimation II: Methods of estimation -- 14. Hypothesis testing -- 15. Misspecification testing.

Weitere Ausg.: Print version: ISBN 9780521413541

Sprache: Englisch

URL: https://doi.org/10.1017/CBO9780511754081

URL: https://doi.org/10.1017/CBO9780511754081

URL: Volltext  (lizenzpflichtig)

UID:

Umfang: XXVII, 815 S. : graph. Darst.

Ausgabe: 1. publ.

ISBN: 0-521-42408-9 , 0-521-41354-0

Sprache: Englisch

Fachgebiete: Wirtschaftswissenschaften

Schlagwort(e): Wahrscheinlichkeitstheorie ; Inferenzstatistik ; Ökonometrie ; Wahrscheinlichkeitstheorie ; Wahrscheinlichkeitstheorie ; Statistische Entscheidungstheorie ; Lehrbuch ; Lehrbuch

UID:

Umfang: 1 online resource (843 pages)

Ausgabe: 1st ed.

ISBN: 9780521413541 , 9780511152139

Inhalt: This major textbook is intended for students taking introductory courses in Probability Theory and Statistical Inference. The text is extremely student-friendly, with pathways designed for semester usage, and although aimed primarily at students of econometrics and economics, will have considerable utility for courses in all disciplines using observational data

