LARGE SAMPLE INFERENCE FOR IRREGULARLY SPACED DEPENDENT OBSERVATIONS BASEDON SUBSAMPLING

In many contexts, e.g., queueing theory, spatial statistics, etc., the data may consist of measurements of some quantity at irregularly scattered points in time and/or space; in other words, the data might correspond to a realization of a marked point process over a compact subset of the space of points. In this paper, we formulate a modified version of the general subsampling methodology which was originally put forth in Politis and Romano (1994) for data observed over points on a lattice, and show that it leads to valid large sample inferences in a very general estimation set-up involving data from a marked point process.

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