Statistics for the 21st Century Methodologies for Applications of the Future

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.

Biography

Gabor Szekely