Anmerkung: Intro -- Contents -- Preface -- Acknowledgments -- Symbols -- Acronyms -- 1 An introduction to empirical modeling -- 1.1 Introduction -- 1.2 Stochastic phenomena, a preliminary view -- 1.3 Chance regularity and statistical models -- 1.4 Statistical adequacy -- 1.5 Statistical versus theory information* -- 1.6 Observed data -- 1.7 Looking ahead -- 1.8 Exercises -- 2 Probability theory: a modeling framework -- 2.1 Introduction -- 2.2 Simple statistical model: a preliminary view -- 2.3 Probability theory: an introduction -- 2.4 Random experiments -- 2.5 Formalizing condition [a]: the outcomes set -- 2.6 Formalizing condition [b]: events and probabilities -- 2.7 Formalizing condition [c]: random trials -- 2.8 Statistical space -- 2.9 A look forward -- 2.10 Exercises -- 3 The notion of a probability model -- 3.1 Introduction -- 3.2 The notion of a simple random variable -- 3.3 The general notion of a random variable -- 3.4 The cumulative distribution and density functions -- 3.5 From a probability space to a probability model -- 3.6 Parameters and moments -- 3.7 Moments -- 3.8 Inequalities -- 3.9 Summary -- 3.10 Exercises -- Appendix A Univariate probability models -- A.1 Discrete univariate distributions -- A.2 Continuous univariate distributions -- 4 The notion of a random sample -- 4.1 Introduction -- 4.2 Joint distributions -- 4.3 Marginal distributions -- 4.4 Conditional distributions -- 4.5 Independence -- 4.6 Identical distributions -- 4.7 A simple statistical model in empirical modeling: a preliminary view -- 4.8 Ordered random samples* -- 4.9 Summary -- 4.10 Exercises -- Appendix B Bivariate distributions -- B.1 Discrete bivariate distributions -- B.2 Continuous bivariate distributions -- 5 Probabilistic concepts and real data -- 5.1 Introduction -- 5.2 Early developments -- 5.3 Graphical displays: a t-plot , 10.3 Attempts to build a bridge between probability and observed data -- 10.4 Toward a tentative bridge -- 10.5 The probabilistic reduction approach to specification -- 10.6 Parametric versus non-parametric models -- 10.7 Summary and conclusions -- 10.8 Exercises -- 11 An introduction to statistical inference -- 11.1 Introduction -- 11.2 An introduction to the classical approach -- 11.3 The classical versus the Bayesian approach -- 11.4 Experimental versus observational data -- 11.5 Neglected facets of statistical inference -- 11.6 Sampling distributions -- 11.7 Functions of random variables -- 11.8 Computer intensive techniques for approximating sampling distributions* -- 11.9 Exercises -- 12 Estimation I: Properties of estimators -- 12.1 Introduction -- 12.2 Defining an estimator -- 12.3 Finite sample properties -- 12.4 Asymptotic properties -- 12.5 The simple Normal model -- 12.6 Sufficient statistics and optimal estimators* -- 12.7 What comes next? -- 12.8 Exercises -- 13 Estimation II: Methods of estimation -- 13.1 Introduction -- 13.2 Moment matching principle -- 13.3 The least-squares method -- 13.4 The method of moments -- 13.5 The maximum likelihood method -- 13.6 Exercises -- 14 Hypothesis testing -- 14.1 Introduction -- 14.2 Leading up to the Fisher approach -- 14.3 The Neyman-Pearson framework -- 14.4 Asymptotic test procedures* -- 14.5 Fisher versus Neyman-Pearson -- 14.6 Conclusion -- 14.7 Exercises -- 15 Misspecification testing -- 15.1 Introduction -- 15.2 Misspecification testing: formulating the problem -- 15.3 A smorgasbord of misspecification tests -- 15.4 The probabilistic reduction approach and misspecification -- 15.5 Empirical examples -- 15.6 Conclusion -- 15.7 Exercises -- References -- Index , 5.4 Assessing distribution assumptions -- 5.5 Independence and the t-plot -- 5.6 Homogeneity and the t-plot -- 5.7 The empirical cdf and related graphs* -- 5.8 Generating pseudo-random numbers* -- 5.9 Summary -- 5.10 Exercises -- 6 The notion of a non-random sample -- 6.1 Introduction -- 6.2 Non-random sample: a preliminary view -- 6.3 Dependence between two random variables: joint distributions -- 6.4 Dependence between two random variables: moments -- 6.5 Dependence and the measurement system -- 6.6 Joint distributions and dependence -- 6.7 From probabilistic concepts to observed data -- 6.8 What comes next? -- 6.9 Exercises -- 7 Regression and related notions -- 7.1 Introduction -- 7.2 Conditioning and regression -- 7.3 Reduction and stochastic conditioning -- 7.4 Weak exogeneity* -- 7.5 The notion of a statistical generating mechanism (GM) -- 7.6 The biometric tradition in statistics -- 7.7 Summary -- 7.8 Exercises -- 8 Stochastic processes -- 8.1 Introduction -- 8.2 The notion of a stochastic process -- 8.3 Stochastic processes: a preliminary view -- 8.4 Dependence restrictions -- 8.5 Homogeneity restrictions -- 8.6 "Building block" stochastic processes -- 8.7 Markov processes -- 8.8 Random walk processes -- 8.9 Martingale processes -- 8.10 Gaussian processes -- 8.11 Point processes -- 8.12 Exercises -- 9 Limit theorems -- 9.1 Introduction to limit theorems -- 9.2 Tracing the roots of limit theorems -- 9.3 The Weak Law of Large Numbers -- 9.4 The Strong Law of Large Numbers -- 9.5 The Law of Iterated Logarithm* -- 9.6 The Central Limit Theorem -- 9.7 Extending the limit theorems* -- 9.8 Functional Central Limit Theorem* -- 9.9 Modes of convergence -- 9.10 Summary and conclusion -- 9.11 Exercises -- 10 From probability theory to statistical inference* -- 10.1 Introduction -- 10.2 Interpretations of probability

Weitere Ausg.: Print version Spanos, Aris Probability Theory and Statistical Inference Cambridge : Cambridge University Press,c1999 ISBN 9780521413541

Sprache: Englisch

Schlagwort(e): Electronic books

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Umfang: XXVII, 815 S. , graph. Darst.

Ausgabe: Reprinted

ISBN: 0521424089 , 0521413540

Sprache: Englisch

Fachgebiete: Wirtschaftswissenschaften , Mathematik

Schlagwort(e): Wahrscheinlichkeitstheorie ; Statistische Entscheidungstheorie ; Wahrscheinlichkeitstheorie ; Inferenzstatistik ; Ökonometrie ; Wahrscheinlichkeitstheorie ; Lehrbuch

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Umfang: XXVII, 815 S. , graph. Darst.

Ausgabe: 3. printing

ISBN: 0521424089 , 9780521424080 , 0521413540

Sprache: Englisch

Fachgebiete: Wirtschaftswissenschaften , Mathematik

Schlagwort(e): Wahrscheinlichkeitstheorie ; Statistische Entscheidungstheorie ; Wahrscheinlichkeitstheorie ; Inferenzstatistik ; Ökonometrie ; Wahrscheinlichkeitstheorie ; Lehrbuch