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Secure Connected Objects

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Banach, Fréchet, Hilbert and Neumann Spaces

Analysis for PDEs Set – Volume 1

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Semi-Markov Migration Models for Credit Risk

Stochastic Models for Insurance Set – Volume 1

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Human Exposure to Electromagnetic Fields

From Extremely Low Frequency (ELF) to Radio Frequency

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Enterprise Interoperability


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Data Treatment in Environmental Sciences

Multivaried Approach

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From Pinch Methodology to Pinch-Exergy Integration of Flexible Systems

Thermodynamics – Energy, Environment, Economy Set

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Exterior Algebras

Elementary Tribute to Grassmann's Ideas

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Nonlinear Theory of Elastic Plates

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Cognitive Approach to Natural Language Processing

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Mathematical Statistics and Stochastic Processes

Denis Bosq, UPMC, Paris, France

ISBN: 9781848213616

Publication Date: April 2012   Hardback   304 pp.

125.00 USD

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Generally, books on mathematical statistics are restricted to the case of independent identically distributed random variables. In this book however, both this case AND the case of dependent variables, i.e. statistics for discrete and continuous time processes, are studied. This second case is very important for today’s practitioners. Mathematical Statistics and Stochastic Processes is based on decision theory and asymptotic statistics and contains up-to-date information on the relevant topics of theory of probability, estimation, confidence intervals, non-parametric statistics and robustness, second-order processes in discrete and continuous time and diffusion processes, statistics for discrete and continuous time processes, statistical prediction, and complements in probability. This book is aimed at students studying courses on probability with an emphasis on measure theory and for all practitioners who apply and use statistics and probability on a daily basis.


Part 1. Mathematical Statistics
1. Introduction to Mathematical Statistics.
2. Principles of Decision Theory.
3. Conditional Expectation.
4. Statistics and Sufficiency.
5. Point Estimation.
6. Hypothesis Testing and Confidence Regions.
7. Asymptotic Statistics.
8. Non-Parametric Methods and Robustness.
Part 2. Statistics for Stochastic Processes
9. Introduction to Statistics for Stochastic Processes.
10. Weakly Stationary Discrete-Time Processes.
11. Poisson Processes – A Probabilistic and Statistical Study.
12. Square-Integrable Continuous-Time Processes.
13. Stochastic Integration and Diffusion Processes.
14. ARMA Processes.
15. Prediction.
Part 3. Supplement
16. Elements of Probability Theory.

About the Authors

Denis Bosq is Emeritus Professor at the Laboratory of Statistics, Theory and Application (LSTA) at UPMC in Paris. He is the author of many books and papers on statistics, theory and probability.


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