Author | McLeish, D. L. author |
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Title | The Theory and Applications of Statistical Inference Functions [electronic resource] / by D. L. McLeish, Christopher G. Small |
Imprint | New York, NY : Springer New York, 1988 |
Connect to | http://dx.doi.org/10.1007/978-1-4612-3872-0 |
Descript | VI, 124 p. online resource |
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