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This book was first published in 2004. Many observed phenomena, from the changing health of a patient to values on the stock market, are characterised by quantities that vary over time: stochastic processes are designed to study them. This book introduces practical methods of applying stochastic processes to an audience knowledgeable only in basic statistics. It covers almost all aspects of the subject and presents the theory in an easily accessible form that is highlighted by application to many examples. These examples arise from dozens of areas, from sociology through medicine to engineering. Complementing these are exercise sets making the book suited for introductory courses in stochastic processes. Software (available from www.cambridge.org) is provided for the freely available R system for the reader to apply to all the models presented.
Mathematical statistics --- Stochastic processes --- Processus stochastiques --- #SBIB:303H510 --- #SBIB:303H61 --- 519.23 --- Methoden sociale wetenschappen: statistische technieken, algemeen --- Wiskundige methoden en technieken --- Random processes --- Probabilities --- Stochastic processes. --- Probabilities. --- Probability --- Statistical inference --- Combinations --- Mathematics --- Chance --- Least squares --- Risk
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Lindsey presents a complete range of material on this subject which can be immediately used with the major statistical packages. The important distinctions between independent and dependent events - frequencies and counts - structures the book. Many of the important areas of modern statistics from generalized linear models to survival analysis are demonstrated to be special cases of categorical data analysis.
Multivariate analysis. --- Multivariate distributions --- Multivariate statistical analysis --- Statistical analysis, Multivariate --- Analysis of variance --- Mathematical statistics --- Matrices --- Multivariate analysis --- Mathematical statistics. --- Statistique mathématique. --- Analyse multivariée.
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