radial distance, and possibly with survey directions. The survey geometry may have a complex shape. The observed galaxies are inevitably on a light-cone, therefore evolutionary effect comes in when a survey has a certain depth.

Therefore, estimating the power spectrum from a real data needs a method to approximately erase those sources of inhomogeneity. Among others, @fel94 offers a standard method to estimate the power spectrum. The estimated power spectrum by such standard method is a convolution of the true power with the window function of the survey. This becomes a problem when the survey has a complex shape.

In a seminal paper, @vog96 introduced a novel method, i.e., the method of Karhunen-Loève (KL) eigenmodes in the context of galaxy redshift surveys. This method can handle complex shape of the survey geometry as well as complex selection functions. In the context of CMB analysis, @bon95 introduced an equivalent method which is called signal-to-noise eigenmodes method. The KL method is now recongnized as one of the most promising methods to tackle large data sets of the cosmological surveys [@teg97; @teg98; @tay01], and has been successfully applied to the Las Campanas Redshift Survey (LCRS) [@mat00b], the Sloan Digital Sky Survey (SDSS) [@sza03; 