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Estimation of a Neyman-Scott process from line transect data by a one-dimensional K-function

We consider the problem of estimating the parameters of a two-dimensional Ney-man-Scott process, from data collected through a line transect survey. Cowling (1998) suggested an estimation method based on a one-dimensional K-function along the transect line. However, her expression for the theoretical K-function was wrong. We develop the correct K-function. We further carry out a simulation study, where we show that the one-dimensional K-function method outperforms a method previously suggested by Hagen and Schweder (1995).