Table of Contents

1: Introduction.- 2: The Space of Inference Functions: Ancillarity, Sufficiency and Projection.- 2.1 Basic definitions.- 2.2 Projections and product sets.- 2.3 Ancillarity, sufficiency and projection for the one-parameter model.- 2.4 Local concepts of ancillarity and sufficiency.- 2.5 Second order ancillarity and sufficiency.- 2.6 Parametrization invariance of local constructions.- 2.7 Background development.- 3: Selecting an Inference Function for 1-Parameter Models.- 3.1 Linearization of inference functions.- 3.2 Adjustments to reduce curvature.- 3.3 Reducing the number of roots.- 3.4 Median adjustment.- 3.5 Approximate normal inference functions.- 3.6 Background development.- 4: Nuisance Parameters.- 4.1 Eliminating nuisance parameters by invariance.- 4.2 Eliminating nuisance parameters by conditioning.- 4.3 Inference for models involving obstructing nuisance parameters.- 4.4 Background details.- 5: Inference under Restrictions.- 5.1 Linear models.- 5.2 Censoring, grouping and truncation.- 5.3 Errors in observations.- 5.4 Backgound details.- 6: Inference for Stochastic Processes.- 6.1 Linear inference functions.- 6.2 Joint estimation in multiparameter models.- 6.3 Martingale inference functions.- 6.4 Applications in spatial statistics.- 6.5 Background details.- References.

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