Statistics for the 21st Century (eBook, PDF)

Methodologies for Applications of the Future
Redaktion: Szekely, Gabor

• Format: PDF

Produktbeschreibung

A selection of articles presented at the Eighth Lukacs Symposium held at the Bowling Green State University, Ohio. They discuss consistency and accuracy of the sequential bootstrap, hypothesis testing, geometry in multivariate analysis, the classical extreme value model, the analysis of cross-classified data, diffusion models for neural activity, e

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Produktdetails

• Verlag: Taylor & Francis eBooks
• Seitenzahl: 500
• Erscheinungstermin: 25. Januar 2000
• Englisch
• ISBN-13: 9781482277494
• Artikelnr.: 57531167

Autorenporträt

Gabor Szekely

Inhaltsangabe

Challenges for categorical data analysis in the 21st century
consistency and accuracy of the sequential bootstrap
hypothesis testing in the 20th century with special reference to testing with misspecified models
the importance of geometry in multivariate analysis and some applications
R.A. Fisher in the 21st century
students can help improve college teaching - a review and an agenda for the statistics
the classical extreme value model - mathematical results versus statistical inference
the analysis of cross-classified data - notes on a century of progress in contingency data table analysis, and some comments on its prehistory and its future
diffusion models for neural activity
estimation with quadratic loss
econometrics in the 21st century
on stability of large (ecological) systems
multi-scale statistical approach to critical-area analysis and modelling of watersheds and landscapes
R.A. Fisher - the founder of modern statistics
higher order asymptotics - costs and benefits
the analysis of subject-specific agreement
some important classes of problems in statistical experimental design, multivariate analysis, and sampling theory
pre-limit and post-limit theorems for statistics
regression modelling with fixed effects - missing values and related problems
some recent developments in nonparametric inference for right censored and randomly truncated data
estimation of parameters in nonlinear regression models.