Abstract

Part 1 Statistical theory - theoretical concepts significance tests theory of optimal tests estimation asymptotic theory. Part 2 Applied statistics - preliminary considerations statistical models statistical inference model adequancy exploratory and robust methods multi-variate methods use of computers. Part 3 Generalized linear models - a class of non-linear regression models likelihood functions estimation analysis of deviance checking the model. Part 4 Residuals and diagnostics - general remarks normal theory linear model a general definition general regression models. Part 5 Life table analysis - survival distributions inference for a single sample dependence on explanatory variables inference for models involving explanatory variables graphical methods - goodness of fit several types of failure: competing risks multi-variate failure distributions. Part 6 Sequential methods - Wald's theory of the SPRT sequential tests with nuisance parameters sequential estimation sequential clinical trials decision theory and optimality in clinical trials inference and decisions sequential design and related topics. Part 7 Time series methods - stationary models frequency domain parametric models non-parametric estimation regression with correlated errors. Part 8 Modelling stochastic phenomena - independence and the Markov property semi-Markov and Markov renewal processes point processes spatial processes applications. Part 9 Optimal design of experiments - convex design theory numerical methods some specific designs for standard problems non-standard problems. Part 10 Likelihood theory - the primary theory some refinements pseudo-likelihoods. Part 11 Quasi-likelihood and estimating functions - least squares quasi-likelihood estimation quasi-likelihood function estimating functions confidence sets an open problem. Part 12 Approximations and asymptotics - basic theory and notation edgeworth expansion saddlepoint expansion Laplace approximations stochastic asymptotic expansion.

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